python_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=pd.Series([2,4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    4\n",
      "2    5\n",
      "3    6\n",
      "4    7\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "subject={'java':[30,40,50],'dbms':[67,78,89]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(subject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   java  dbms\n",
      "0    30    67\n",
      "1    40    78\n",
      "2    50    89\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.DataFrame(subject,index=['raj','sumit','kapil'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       java  dbms\n",
      "raj      30    67\n",
      "sumit    40    78\n",
      "kapil    50    89\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "java    30\n",
       "dbms    67\n",
       "Name: raj, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.loc['raj']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 4, 5, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "print(ds.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    4\n",
       "2    5\n",
       "3    6\n",
       "4    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([2,4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "subject={'java':[30,40,50],'dbms':[67,78,89]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>java</th>\n",
       "      <th>dbms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   java  dbms\n",
       "0    30    67\n",
       "1    40    78\n",
       "2    50    89"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(subject)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add, subtract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds1=pd.Series([2,4,5,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds2=pd.Series([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3\n",
       "1     6\n",
       "2     8\n",
       "3    10\n",
       "4    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1+ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     3\n",
      "1     6\n",
      "2     8\n",
      "3    10\n",
      "4    12\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ds1+ds2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1-ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2\n",
       "1     8\n",
       "2    15\n",
       "3    24\n",
       "4    35\n",
       "dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1*ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2.000000\n",
       "1    2.000000\n",
       "2    1.666667\n",
       "3    1.500000\n",
       "4    1.400000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1/ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "3    True\n",
       "4    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1>ds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds1<ds2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to comvert an array into pandas series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1=[3,5,7,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 5, 7, 9]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(d1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=np.array([3,5,7,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 7, 9])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3\n",
       "1    5\n",
       "2    7\n",
       "3    9\n",
       "dtype: int32"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#error data must be one dimensional if array is d1 instead of ([3,5,7,9]) in cell 35"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# short series in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.Series(['100','50','300.5','python'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.Series(s).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       100\n",
       "2     300.5\n",
       "1        50\n",
       "3    python\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.Series([100,50,30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1     50\n",
       "2     30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1     50\n",
       "2     30\n",
       "0     90\n",
       "1     10\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.append(pd.Series([90,10]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# order of index in pandas series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.Series(data=[1,2,3,4],index=['A','B','C','D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1\n",
       "B    2\n",
       "C    3\n",
       "D    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reindexing is aslo possible in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    2\n",
       "A    1\n",
       "C    3\n",
       "D    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.reindex(index=['B','A','C','D'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to display columnwise data/ row wise data of our intrest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam_info={'name':['ajay','deep','kapil','sumit'],'score':[80.5,90,60.5,np.nan],'attempt':[1,3,2,3],'qualify':['yes','no','yes','no']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(exam_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   name     4 non-null      object \n",
      " 1   score    3 non-null      float64\n",
      " 2   attempt  4 non-null      int64  \n",
      " 3   qualify  4 non-null      object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# iloc : location base indexing row and column with index value,\n",
    "#specific row from data frame using iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       ajay\n",
       "score      80.5\n",
       "attempt       1\n",
       "qualify     yes\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       deep\n",
       "score        90\n",
       "attempt       3\n",
       "qualify      no\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       sumit\n",
       "score        NaN\n",
       "attempt        3\n",
       "qualify       no\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "exam_info={'name':['ajay','deep','kapil','sumit'],'score':[80.5,90,60.5,np.nan],'attempt':[1,3,2,3],'qualify':['yes','no','yes','no']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(exam_info) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "  df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   name     4 non-null      object \n",
      " 1   score    3 non-null      float64\n",
      " 2   attempt  4 non-null      int64  \n",
      " 3   qualify  4 non-null      object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 256.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       ajay\n",
       "score      80.5\n",
       "attempt       1\n",
       "qualify     yes\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       deep\n",
       "score        90\n",
       "attempt       3\n",
       "qualify      no\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name       sumit\n",
       "score        NaN\n",
       "attempt        3\n",
       "qualify       no\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [name, score, attempt, qualify]\n",
       "Index: []"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  score  attempt qualify\n",
       "0  ajay   80.5        1     yes\n",
       "1  deep   90.0        3      no"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name  score\n",
      "0   ajay   80.5\n",
      "1   deep   90.0\n",
      "2  kapil   60.5\n",
      "3  sumit    NaN\n"
     ]
    }
   ],
   "source": [
    "print(df[['name','score']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': ['ajay', 'deep', 'kapil', 'sumit'],\n",
       " 'score': [80.5, 90, 60.5, nan],\n",
       " 'attempt': [1, 3, 2, 3],\n",
       " 'qualify': ['yes', 'no', 'yes', 'no']}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exam_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# select name and attempt from DF fro 1 & 3 row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>attempt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  attempt\n",
       "1   deep        3\n",
       "3  sumit        3"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,3],[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['score'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to select rows between specific range i.e to sellect rows where score is in between 60 & 80.5(inclusive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "      <th></th>\n",
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       "      <th>score</th>\n",
       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "2  kapil   60.5        2     yes"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['score'].between(60,80.5)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find the row where attempt is less than 3 and score are greater than 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>attempt</th>\n",
       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "2  kapil   60.5        2     yes"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['attempt']<3) & (df['score']>60)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
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       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to change NAN to 70."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[3,'score']=70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>3</td>\n",
       "      <td>no</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit   70.0        3      no"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sum of total attempt made by stuudents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['attempt'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[4]=['raj',59,2,'yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>yes</td>\n",
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      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit   70.0        3      no\n",
       "4    raj   59.0        2     yes"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>raj</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "4    raj   59.0        2     yes"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# in above data frame replaaaace yes with true and no with false for qualify column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     true\n",
       "1    false\n",
       "2     true\n",
       "3    false\n",
       "4     true\n",
       "Name: qualify, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['qualify'].map({'yes':'true','no':'false'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['qualify']=df['qualify'].map({'yes':'true','no':'false'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>2</th>\n",
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       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>raj</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1    true\n",
       "1   deep   90.0        3   false\n",
       "2  kapil   60.5        2    true\n",
       "3  sumit   70.0        3   false\n",
       "4    raj   59.0        2    true"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to change name deep with deepak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>raj</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1    true\n",
       "1   deep   90.0        3   false\n",
       "2  kapil   60.5        2    true\n",
       "3  sumit   70.0        3   false\n",
       "4    raj   59.0        2    true"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      ajay\n",
       "1    deepak\n",
       "2     kapil\n",
       "3     sumit\n",
       "4       raj\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name'].replace('deep','deepak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['name']=df['name'].replace('deep','deepak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>qualify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>true</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deepak</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3</td>\n",
       "      <td>false</td>\n",
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       "      <th>4</th>\n",
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       "      <td>59.0</td>\n",
       "      <td>2</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  score  attempt qualify\n",
       "0    ajay   80.5        1    true\n",
       "1  deepak   90.0        3   false\n",
       "2   kapil   60.5        2    true\n",
       "3   sumit   70.0        3   false\n",
       "4     raj   59.0        2    true"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      ajay\n",
       "1    deepak\n",
       "2     kapil\n",
       "3     sumit\n",
       "4       raj\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    3\n",
       "2    2\n",
       "3    3\n",
       "4    2\n",
       "Name: attempt, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pop('attempt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>deepak</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>70.0</td>\n",
       "      <td>false</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>raj</td>\n",
       "      <td>59.0</td>\n",
       "      <td>true</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  score qualify\n",
       "0    ajay   80.5    true\n",
       "1  deepak   90.0   false\n",
       "2   kapil   60.5    true\n",
       "3   sumit   70.0   false\n",
       "4     raj   59.0    true"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# want to add new column named as colour with values like red,green,blue & yellow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(exam_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify\n",
       "0   ajay   80.5        1     yes\n",
       "1   deep   90.0        3      no\n",
       "2  kapil   60.5        2     yes\n",
       "3  sumit    NaN        3      no"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "color = ['red','yellow','blue','green']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['color'] = color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>qualify</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ajay</td>\n",
       "      <td>80.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>deep</td>\n",
       "      <td>90.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>yellow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kapil</td>\n",
       "      <td>60.5</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sumit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>green</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  score  attempt qualify   color\n",
       "0   ajay   80.5        1     yes     red\n",
       "1   deep   90.0        3      no  yellow\n",
       "2  kapil   60.5        2     yes    blue\n",
       "3  sumit    NaN        3      no   green"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['name', 'score', 'attempt', 'qualify', 'color']"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas filtering of data or data analysis based upon csv filea(world_alcohol.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to diplay dimension or shape of csv file & extract some columns also."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "w=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/world_alcohol.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Niger</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Wine</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1984</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1985</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Democratic People's Republic of Korea</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year       WHO region                                Country  \\\n",
       "0   1986  Western Pacific                               Viet Nam   \n",
       "1   1986         Americas                                Uruguay   \n",
       "2   1985           Africa                           Cte d'Ivoire   \n",
       "3   1986         Americas                               Colombia   \n",
       "4   1987         Americas                  Saint Kitts and Nevis   \n",
       "..   ...              ...                                    ...   \n",
       "95  1984           Africa                                  Niger   \n",
       "96  1985           Europe                             Luxembourg   \n",
       "97  1984  South-East Asia                              Indonesia   \n",
       "98  1984           Africa                      Equatorial Guinea   \n",
       "99  1985  South-East Asia  Democratic People's Republic of Korea   \n",
       "\n",
       "   Beverage Types  Display Value  \n",
       "0            Wine           0.00  \n",
       "1           Other           0.50  \n",
       "2            Wine           1.62  \n",
       "3            Beer           4.27  \n",
       "4            Beer           1.98  \n",
       "..            ...            ...  \n",
       "95          Other           0.00  \n",
       "96           Wine           7.38  \n",
       "97           Wine           0.00  \n",
       "98           Wine           0.00  \n",
       "99           Wine           0.00  \n",
       "\n",
       "[100 rows x 5 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       WHO region                Country Beverage Types  Display Value\n",
       "0  1986  Western Pacific               Viet Nam           Wine           0.00\n",
       "1  1986         Americas                Uruguay          Other           0.50\n",
       "2  1985           Africa           Cte d'Ivoire           Wine           1.62\n",
       "3  1986         Americas               Colombia           Beer           4.27\n",
       "4  1987         Americas  Saint Kitts and Nevis           Beer           1.98"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
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       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
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       "      <td>Colombia</td>\n",
       "      <td>Beer</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>Other</td>\n",
       "      <td>6.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       WHO region                Country Beverage Types  Display Value\n",
       "0  1986  Western Pacific               Viet Nam           Wine           0.00\n",
       "1  1986         Americas                Uruguay          Other           0.50\n",
       "2  1985           Africa           Cte d'Ivoire           Wine           1.62\n",
       "3  1986         Americas               Colombia           Beer           4.27\n",
       "4  1987         Americas  Saint Kitts and Nevis           Beer           1.98\n",
       "5  1987         Americas              Guatemala          Other           0.00\n",
       "6  1987           Africa              Mauritius           Wine           0.13\n",
       "7  1985           Africa                 Angola        Spirits           0.39\n",
       "8  1986         Americas    Antigua and Barbuda        Spirits           1.55\n",
       "9  1984           Africa                Nigeria          Other           6.10"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of row 100\n"
     ]
    }
   ],
   "source": [
    "print('total number of row',w.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'WHO region', 'Country', 'Beverage Types', 'Display Value'], dtype='object')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
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       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
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       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
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       "    </tr>\n",
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       "      <td>1986</td>\n",
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       "      <td>Other</td>\n",
       "      <td>0.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       WHO region   Country Beverage Types  Display Value\n",
       "0  1986  Western Pacific  Viet Nam           Wine            0.0\n",
       "1  1986         Americas   Uruguay          Other            0.5"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.iloc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
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       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1984</td>\n",
       "      <td>South-East Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1985</td>\n",
       "      <td>South-East Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year       WHO region\n",
       "0   1986  Western Pacific\n",
       "1   1986         Americas\n",
       "2   1985           Africa\n",
       "3   1986         Americas\n",
       "4   1987         Americas\n",
       "..   ...              ...\n",
       "95  1984           Africa\n",
       "96  1985           Europe\n",
       "97  1984  South-East Asia\n",
       "98  1984           Africa\n",
       "99  1985  South-East Asia\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.iloc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>4</th>\n",
       "      <td>1987</td>\n",
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       "      <th>5</th>\n",
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       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year       WHO region\n",
       "0  1986  Western Pacific\n",
       "1  1986         Americas\n",
       "2  1985           Africa\n",
       "3  1986         Americas\n",
       "4  1987         Americas\n",
       "5  1987         Americas\n",
       "6  1987           Africa\n",
       "7  1985           Africa\n",
       "8  1986         Americas\n",
       "9  1984           Africa"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.iloc[:,:2].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>Year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <td>1.62</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.27</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.98</td>\n",
       "      <td>1987</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>7.38</td>\n",
       "      <td>1985</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1984</td>\n",
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       "      <th>98</th>\n",
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       "      <th>99</th>\n",
       "      <td>0.00</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Display Value  Year\n",
       "0            0.00  1986\n",
       "1            0.50  1986\n",
       "2            1.62  1985\n",
       "3            4.27  1986\n",
       "4            1.98  1987\n",
       "..            ...   ...\n",
       "95           0.00  1984\n",
       "96           7.38  1985\n",
       "97           0.00  1984\n",
       "98           0.00  1984\n",
       "99           0.00  1985\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[['Display Value','Year']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Bolivia (Plurinational State of)</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1986</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1984</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Niger</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year WHO region                           Country Beverage Types  \\\n",
       "4   1987   Americas             Saint Kitts and Nevis           Beer   \n",
       "74  1986   Americas  Bolivia (Plurinational State of)        Spirits   \n",
       "70  1986     Africa                        Madagascar        Spirits   \n",
       "23  1984     Europe                            Turkey           Beer   \n",
       "95  1984     Africa                             Niger          Other   \n",
       "\n",
       "    Display Value  \n",
       "4            1.98  \n",
       "74           2.06  \n",
       "70           1.02  \n",
       "23           0.44  \n",
       "95           0.00  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Niger</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>Wine</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1984</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1984</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1985</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Democratic People's Republic of Korea</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year       WHO region                                Country  \\\n",
       "0   1986  Western Pacific                               Viet Nam   \n",
       "1   1986         Americas                                Uruguay   \n",
       "2   1985           Africa                           Cte d'Ivoire   \n",
       "3   1986         Americas                               Colombia   \n",
       "4   1987         Americas                  Saint Kitts and Nevis   \n",
       "..   ...              ...                                    ...   \n",
       "95  1984           Africa                                  Niger   \n",
       "96  1985           Europe                             Luxembourg   \n",
       "97  1984  South-East Asia                              Indonesia   \n",
       "98  1984           Africa                      Equatorial Guinea   \n",
       "99  1985  South-East Asia  Democratic People's Republic of Korea   \n",
       "\n",
       "   Beverage Types  Display Value  \n",
       "0            Wine           0.00  \n",
       "1           Other           0.50  \n",
       "2            Wine           1.62  \n",
       "3            Beer           4.27  \n",
       "4            Beer           1.98  \n",
       "..            ...            ...  \n",
       "95          Other           0.00  \n",
       "96           Wine           7.38  \n",
       "97           Wine           0.00  \n",
       "98           Wine           0.00  \n",
       "99           Wine           0.00  \n",
       "\n",
       "[95 rows x 5 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to remove duplicate values from WHO region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1984</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Norway</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1986</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             WHO region       Country Beverage Types  Display Value\n",
       "0   1986        Western Pacific      Viet Nam           Wine           0.00\n",
       "1   1986               Americas       Uruguay          Other           0.50\n",
       "2   1985                 Africa  Cte d'Ivoire           Wine           1.62\n",
       "13  1984  Eastern Mediterranean   Afghanistan          Other           0.00\n",
       "18  1984                 Europe        Norway        Spirits           1.62\n",
       "20  1986        South-East Asia       Myanmar           Wine           0.00"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.drop_duplicates('WHO region')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find how much alcohol has consumed within a specific year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1985</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Lao People's Democratic Republic</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1985</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1985</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year       WHO region                                            Country  \\\n",
       "2   1985           Africa                                       Cte d'Ivoire   \n",
       "7   1985           Africa                                             Angola   \n",
       "12  1985  Western Pacific                   Lao People's Democratic Republic   \n",
       "14  1985  Western Pacific                                           Viet Nam   \n",
       "24  1985           Africa                                            Comoros   \n",
       "26  1985           Europe  United Kingdom of Great Britain and Northern I...   \n",
       "33  1985           Africa                                         Mauritania   \n",
       "35  1985         Americas                              Saint Kitts and Nevis   \n",
       "44  1985           Europe                                          Lithuania   \n",
       "50  1985           Europe                                        Switzerland   \n",
       "\n",
       "   Beverage Types  Display Value  \n",
       "2            Wine           1.62  \n",
       "7         Spirits           0.39  \n",
       "12           Beer           0.00  \n",
       "14        Spirits           0.05  \n",
       "24          Other            NaN  \n",
       "26           Wine           1.36  \n",
       "33          Other           0.00  \n",
       "35        Spirits           2.24  \n",
       "44          Other            NaN  \n",
       "50          Other           0.30  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[w['Year']==1985].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#alcohol consumption details in Year 1987 or 1989."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>Beer</td>\n",
       "      <td>2.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1987</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Burkina Faso</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1987</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>1987</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Beer</td>\n",
       "      <td>3.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1987</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>1989</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Wine</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Bolivia (Plurinational State of)</td>\n",
       "      <td>Beer</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Namibia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Uganda</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1989</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Other</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1987</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1989</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>Beer</td>\n",
       "      <td>4.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             WHO region                           Country  \\\n",
       "4   1987               Americas             Saint Kitts and Nevis   \n",
       "5   1987               Americas                         Guatemala   \n",
       "6   1987                 Africa                         Mauritius   \n",
       "10  1987                 Africa                          Botswana   \n",
       "11  1989               Americas                         Guatemala   \n",
       "15  1987                 Africa                     Guinea-Bissau   \n",
       "17  1989                 Africa                        Seychelles   \n",
       "21  1989               Americas                        Costa Rica   \n",
       "28  1987        Western Pacific                          Viet Nam   \n",
       "32  1989                 Africa                         Mauritius   \n",
       "36  1987  Eastern Mediterranean                             Egypt   \n",
       "38  1987  Eastern Mediterranean                             Qatar   \n",
       "39  1987                 Africa                      Burkina Faso   \n",
       "40  1987                 Europe                           Austria   \n",
       "45  1989                 Africa                          Zimbabwe   \n",
       "46  1987               Americas               Trinidad and Tobago   \n",
       "48  1987               Americas                         Nicaragua   \n",
       "51  1987                 Europe                           Finland   \n",
       "55  1989               Americas                          Suriname   \n",
       "56  1987        Western Pacific                          Viet Nam   \n",
       "57  1989                 Europe                           Croatia   \n",
       "59  1989  Eastern Mediterranean              Syrian Arab Republic   \n",
       "60  1987  Eastern Mediterranean        Iran (Islamic Republic of)   \n",
       "62  1987               Americas                          Suriname   \n",
       "64  1989               Americas  Bolivia (Plurinational State of)   \n",
       "65  1989  Eastern Mediterranean                           Somalia   \n",
       "66  1987  Eastern Mediterranean                              Iraq   \n",
       "67  1989                 Africa                           Namibia   \n",
       "68  1989                 Africa                            Uganda   \n",
       "72  1987                 Africa                        Mauritania   \n",
       "75  1989  Eastern Mediterranean                       Afghanistan   \n",
       "78  1989               Americas                           Jamaica   \n",
       "79  1989                 Europe                           Finland   \n",
       "82  1987                 Europe                           Ireland   \n",
       "87  1989  Eastern Mediterranean                              Iraq   \n",
       "88  1987  Eastern Mediterranean                           Lebanon   \n",
       "90  1989                 Africa                            Malawi   \n",
       "91  1989                 Europe                          Bulgaria   \n",
       "93  1987                 Africa                        Madagascar   \n",
       "\n",
       "   Beverage Types  Display Value  \n",
       "4            Beer           1.98  \n",
       "5           Other           0.00  \n",
       "6            Wine           0.13  \n",
       "10           Wine           0.20  \n",
       "11           Beer           0.62  \n",
       "15           Wine           0.07  \n",
       "17           Beer           2.23  \n",
       "21        Spirits           4.51  \n",
       "28           Beer           0.11  \n",
       "32           Beer           1.60  \n",
       "36           Beer           0.07  \n",
       "38          Other           0.00  \n",
       "39        Spirits           0.01  \n",
       "40        Spirits           1.90  \n",
       "45           Beer           0.19  \n",
       "46        Spirits           2.26  \n",
       "48           Beer           0.70  \n",
       "51           Beer           3.88  \n",
       "55           Wine           0.04  \n",
       "56           Wine           0.00  \n",
       "57           Wine           5.10  \n",
       "59          Other           0.00  \n",
       "60          Other           0.00  \n",
       "62          Other           0.00  \n",
       "64           Beer           1.26  \n",
       "65           Beer           0.00  \n",
       "66           Wine           0.01  \n",
       "67           Beer           0.00  \n",
       "68           Beer           0.12  \n",
       "72          Other           0.00  \n",
       "75          Other           0.00  \n",
       "78          Other           0.00  \n",
       "79          Other           2.09  \n",
       "82        Spirits           2.25  \n",
       "87           Wine           0.01  \n",
       "88           Beer           0.42  \n",
       "90           Wine           0.01  \n",
       "91           Beer           4.43  \n",
       "93          Other            NaN  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[(w['Year']==1987)|(w['Year']==1989)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1986</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Tunisia</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>Beer</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>1986</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1984</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Syrian Arab Republic</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1985</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Libya</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1986</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1986</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1989</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1986</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1984</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1985</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Democratic People's Republic of Korea</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             WHO region                                Country  \\\n",
       "13  1984  Eastern Mediterranean                            Afghanistan   \n",
       "20  1986        South-East Asia                                Myanmar   \n",
       "25  1984  Eastern Mediterranean                                Tunisia   \n",
       "27  1984  Eastern Mediterranean                                Bahrain   \n",
       "36  1987  Eastern Mediterranean                                  Egypt   \n",
       "38  1987  Eastern Mediterranean                                  Qatar   \n",
       "52  1986  Eastern Mediterranean                           Saudi Arabia   \n",
       "53  1984  Eastern Mediterranean                                 Kuwait   \n",
       "58  1984  Eastern Mediterranean                                Somalia   \n",
       "59  1989  Eastern Mediterranean                   Syrian Arab Republic   \n",
       "60  1987  Eastern Mediterranean             Iran (Islamic Republic of)   \n",
       "63  1985  Eastern Mediterranean                                  Libya   \n",
       "65  1989  Eastern Mediterranean                                Somalia   \n",
       "66  1987  Eastern Mediterranean                                   Iraq   \n",
       "73  1986  Eastern Mediterranean                               Pakistan   \n",
       "75  1989  Eastern Mediterranean                            Afghanistan   \n",
       "84  1986        South-East Asia                              Sri Lanka   \n",
       "87  1989  Eastern Mediterranean                                   Iraq   \n",
       "88  1987  Eastern Mediterranean                                Lebanon   \n",
       "89  1986  Eastern Mediterranean                                Lebanon   \n",
       "97  1984        South-East Asia                              Indonesia   \n",
       "99  1985        South-East Asia  Democratic People's Republic of Korea   \n",
       "\n",
       "   Beverage Types  Display Value  \n",
       "13          Other           0.00  \n",
       "20           Wine           0.00  \n",
       "25          Other           0.00  \n",
       "27           Beer           2.22  \n",
       "36           Beer           0.07  \n",
       "38          Other           0.00  \n",
       "52           Wine           0.00  \n",
       "53           Beer           0.00  \n",
       "58        Spirits           0.00  \n",
       "59          Other           0.00  \n",
       "60          Other           0.00  \n",
       "63          Other           0.00  \n",
       "65           Beer           0.00  \n",
       "66           Wine           0.01  \n",
       "73          Other           0.01  \n",
       "75          Other           0.00  \n",
       "84          Other           0.00  \n",
       "87           Wine           0.01  \n",
       "88           Beer           0.42  \n",
       "89           Wine           0.70  \n",
       "97           Wine           0.00  \n",
       "99           Wine           0.00  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[w['WHO region'].str.contains('Ea')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1986</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>Myanmar</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1986</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>Other</td>\n",
       "      <td>4.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1987</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Iran (Islamic Republic of)</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1986</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Madagascar</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1985</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1989</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             WHO region                     Country Beverage Types  \\\n",
       "0   1986        Western Pacific                    Viet Nam           Wine   \n",
       "10  1987                 Africa                    Botswana           Wine   \n",
       "20  1986        South-East Asia                     Myanmar           Wine   \n",
       "30  1986                 Africa                Sierra Leone          Other   \n",
       "40  1987                 Europe                     Austria        Spirits   \n",
       "50  1985                 Europe                 Switzerland          Other   \n",
       "60  1987  Eastern Mediterranean  Iran (Islamic Republic of)          Other   \n",
       "70  1986                 Africa                  Madagascar        Spirits   \n",
       "80  1985                 Africa                      Malawi          Other   \n",
       "90  1989                 Africa                      Malawi           Wine   \n",
       "\n",
       "    Display Value  \n",
       "0            0.00  \n",
       "10           0.20  \n",
       "20           0.00  \n",
       "30           4.48  \n",
       "40           1.90  \n",
       "50           0.30  \n",
       "60           0.00  \n",
       "70           1.02  \n",
       "80           0.84  \n",
       "90           0.01  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.filter(regex='0',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Country</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1986</td>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1985</td>\n",
       "      <td>Cte d'Ivoire</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1986</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1987</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1984</td>\n",
       "      <td>Niger</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1985</td>\n",
       "      <td>Luxembourg</td>\n",
       "      <td>7.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1984</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1984</td>\n",
       "      <td>Equatorial Guinea</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1985</td>\n",
       "      <td>Democratic People's Republic of Korea</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year                                Country  Display Value\n",
       "0   1986                               Viet Nam           0.00\n",
       "1   1986                                Uruguay           0.50\n",
       "2   1985                           Cte d'Ivoire           1.62\n",
       "3   1986                               Colombia           4.27\n",
       "4   1987                  Saint Kitts and Nevis           1.98\n",
       "..   ...                                    ...            ...\n",
       "95  1984                                  Niger           0.00\n",
       "96  1985                             Luxembourg           7.38\n",
       "97  1984                              Indonesia           0.00\n",
       "98  1984                      Equatorial Guinea           0.00\n",
       "99  1985  Democratic People's Republic of Korea           0.00\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.iloc[:,0::2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# find all records starting from 2 nd row, access every 5th row from cvv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>WHO region</th>\n",
       "      <th>Country</th>\n",
       "      <th>Beverage Types</th>\n",
       "      <th>Display Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Other</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1987</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Guatemala</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1984</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1989</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1985</td>\n",
       "      <td>Europe</td>\n",
       "      <td>United Kingdom of Great Britain and Northern I...</td>\n",
       "      <td>Wine</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1986</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>Micronesia (Federated States of)</td>\n",
       "      <td>Wine</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>1987</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>Beer</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1986</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Beer</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>1987</td>\n",
       "      <td>Americas</td>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>Spirits</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year             WHO region  \\\n",
       "1   1986               Americas   \n",
       "6   1987                 Africa   \n",
       "11  1989               Americas   \n",
       "16  1984               Americas   \n",
       "21  1989               Americas   \n",
       "26  1985                 Europe   \n",
       "31  1986        Western Pacific   \n",
       "36  1987  Eastern Mediterranean   \n",
       "41  1986                 Europe   \n",
       "46  1987               Americas   \n",
       "\n",
       "                                              Country Beverage Types  \\\n",
       "1                                             Uruguay          Other   \n",
       "6                                           Mauritius           Wine   \n",
       "11                                          Guatemala           Beer   \n",
       "16                                         Costa Rica           Wine   \n",
       "21                                         Costa Rica        Spirits   \n",
       "26  United Kingdom of Great Britain and Northern I...           Wine   \n",
       "31                   Micronesia (Federated States of)           Wine   \n",
       "36                                              Egypt           Beer   \n",
       "41                                     Czech Republic           Beer   \n",
       "46                                Trinidad and Tobago        Spirits   \n",
       "\n",
       "    Display Value  \n",
       "1            0.50  \n",
       "6            0.13  \n",
       "11           0.62  \n",
       "16           0.06  \n",
       "21           4.51  \n",
       "26           1.36  \n",
       "31           0.00  \n",
       "36           0.07  \n",
       "41           6.82  \n",
       "46           2.26  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w.iloc[1::5].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import excel file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.read_excel('Downloads/coalpublic2013.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>69339</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>327780</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>175058</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>154861</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>103332</td>\n",
       "      <td>Powhatan Mine</td>\n",
       "      <td>140521</td>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>103375</td>\n",
       "      <td>Johnson Mine</td>\n",
       "      <td>580</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>125824</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>103432</td>\n",
       "      <td>Skelton Creek</td>\n",
       "      <td>8252</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>103437</td>\n",
       "      <td>Black Warrior Mine No 1</td>\n",
       "      <td>145924</td>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "0   2013   103381            Tacoa Highwall Miner       56004        22392\n",
       "1   2013   103404                Reid School Mine       28807        28447\n",
       "2   2013   100759  North River #1 Underground Min     1440115       474784\n",
       "3   2013   103246                      Bear Creek       87587        29193\n",
       "4   2013   103451                     Knight Mine      147499        46393\n",
       "5   2013   103433              Crane Central Mine       69339        47195\n",
       "6   2013   100329                    Concord Mine           0       144002\n",
       "7   2013   100851                  Oak Grove Mine     2269014      1001809\n",
       "8   2013   102901                Shoal Creek Mine           0        12396\n",
       "9   2013   102901                Shoal Creek Mine     1453024      1237415\n",
       "10  2013   103180             Sloan Mountain Mine      327780       196963\n",
       "11  2013   103182                        Fishtrap      175058        87314\n",
       "12  2013   103285                     Narley Mine      154861        90584\n",
       "13  2013   103332                   Powhatan Mine      140521        61394\n",
       "14  2013   103375                    Johnson Mine         580         1900\n",
       "15  2013   103419               Maxine-Pratt Mine      125824       107469\n",
       "16  2013   103432                   Skelton Creek        8252          220\n",
       "17  2013   103437         Black Warrior Mine No 1      145924        70926\n",
       "18  2013   102976   Piney Woods Preparation Plant           0        14828\n",
       "19  2013   102976   Piney Woods Preparation Plant           0        23193\n",
       "20  2013   103380                          Calera           0        12621\n",
       "21  2013   103380                          Calera           0         1402\n",
       "22  2013   103422                 Clark No 1 Mine      122727       140250\n",
       "23  2013   103467             Helena Surface Mine       59664        30539\n",
       "24  2013   101247                       No 4 Mine     2622528      1551141\n",
       "25  2013   101401                       No 7 Mine     5405412      2464719\n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6      258078       119542\n",
       "27  2013   103179             Fleetwood Mine No 1       75937        63745\n",
       "28  2013   103303                    Shannon Mine      317491       164388\n",
       "29  2013   103323                   Deerlick Mine      133452        46381\n",
       "30  2013   103364           Brc Alabama No. 7 Llc           0        14324\n",
       "31  2013   103436                Swann's Crossing      137511        77190\n",
       "32  2013   100347                    Choctaw Mine      537429       215295\n",
       "33  2013   101362                 Manchester Mine      219457       116914\n",
       "34  2013   102996                  Jap Creek Mine      375715       164093\n",
       "35  2013   103155              Corinth Prep Plant           0        27996\n",
       "36  2013   103155              Corinth Prep Plant           0        51994\n",
       "37  2013   103195     Mccollum/Sparks Branch Mine       71910        17411\n",
       "38  2013   103342             Reese's Branch Mine      263888       115123\n",
       "39  2013   103370             Cresent Valley Mine        2860          621\n",
       "40  2013   103372                 Cane Creek Mine       66258        32401\n",
       "41  2013   103376                      Town Creek      299167       176499\n",
       "42  2013   103389                Carbon Hill Mine       76241        84966\n",
       "43  2013   103410                Coal Valley Mine      407841       158591\n",
       "44  2013   103423                Dutton Hill Mine       37275         9162\n",
       "45  2013  1519322                         Ghm #25       25054         3108\n",
       "46  2013   103321                  Poplar Springs      189370        76366\n",
       "47  2013   103358                       Old Union      284563       161805\n",
       "48  2013  5000030                        Usibelli     1631584       286079\n",
       "49  2013   201195                    Kayenta Mine     7602722      1015333"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
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  {
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   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "0  2013   103381            Tacoa Highwall Miner       56004        22392\n",
       "1  2013   103404                Reid School Mine       28807        28447\n",
       "2  2013   100759  North River #1 Underground Min     1440115       474784\n",
       "3  2013   103246                      Bear Creek       87587        29193\n",
       "4  2013   103451                     Knight Mine      147499        46393"
      ]
     },
     "execution_count": 124,
     "metadata": {},
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    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2013\n",
       "1     2013\n",
       "2     2013\n",
       "3     2013\n",
       "4     2013\n",
       "5     2013\n",
       "6     2013\n",
       "7     2013\n",
       "8     2013\n",
       "9     2013\n",
       "10    2013\n",
       "11    2013\n",
       "12    2013\n",
       "13    2013\n",
       "14    2013\n",
       "15    2013\n",
       "16    2013\n",
       "17    2013\n",
       "18    2013\n",
       "19    2013\n",
       "20    2013\n",
       "21    2013\n",
       "22    2013\n",
       "23    2013\n",
       "24    2013\n",
       "25    2013\n",
       "26    2013\n",
       "27    2013\n",
       "28    2013\n",
       "29    2013\n",
       "30    2013\n",
       "31    2013\n",
       "32    2013\n",
       "33    2013\n",
       "34    2013\n",
       "35    2013\n",
       "36    2013\n",
       "37    2013\n",
       "38    2013\n",
       "39    2013\n",
       "40    2013\n",
       "41    2013\n",
       "42    2013\n",
       "43    2013\n",
       "44    2013\n",
       "45    2013\n",
       "46    2013\n",
       "47    2013\n",
       "48    2013\n",
       "49    2013\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Year']\n",
    "#read specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=[1,2,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=pd.read_excel('Downloads/coalpublic2013.xls', usecols=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>103332</td>\n",
       "      <td>Powhatan Mine</td>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>103375</td>\n",
       "      <td>Johnson Mine</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>103432</td>\n",
       "      <td>Skelton Creek</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>103437</td>\n",
       "      <td>Black Warrior Mine No 1</td>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSHA ID                       Mine Name  Labor Hours\n",
       "0    103381            Tacoa Highwall Miner        22392\n",
       "1    103404                Reid School Mine        28447\n",
       "2    100759  North River #1 Underground Min       474784\n",
       "3    103246                      Bear Creek        29193\n",
       "4    103451                     Knight Mine        46393\n",
       "5    103433              Crane Central Mine        47195\n",
       "6    100329                    Concord Mine       144002\n",
       "7    100851                  Oak Grove Mine      1001809\n",
       "8    102901                Shoal Creek Mine        12396\n",
       "9    102901                Shoal Creek Mine      1237415\n",
       "10   103180             Sloan Mountain Mine       196963\n",
       "11   103182                        Fishtrap        87314\n",
       "12   103285                     Narley Mine        90584\n",
       "13   103332                   Powhatan Mine        61394\n",
       "14   103375                    Johnson Mine         1900\n",
       "15   103419               Maxine-Pratt Mine       107469\n",
       "16   103432                   Skelton Creek          220\n",
       "17   103437         Black Warrior Mine No 1        70926\n",
       "18   102976   Piney Woods Preparation Plant        14828\n",
       "19   102976   Piney Woods Preparation Plant        23193\n",
       "20   103380                          Calera        12621\n",
       "21   103380                          Calera         1402\n",
       "22   103422                 Clark No 1 Mine       140250\n",
       "23   103467             Helena Surface Mine        30539\n",
       "24   101247                       No 4 Mine      1551141\n",
       "25   101401                       No 7 Mine      2464719\n",
       "26   103172  Searles Mine No. 2, 3, 4, 5, 6       119542\n",
       "27   103179             Fleetwood Mine No 1        63745\n",
       "28   103303                    Shannon Mine       164388\n",
       "29   103323                   Deerlick Mine        46381\n",
       "30   103364           Brc Alabama No. 7 Llc        14324\n",
       "31   103436                Swann's Crossing        77190\n",
       "32   100347                    Choctaw Mine       215295\n",
       "33   101362                 Manchester Mine       116914\n",
       "34   102996                  Jap Creek Mine       164093\n",
       "35   103155              Corinth Prep Plant        27996\n",
       "36   103155              Corinth Prep Plant        51994\n",
       "37   103195     Mccollum/Sparks Branch Mine        17411\n",
       "38   103342             Reese's Branch Mine       115123\n",
       "39   103370             Cresent Valley Mine          621\n",
       "40   103372                 Cane Creek Mine        32401\n",
       "41   103376                      Town Creek       176499\n",
       "42   103389                Carbon Hill Mine        84966\n",
       "43   103410                Coal Valley Mine       158591\n",
       "44   103423                Dutton Hill Mine         9162\n",
       "45  1519322                         Ghm #25         3108\n",
       "46   103321                  Poplar Springs        76366\n",
       "47   103358                       Old Union       161805\n",
       "48  5000030                        Usibelli       286079\n",
       "49   201195                    Kayenta Mine      1015333"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27854323"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Production'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "557086.46"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Production'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7602722"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Production'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x['Production'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "x.insert(3,'new coloumn',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>new coloumn</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69339</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327780</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>NaN</td>\n",
       "      <td>175058</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154861</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>103332</td>\n",
       "      <td>Powhatan Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>140521</td>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>103375</td>\n",
       "      <td>Johnson Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>125824</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>103432</td>\n",
       "      <td>Skelton Creek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8252</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>103437</td>\n",
       "      <td>Black Warrior Mine No 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>145924</td>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  new coloumn  Production  \\\n",
       "0   2013   103381            Tacoa Highwall Miner          NaN       56004   \n",
       "1   2013   103404                Reid School Mine          NaN       28807   \n",
       "2   2013   100759  North River #1 Underground Min          NaN     1440115   \n",
       "3   2013   103246                      Bear Creek          NaN       87587   \n",
       "4   2013   103451                     Knight Mine          NaN      147499   \n",
       "5   2013   103433              Crane Central Mine          NaN       69339   \n",
       "6   2013   100329                    Concord Mine          NaN           0   \n",
       "7   2013   100851                  Oak Grove Mine          NaN     2269014   \n",
       "8   2013   102901                Shoal Creek Mine          NaN           0   \n",
       "9   2013   102901                Shoal Creek Mine          NaN     1453024   \n",
       "10  2013   103180             Sloan Mountain Mine          NaN      327780   \n",
       "11  2013   103182                        Fishtrap          NaN      175058   \n",
       "12  2013   103285                     Narley Mine          NaN      154861   \n",
       "13  2013   103332                   Powhatan Mine          NaN      140521   \n",
       "14  2013   103375                    Johnson Mine          NaN         580   \n",
       "15  2013   103419               Maxine-Pratt Mine          NaN      125824   \n",
       "16  2013   103432                   Skelton Creek          NaN        8252   \n",
       "17  2013   103437         Black Warrior Mine No 1          NaN      145924   \n",
       "18  2013   102976   Piney Woods Preparation Plant          NaN           0   \n",
       "19  2013   102976   Piney Woods Preparation Plant          NaN           0   \n",
       "20  2013   103380                          Calera          NaN           0   \n",
       "21  2013   103380                          Calera          NaN           0   \n",
       "22  2013   103422                 Clark No 1 Mine          NaN      122727   \n",
       "23  2013   103467             Helena Surface Mine          NaN       59664   \n",
       "24  2013   101247                       No 4 Mine          NaN     2622528   \n",
       "25  2013   101401                       No 7 Mine          NaN     5405412   \n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6          NaN      258078   \n",
       "27  2013   103179             Fleetwood Mine No 1          NaN       75937   \n",
       "28  2013   103303                    Shannon Mine          NaN      317491   \n",
       "29  2013   103323                   Deerlick Mine          NaN      133452   \n",
       "30  2013   103364           Brc Alabama No. 7 Llc          NaN           0   \n",
       "31  2013   103436                Swann's Crossing          NaN      137511   \n",
       "32  2013   100347                    Choctaw Mine          NaN      537429   \n",
       "33  2013   101362                 Manchester Mine          NaN      219457   \n",
       "34  2013   102996                  Jap Creek Mine          NaN      375715   \n",
       "35  2013   103155              Corinth Prep Plant          NaN           0   \n",
       "36  2013   103155              Corinth Prep Plant          NaN           0   \n",
       "37  2013   103195     Mccollum/Sparks Branch Mine          NaN       71910   \n",
       "38  2013   103342             Reese's Branch Mine          NaN      263888   \n",
       "39  2013   103370             Cresent Valley Mine          NaN        2860   \n",
       "40  2013   103372                 Cane Creek Mine          NaN       66258   \n",
       "41  2013   103376                      Town Creek          NaN      299167   \n",
       "42  2013   103389                Carbon Hill Mine          NaN       76241   \n",
       "43  2013   103410                Coal Valley Mine          NaN      407841   \n",
       "44  2013   103423                Dutton Hill Mine          NaN       37275   \n",
       "45  2013  1519322                         Ghm #25          NaN       25054   \n",
       "46  2013   103321                  Poplar Springs          NaN      189370   \n",
       "47  2013   103358                       Old Union          NaN      284563   \n",
       "48  2013  5000030                        Usibelli          NaN     1631584   \n",
       "49  2013   201195                    Kayenta Mine          NaN     7602722   \n",
       "\n",
       "    Labor Hours  \n",
       "0         22392  \n",
       "1         28447  \n",
       "2        474784  \n",
       "3         29193  \n",
       "4         46393  \n",
       "5         47195  \n",
       "6        144002  \n",
       "7       1001809  \n",
       "8         12396  \n",
       "9       1237415  \n",
       "10       196963  \n",
       "11        87314  \n",
       "12        90584  \n",
       "13        61394  \n",
       "14         1900  \n",
       "15       107469  \n",
       "16          220  \n",
       "17        70926  \n",
       "18        14828  \n",
       "19        23193  \n",
       "20        12621  \n",
       "21         1402  \n",
       "22       140250  \n",
       "23        30539  \n",
       "24      1551141  \n",
       "25      2464719  \n",
       "26       119542  \n",
       "27        63745  \n",
       "28       164388  \n",
       "29        46381  \n",
       "30        14324  \n",
       "31        77190  \n",
       "32       215295  \n",
       "33       116914  \n",
       "34       164093  \n",
       "35        27996  \n",
       "36        51994  \n",
       "37        17411  \n",
       "38       115123  \n",
       "39          621  \n",
       "40        32401  \n",
       "41       176499  \n",
       "42        84966  \n",
       "43       158591  \n",
       "44         9162  \n",
       "45         3108  \n",
       "46        76366  \n",
       "47       161805  \n",
       "48       286079  \n",
       "49      1015333  "
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>new coloumn</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>NaN</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>NaN</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  new coloumn  Production  \\\n",
       "20  2013   103380                          Calera          NaN           0   \n",
       "21  2013   103380                          Calera          NaN           0   \n",
       "22  2013   103422                 Clark No 1 Mine          NaN      122727   \n",
       "23  2013   103467             Helena Surface Mine          NaN       59664   \n",
       "24  2013   101247                       No 4 Mine          NaN     2622528   \n",
       "25  2013   101401                       No 7 Mine          NaN     5405412   \n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6          NaN      258078   \n",
       "27  2013   103179             Fleetwood Mine No 1          NaN       75937   \n",
       "28  2013   103303                    Shannon Mine          NaN      317491   \n",
       "29  2013   103323                   Deerlick Mine          NaN      133452   \n",
       "30  2013   103364           Brc Alabama No. 7 Llc          NaN           0   \n",
       "31  2013   103436                Swann's Crossing          NaN      137511   \n",
       "32  2013   100347                    Choctaw Mine          NaN      537429   \n",
       "33  2013   101362                 Manchester Mine          NaN      219457   \n",
       "34  2013   102996                  Jap Creek Mine          NaN      375715   \n",
       "35  2013   103155              Corinth Prep Plant          NaN           0   \n",
       "36  2013   103155              Corinth Prep Plant          NaN           0   \n",
       "37  2013   103195     Mccollum/Sparks Branch Mine          NaN       71910   \n",
       "38  2013   103342             Reese's Branch Mine          NaN      263888   \n",
       "39  2013   103370             Cresent Valley Mine          NaN        2860   \n",
       "40  2013   103372                 Cane Creek Mine          NaN       66258   \n",
       "41  2013   103376                      Town Creek          NaN      299167   \n",
       "42  2013   103389                Carbon Hill Mine          NaN       76241   \n",
       "43  2013   103410                Coal Valley Mine          NaN      407841   \n",
       "44  2013   103423                Dutton Hill Mine          NaN       37275   \n",
       "45  2013  1519322                         Ghm #25          NaN       25054   \n",
       "46  2013   103321                  Poplar Springs          NaN      189370   \n",
       "47  2013   103358                       Old Union          NaN      284563   \n",
       "48  2013  5000030                        Usibelli          NaN     1631584   \n",
       "49  2013   201195                    Kayenta Mine          NaN     7602722   \n",
       "\n",
       "    Labor Hours  \n",
       "20        12621  \n",
       "21         1402  \n",
       "22       140250  \n",
       "23        30539  \n",
       "24      1551141  \n",
       "25      2464719  \n",
       "26       119542  \n",
       "27        63745  \n",
       "28       164388  \n",
       "29        46381  \n",
       "30        14324  \n",
       "31        77190  \n",
       "32       215295  \n",
       "33       116914  \n",
       "34       164093  \n",
       "35        27996  \n",
       "36        51994  \n",
       "37        17411  \n",
       "38       115123  \n",
       "39          621  \n",
       "40        32401  \n",
       "41       176499  \n",
       "42        84966  \n",
       "43       158591  \n",
       "44         9162  \n",
       "45         3108  \n",
       "46        76366  \n",
       "47       161805  \n",
       "48       286079  \n",
       "49      1015333  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.iloc[20::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>new coloumn</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID             Mine Name  new coloumn  Production  Labor Hours\n",
       "0   2013   103381  Tacoa Highwall Miner          NaN       56004        22392\n",
       "20  2013   103380                Calera          NaN           0        12621\n",
       "40  2013   103372       Cane Creek Mine          NaN       66258        32401"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.iloc[::20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=pd.read_excel('Downloads/coalpublic2013.xls', skiprows=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2013</th>\n",
       "      <th>102976</th>\n",
       "      <th>Piney Woods Preparation Plant</th>\n",
       "      <th>0</th>\n",
       "      <th>23193</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2013   102976   Piney Woods Preparation Plant        0    23193\n",
       "0   2013   103380                          Calera        0    12621\n",
       "1   2013   103380                          Calera        0     1402\n",
       "2   2013   103422                 Clark No 1 Mine   122727   140250\n",
       "3   2013   103467             Helena Surface Mine    59664    30539\n",
       "4   2013   101247                       No 4 Mine  2622528  1551141\n",
       "5   2013   101401                       No 7 Mine  5405412  2464719\n",
       "6   2013   103172  Searles Mine No. 2, 3, 4, 5, 6   258078   119542\n",
       "7   2013   103179             Fleetwood Mine No 1    75937    63745\n",
       "8   2013   103303                    Shannon Mine   317491   164388\n",
       "9   2013   103323                   Deerlick Mine   133452    46381\n",
       "10  2013   103364           Brc Alabama No. 7 Llc        0    14324\n",
       "11  2013   103436                Swann's Crossing   137511    77190\n",
       "12  2013   100347                    Choctaw Mine   537429   215295\n",
       "13  2013   101362                 Manchester Mine   219457   116914\n",
       "14  2013   102996                  Jap Creek Mine   375715   164093\n",
       "15  2013   103155              Corinth Prep Plant        0    27996\n",
       "16  2013   103155              Corinth Prep Plant        0    51994\n",
       "17  2013   103195     Mccollum/Sparks Branch Mine    71910    17411\n",
       "18  2013   103342             Reese's Branch Mine   263888   115123\n",
       "19  2013   103370             Cresent Valley Mine     2860      621\n",
       "20  2013   103372                 Cane Creek Mine    66258    32401\n",
       "21  2013   103376                      Town Creek   299167   176499\n",
       "22  2013   103389                Carbon Hill Mine    76241    84966\n",
       "23  2013   103410                Coal Valley Mine   407841   158591\n",
       "24  2013   103423                Dutton Hill Mine    37275     9162\n",
       "25  2013  1519322                         Ghm #25    25054     3108\n",
       "26  2013   103321                  Poplar Springs   189370    76366\n",
       "27  2013   103358                       Old Union   284563   161805\n",
       "28  2013  5000030                        Usibelli  1631584   286079\n",
       "29  2013   201195                    Kayenta Mine  7602722  1015333"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1=pd.read_excel('Downloads/coalpublic2013.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>69339</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>327780</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>175058</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>154861</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>103332</td>\n",
       "      <td>Powhatan Mine</td>\n",
       "      <td>140521</td>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>103375</td>\n",
       "      <td>Johnson Mine</td>\n",
       "      <td>580</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>125824</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>103432</td>\n",
       "      <td>Skelton Creek</td>\n",
       "      <td>8252</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>103437</td>\n",
       "      <td>Black Warrior Mine No 1</td>\n",
       "      <td>145924</td>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "0   2013   103381            Tacoa Highwall Miner       56004        22392\n",
       "1   2013   103404                Reid School Mine       28807        28447\n",
       "2   2013   100759  North River #1 Underground Min     1440115       474784\n",
       "3   2013   103246                      Bear Creek       87587        29193\n",
       "4   2013   103451                     Knight Mine      147499        46393\n",
       "5   2013   103433              Crane Central Mine       69339        47195\n",
       "6   2013   100329                    Concord Mine           0       144002\n",
       "7   2013   100851                  Oak Grove Mine     2269014      1001809\n",
       "8   2013   102901                Shoal Creek Mine           0        12396\n",
       "9   2013   102901                Shoal Creek Mine     1453024      1237415\n",
       "10  2013   103180             Sloan Mountain Mine      327780       196963\n",
       "11  2013   103182                        Fishtrap      175058        87314\n",
       "12  2013   103285                     Narley Mine      154861        90584\n",
       "13  2013   103332                   Powhatan Mine      140521        61394\n",
       "14  2013   103375                    Johnson Mine         580         1900\n",
       "15  2013   103419               Maxine-Pratt Mine      125824       107469\n",
       "16  2013   103432                   Skelton Creek        8252          220\n",
       "17  2013   103437         Black Warrior Mine No 1      145924        70926\n",
       "18  2013   102976   Piney Woods Preparation Plant           0        14828\n",
       "19  2013   102976   Piney Woods Preparation Plant           0        23193\n",
       "20  2013   103380                          Calera           0        12621\n",
       "21  2013   103380                          Calera           0         1402\n",
       "22  2013   103422                 Clark No 1 Mine      122727       140250\n",
       "23  2013   103467             Helena Surface Mine       59664        30539\n",
       "24  2013   101247                       No 4 Mine     2622528      1551141\n",
       "25  2013   101401                       No 7 Mine     5405412      2464719\n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6      258078       119542\n",
       "27  2013   103179             Fleetwood Mine No 1       75937        63745\n",
       "28  2013   103303                    Shannon Mine      317491       164388\n",
       "29  2013   103323                   Deerlick Mine      133452        46381\n",
       "30  2013   103364           Brc Alabama No. 7 Llc           0        14324\n",
       "31  2013   103436                Swann's Crossing      137511        77190\n",
       "32  2013   100347                    Choctaw Mine      537429       215295\n",
       "33  2013   101362                 Manchester Mine      219457       116914\n",
       "34  2013   102996                  Jap Creek Mine      375715       164093\n",
       "35  2013   103155              Corinth Prep Plant           0        27996\n",
       "36  2013   103155              Corinth Prep Plant           0        51994\n",
       "37  2013   103195     Mccollum/Sparks Branch Mine       71910        17411\n",
       "38  2013   103342             Reese's Branch Mine      263888       115123\n",
       "39  2013   103370             Cresent Valley Mine        2860          621\n",
       "40  2013   103372                 Cane Creek Mine       66258        32401\n",
       "41  2013   103376                      Town Creek      299167       176499\n",
       "42  2013   103389                Carbon Hill Mine       76241        84966\n",
       "43  2013   103410                Coal Valley Mine      407841       158591\n",
       "44  2013   103423                Dutton Hill Mine       37275         9162\n",
       "45  2013  1519322                         Ghm #25       25054         3108\n",
       "46  2013   103321                  Poplar Springs      189370        76366\n",
       "47  2013   103358                       Old Union      284563       161805\n",
       "48  2013  5000030                        Usibelli     1631584       286079\n",
       "49  2013   201195                    Kayenta Mine     7602722      1015333"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Production     27854323\n",
       "Labor Hours    11098816\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[['Production','Labor Hours']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_row = x1[['Production','Labor Hours']].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=sum_row).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27854323</td>\n",
       "      <td>11098816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Production  Labor Hours\n",
       "0    27854323     11098816"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>69339</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "0  2013   103381            Tacoa Highwall Miner       56004        22392\n",
       "1  2013   103404                Reid School Mine       28807        28447\n",
       "2  2013   100759  North River #1 Underground Min     1440115       474784\n",
       "3  2013   103246                      Bear Creek       87587        29193\n",
       "4  2013   103451                     Knight Mine      147499        46393\n",
       "5  2013   103433              Crane Central Mine       69339        47195\n",
       "6  2013   100329                    Concord Mine           0       144002\n",
       "7  2013   100851                  Oak Grove Mine     2269014      1001809\n",
       "8  2013   102901                Shoal Creek Mine           0        12396\n",
       "9  2013   102901                Shoal Creek Mine     1453024      1237415"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID         Mine Name  Production  Labor Hours\n",
       "40  2013   103372   Cane Creek Mine       66258        32401\n",
       "41  2013   103376        Town Creek      299167       176499\n",
       "42  2013   103389  Carbon Hill Mine       76241        84966\n",
       "43  2013   103410  Coal Valley Mine      407841       158591\n",
       "44  2013   103423  Dutton Hill Mine       37275         9162\n",
       "45  2013  1519322           Ghm #25       25054         3108\n",
       "46  2013   103321    Poplar Springs      189370        76366\n",
       "47  2013   103358         Old Union      284563       161805\n",
       "48  2013  5000030          Usibelli     1631584       286079\n",
       "49  2013   201195      Kayenta Mine     7602722      1015333"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To sum subtotal of labour hour against mine name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>103381</td>\n",
       "      <td>Tacoa Highwall Miner</td>\n",
       "      <td>56004</td>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>103404</td>\n",
       "      <td>Reid School Mine</td>\n",
       "      <td>28807</td>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>103246</td>\n",
       "      <td>Bear Creek</td>\n",
       "      <td>87587</td>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>103451</td>\n",
       "      <td>Knight Mine</td>\n",
       "      <td>147499</td>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>103433</td>\n",
       "      <td>Crane Central Mine</td>\n",
       "      <td>69339</td>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>327780</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>175058</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>154861</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013</td>\n",
       "      <td>103332</td>\n",
       "      <td>Powhatan Mine</td>\n",
       "      <td>140521</td>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013</td>\n",
       "      <td>103375</td>\n",
       "      <td>Johnson Mine</td>\n",
       "      <td>580</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>125824</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013</td>\n",
       "      <td>103432</td>\n",
       "      <td>Skelton Creek</td>\n",
       "      <td>8252</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013</td>\n",
       "      <td>103437</td>\n",
       "      <td>Black Warrior Mine No 1</td>\n",
       "      <td>145924</td>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013</td>\n",
       "      <td>103467</td>\n",
       "      <td>Helena Surface Mine</td>\n",
       "      <td>59664</td>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013</td>\n",
       "      <td>103179</td>\n",
       "      <td>Fleetwood Mine No 1</td>\n",
       "      <td>75937</td>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013</td>\n",
       "      <td>103323</td>\n",
       "      <td>Deerlick Mine</td>\n",
       "      <td>133452</td>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013</td>\n",
       "      <td>103364</td>\n",
       "      <td>Brc Alabama No. 7 Llc</td>\n",
       "      <td>0</td>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013</td>\n",
       "      <td>103436</td>\n",
       "      <td>Swann's Crossing</td>\n",
       "      <td>137511</td>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>27996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2013</td>\n",
       "      <td>103155</td>\n",
       "      <td>Corinth Prep Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>51994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2013</td>\n",
       "      <td>103195</td>\n",
       "      <td>Mccollum/Sparks Branch Mine</td>\n",
       "      <td>71910</td>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2013</td>\n",
       "      <td>103370</td>\n",
       "      <td>Cresent Valley Mine</td>\n",
       "      <td>2860</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2013</td>\n",
       "      <td>103372</td>\n",
       "      <td>Cane Creek Mine</td>\n",
       "      <td>66258</td>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2013</td>\n",
       "      <td>103423</td>\n",
       "      <td>Dutton Hill Mine</td>\n",
       "      <td>37275</td>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2013</td>\n",
       "      <td>1519322</td>\n",
       "      <td>Ghm #25</td>\n",
       "      <td>25054</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2013</td>\n",
       "      <td>103321</td>\n",
       "      <td>Poplar Springs</td>\n",
       "      <td>189370</td>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "0   2013   103381            Tacoa Highwall Miner       56004        22392\n",
       "1   2013   103404                Reid School Mine       28807        28447\n",
       "2   2013   100759  North River #1 Underground Min     1440115       474784\n",
       "3   2013   103246                      Bear Creek       87587        29193\n",
       "4   2013   103451                     Knight Mine      147499        46393\n",
       "5   2013   103433              Crane Central Mine       69339        47195\n",
       "6   2013   100329                    Concord Mine           0       144002\n",
       "7   2013   100851                  Oak Grove Mine     2269014      1001809\n",
       "8   2013   102901                Shoal Creek Mine           0        12396\n",
       "9   2013   102901                Shoal Creek Mine     1453024      1237415\n",
       "10  2013   103180             Sloan Mountain Mine      327780       196963\n",
       "11  2013   103182                        Fishtrap      175058        87314\n",
       "12  2013   103285                     Narley Mine      154861        90584\n",
       "13  2013   103332                   Powhatan Mine      140521        61394\n",
       "14  2013   103375                    Johnson Mine         580         1900\n",
       "15  2013   103419               Maxine-Pratt Mine      125824       107469\n",
       "16  2013   103432                   Skelton Creek        8252          220\n",
       "17  2013   103437         Black Warrior Mine No 1      145924        70926\n",
       "18  2013   102976   Piney Woods Preparation Plant           0        14828\n",
       "19  2013   102976   Piney Woods Preparation Plant           0        23193\n",
       "20  2013   103380                          Calera           0        12621\n",
       "21  2013   103380                          Calera           0         1402\n",
       "22  2013   103422                 Clark No 1 Mine      122727       140250\n",
       "23  2013   103467             Helena Surface Mine       59664        30539\n",
       "24  2013   101247                       No 4 Mine     2622528      1551141\n",
       "25  2013   101401                       No 7 Mine     5405412      2464719\n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6      258078       119542\n",
       "27  2013   103179             Fleetwood Mine No 1       75937        63745\n",
       "28  2013   103303                    Shannon Mine      317491       164388\n",
       "29  2013   103323                   Deerlick Mine      133452        46381\n",
       "30  2013   103364           Brc Alabama No. 7 Llc           0        14324\n",
       "31  2013   103436                Swann's Crossing      137511        77190\n",
       "32  2013   100347                    Choctaw Mine      537429       215295\n",
       "33  2013   101362                 Manchester Mine      219457       116914\n",
       "34  2013   102996                  Jap Creek Mine      375715       164093\n",
       "35  2013   103155              Corinth Prep Plant           0        27996\n",
       "36  2013   103155              Corinth Prep Plant           0        51994\n",
       "37  2013   103195     Mccollum/Sparks Branch Mine       71910        17411\n",
       "38  2013   103342             Reese's Branch Mine      263888       115123\n",
       "39  2013   103370             Cresent Valley Mine        2860          621\n",
       "40  2013   103372                 Cane Creek Mine       66258        32401\n",
       "41  2013   103376                      Town Creek      299167       176499\n",
       "42  2013   103389                Carbon Hill Mine       76241        84966\n",
       "43  2013   103410                Coal Valley Mine      407841       158591\n",
       "44  2013   103423                Dutton Hill Mine       37275         9162\n",
       "45  2013  1519322                         Ghm #25       25054         3108\n",
       "46  2013   103321                  Poplar Springs      189370        76366\n",
       "47  2013   103358                       Old Union      284563       161805\n",
       "48  2013  5000030                        Usibelli     1631584       286079\n",
       "49  2013   201195                    Kayenta Mine     7602722      1015333"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x2= x1[['Mine Name','Labor Hours']].groupby('Mine Name').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mine Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bear Creek</th>\n",
       "      <td>29193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black Warrior Mine No 1</th>\n",
       "      <td>70926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brc Alabama No. 7 Llc</th>\n",
       "      <td>14324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Calera</th>\n",
       "      <td>14023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cane Creek Mine</th>\n",
       "      <td>32401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carbon Hill Mine</th>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Choctaw Mine</th>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clark No 1 Mine</th>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coal Valley Mine</th>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Concord Mine</th>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corinth Prep Plant</th>\n",
       "      <td>79990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crane Central Mine</th>\n",
       "      <td>47195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cresent Valley Mine</th>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deerlick Mine</th>\n",
       "      <td>46381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dutton Hill Mine</th>\n",
       "      <td>9162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fishtrap</th>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fleetwood Mine No 1</th>\n",
       "      <td>63745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ghm #25</th>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helena Surface Mine</th>\n",
       "      <td>30539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jap Creek Mine</th>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Johnson Mine</th>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kayenta Mine</th>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Knight Mine</th>\n",
       "      <td>46393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manchester Mine</th>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maxine-Pratt Mine</th>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mccollum/Sparks Branch Mine</th>\n",
       "      <td>17411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Narley Mine</th>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No 4 Mine</th>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No 7 Mine</th>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North River #1 Underground Min</th>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oak Grove Mine</th>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old Union</th>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Piney Woods Preparation Plant</th>\n",
       "      <td>38021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poplar Springs</th>\n",
       "      <td>76366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Powhatan Mine</th>\n",
       "      <td>61394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reese's Branch Mine</th>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reid School Mine</th>\n",
       "      <td>28447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Searles Mine No. 2, 3, 4, 5, 6</th>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shannon Mine</th>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shoal Creek Mine</th>\n",
       "      <td>1249811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Skelton Creek</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sloan Mountain Mine</th>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Swann's Crossing</th>\n",
       "      <td>77190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tacoa Highwall Miner</th>\n",
       "      <td>22392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town Creek</th>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Usibelli</th>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Labor Hours\n",
       "Mine Name                                  \n",
       "Bear Creek                            29193\n",
       "Black Warrior Mine No 1               70926\n",
       "Brc Alabama No. 7 Llc                 14324\n",
       "Calera                                14023\n",
       "Cane Creek Mine                       32401\n",
       "Carbon Hill Mine                      84966\n",
       "Choctaw Mine                         215295\n",
       "Clark No 1 Mine                      140250\n",
       "Coal Valley Mine                     158591\n",
       "Concord Mine                         144002\n",
       "Corinth Prep Plant                    79990\n",
       "Crane Central Mine                    47195\n",
       "Cresent Valley Mine                     621\n",
       "Deerlick Mine                         46381\n",
       "Dutton Hill Mine                       9162\n",
       "Fishtrap                              87314\n",
       "Fleetwood Mine No 1                   63745\n",
       "Ghm #25                                3108\n",
       "Helena Surface Mine                   30539\n",
       "Jap Creek Mine                       164093\n",
       "Johnson Mine                           1900\n",
       "Kayenta Mine                        1015333\n",
       "Knight Mine                           46393\n",
       "Manchester Mine                      116914\n",
       "Maxine-Pratt Mine                    107469\n",
       "Mccollum/Sparks Branch Mine           17411\n",
       "Narley Mine                           90584\n",
       "No 4 Mine                           1551141\n",
       "No 7 Mine                           2464719\n",
       "North River #1 Underground Min       474784\n",
       "Oak Grove Mine                      1001809\n",
       "Old Union                            161805\n",
       "Piney Woods Preparation Plant         38021\n",
       "Poplar Springs                        76366\n",
       "Powhatan Mine                         61394\n",
       "Reese's Branch Mine                  115123\n",
       "Reid School Mine                      28447\n",
       "Searles Mine No. 2, 3, 4, 5, 6       119542\n",
       "Shannon Mine                         164388\n",
       "Shoal Creek Mine                    1249811\n",
       "Skelton Creek                           220\n",
       "Sloan Mountain Mine                  196963\n",
       "Swann's Crossing                      77190\n",
       "Tacoa Highwall Miner                  22392\n",
       "Town Creek                           176499\n",
       "Usibelli                             286079"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find out specific MSHA ID coolpublic2013 dtaset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = x1[x1['MSHA ID']==102901].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  MSHA ID         Mine Name  Production  Labor Hours\n",
       "8  2013   102901  Shoal Creek Mine           0        12396\n",
       "9  2013   102901  Shoal Creek Mine     1453024      1237415"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "x3 = x1[x1['MSHA ID']==102901]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>12396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  MSHA ID         Mine Name  Production  Labor Hours\n",
       "8  2013   102901  Shoal Creek Mine           0        12396\n",
       "9  2013   102901  Shoal Creek Mine     1453024      1237415"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find labor hours greater than 20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "x4 = x1[x1['Labor Hours']>=80000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>100759</td>\n",
       "      <td>North River #1 Underground Min</td>\n",
       "      <td>1440115</td>\n",
       "      <td>474784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013</td>\n",
       "      <td>100329</td>\n",
       "      <td>Concord Mine</td>\n",
       "      <td>0</td>\n",
       "      <td>144002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>100851</td>\n",
       "      <td>Oak Grove Mine</td>\n",
       "      <td>2269014</td>\n",
       "      <td>1001809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013</td>\n",
       "      <td>102901</td>\n",
       "      <td>Shoal Creek Mine</td>\n",
       "      <td>1453024</td>\n",
       "      <td>1237415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>103180</td>\n",
       "      <td>Sloan Mountain Mine</td>\n",
       "      <td>327780</td>\n",
       "      <td>196963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>103182</td>\n",
       "      <td>Fishtrap</td>\n",
       "      <td>175058</td>\n",
       "      <td>87314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013</td>\n",
       "      <td>103285</td>\n",
       "      <td>Narley Mine</td>\n",
       "      <td>154861</td>\n",
       "      <td>90584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013</td>\n",
       "      <td>103419</td>\n",
       "      <td>Maxine-Pratt Mine</td>\n",
       "      <td>125824</td>\n",
       "      <td>107469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013</td>\n",
       "      <td>103422</td>\n",
       "      <td>Clark No 1 Mine</td>\n",
       "      <td>122727</td>\n",
       "      <td>140250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013</td>\n",
       "      <td>101247</td>\n",
       "      <td>No 4 Mine</td>\n",
       "      <td>2622528</td>\n",
       "      <td>1551141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013</td>\n",
       "      <td>101401</td>\n",
       "      <td>No 7 Mine</td>\n",
       "      <td>5405412</td>\n",
       "      <td>2464719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013</td>\n",
       "      <td>103172</td>\n",
       "      <td>Searles Mine No. 2, 3, 4, 5, 6</td>\n",
       "      <td>258078</td>\n",
       "      <td>119542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013</td>\n",
       "      <td>103303</td>\n",
       "      <td>Shannon Mine</td>\n",
       "      <td>317491</td>\n",
       "      <td>164388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013</td>\n",
       "      <td>100347</td>\n",
       "      <td>Choctaw Mine</td>\n",
       "      <td>537429</td>\n",
       "      <td>215295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2013</td>\n",
       "      <td>101362</td>\n",
       "      <td>Manchester Mine</td>\n",
       "      <td>219457</td>\n",
       "      <td>116914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2013</td>\n",
       "      <td>102996</td>\n",
       "      <td>Jap Creek Mine</td>\n",
       "      <td>375715</td>\n",
       "      <td>164093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2013</td>\n",
       "      <td>103342</td>\n",
       "      <td>Reese's Branch Mine</td>\n",
       "      <td>263888</td>\n",
       "      <td>115123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2013</td>\n",
       "      <td>103376</td>\n",
       "      <td>Town Creek</td>\n",
       "      <td>299167</td>\n",
       "      <td>176499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2013</td>\n",
       "      <td>103389</td>\n",
       "      <td>Carbon Hill Mine</td>\n",
       "      <td>76241</td>\n",
       "      <td>84966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2013</td>\n",
       "      <td>103410</td>\n",
       "      <td>Coal Valley Mine</td>\n",
       "      <td>407841</td>\n",
       "      <td>158591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2013</td>\n",
       "      <td>103358</td>\n",
       "      <td>Old Union</td>\n",
       "      <td>284563</td>\n",
       "      <td>161805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2013</td>\n",
       "      <td>5000030</td>\n",
       "      <td>Usibelli</td>\n",
       "      <td>1631584</td>\n",
       "      <td>286079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2013</td>\n",
       "      <td>201195</td>\n",
       "      <td>Kayenta Mine</td>\n",
       "      <td>7602722</td>\n",
       "      <td>1015333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                       Mine Name  Production  Labor Hours\n",
       "2   2013   100759  North River #1 Underground Min     1440115       474784\n",
       "6   2013   100329                    Concord Mine           0       144002\n",
       "7   2013   100851                  Oak Grove Mine     2269014      1001809\n",
       "9   2013   102901                Shoal Creek Mine     1453024      1237415\n",
       "10  2013   103180             Sloan Mountain Mine      327780       196963\n",
       "11  2013   103182                        Fishtrap      175058        87314\n",
       "12  2013   103285                     Narley Mine      154861        90584\n",
       "15  2013   103419               Maxine-Pratt Mine      125824       107469\n",
       "22  2013   103422                 Clark No 1 Mine      122727       140250\n",
       "24  2013   101247                       No 4 Mine     2622528      1551141\n",
       "25  2013   101401                       No 7 Mine     5405412      2464719\n",
       "26  2013   103172  Searles Mine No. 2, 3, 4, 5, 6      258078       119542\n",
       "28  2013   103303                    Shannon Mine      317491       164388\n",
       "32  2013   100347                    Choctaw Mine      537429       215295\n",
       "33  2013   101362                 Manchester Mine      219457       116914\n",
       "34  2013   102996                  Jap Creek Mine      375715       164093\n",
       "38  2013   103342             Reese's Branch Mine      263888       115123\n",
       "41  2013   103376                      Town Creek      299167       176499\n",
       "42  2013   103389                Carbon Hill Mine       76241        84966\n",
       "43  2013   103410                Coal Valley Mine      407841       158591\n",
       "47  2013   103358                       Old Union      284563       161805\n",
       "48  2013  5000030                        Usibelli     1631584       286079\n",
       "49  2013   201195                    Kayenta Mine     7602722      1015333"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To display all records that include two specific MSHA ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y=x1[x1['MSHA ID'].isin([102976,103380])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>MSHA ID</th>\n",
       "      <th>Mine Name</th>\n",
       "      <th>Production</th>\n",
       "      <th>Labor Hours</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>14828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>102976</td>\n",
       "      <td>Piney Woods Preparation Plant</td>\n",
       "      <td>0</td>\n",
       "      <td>23193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013</td>\n",
       "      <td>103380</td>\n",
       "      <td>Calera</td>\n",
       "      <td>0</td>\n",
       "      <td>1402</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  MSHA ID                      Mine Name  Production  Labor Hours\n",
       "18  2013   102976  Piney Woods Preparation Plant           0        14828\n",
       "19  2013   102976  Piney Woods Preparation Plant           0        23193\n",
       "20  2013   103380                         Calera           0        12621\n",
       "21  2013   103380                         Calera           0         1402"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To display Mine Name starting with P"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# will discuss first Lambda function to understand above query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=pd.read_excel('C:/Users/Pratik Bharadwaj/Desktop/employee.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>emp_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>Steven</td>\n",
       "      <td>King</td>\n",
       "      <td>2003-06-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Neena</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>2005-09-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>Lex</td>\n",
       "      <td>De Haan</td>\n",
       "      <td>2001-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>2006-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>2007-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>105</td>\n",
       "      <td>David</td>\n",
       "      <td>Austin</td>\n",
       "      <td>2005-06-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>valli</td>\n",
       "      <td>Pataballa</td>\n",
       "      <td>2006-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>2007-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>108</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Greenberg</td>\n",
       "      <td>2002-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>109</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Feviet</td>\n",
       "      <td>2002-08-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>John</td>\n",
       "      <td>Chen</td>\n",
       "      <td>2005-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111</td>\n",
       "      <td>Ismael</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>2005-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "      <td>Jose Manueal</td>\n",
       "      <td>Urman</td>\n",
       "      <td>2006-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Popp</td>\n",
       "      <td>2007-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>114</td>\n",
       "      <td>Den</td>\n",
       "      <td>Raphaely</td>\n",
       "      <td>2002-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>115</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Khoo</td>\n",
       "      <td>2003-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Baida</td>\n",
       "      <td>2005-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>117</td>\n",
       "      <td>Sigal</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>2005-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>118</td>\n",
       "      <td>Guy</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>2006-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>119</td>\n",
       "      <td>Karen</td>\n",
       "      <td>Colmenares</td>\n",
       "      <td>2007-08-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    emp_id    first_name   last_name  hire_date\n",
       "0      100        Steven        King 2003-06-17\n",
       "1      101         Neena     Kochhar 2005-09-21\n",
       "2      102           Lex     De Haan 2001-01-13\n",
       "3      103     Alexander      Hunold 2006-01-03\n",
       "4      104         Bruce       Ernst 2007-05-21\n",
       "5      105         David      Austin 2005-06-25\n",
       "6      106         valli   Pataballa 2006-02-05\n",
       "7      107         Diana     Lorentz 2007-02-07\n",
       "8      108         Nancy   Greenberg 2002-08-17\n",
       "9      109        Daniel      Feviet 2002-08-16\n",
       "10     110          John        Chen 2005-09-28\n",
       "11     111        Ismael     Sciarra 2005-09-30\n",
       "12     112  Jose Manueal       Urman 2006-03-07\n",
       "13     113          Luis        Popp 2007-12-07\n",
       "14     114           Den    Raphaely 2002-12-07\n",
       "15     115     Alexander        Khoo 2003-05-18\n",
       "16     116        Shelli       Baida 2005-12-24\n",
       "17     117         Sigal      Tobias 2005-07-24\n",
       "18     118           Guy      Himuro 2006-11-15\n",
       "19     119         Karen  Colmenares 2007-08-10"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To find name of employees where hire data is greater than 01-01-07"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd=z[z['hire_date']>'20070101']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>hire_date</th>\n",
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       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>2007-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>2007-02-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Popp</td>\n",
       "      <td>2007-12-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>119</td>\n",
       "      <td>Karen</td>\n",
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       "    </tr>\n",
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      ],
      "text/plain": [
       "    emp_id first_name   last_name  hire_date\n",
       "4      104      Bruce       Ernst 2007-05-21\n",
       "7      107      Diana     Lorentz 2007-02-07\n",
       "13     113       Luis        Popp 2007-12-07\n",
       "19     119      Karen  Colmenares 2007-08-10"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sort values according to hire date attribute/column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=z.sort_values('hire_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
       "      <td>105</td>\n",
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       "      <td>2005-06-25</td>\n",
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       "      <th>17</th>\n",
       "      <td>117</td>\n",
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       "      <td>2005-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Neena</td>\n",
       "      <td>Kochhar</td>\n",
       "      <td>2005-09-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>110</td>\n",
       "      <td>John</td>\n",
       "      <td>Chen</td>\n",
       "      <td>2005-09-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>111</td>\n",
       "      <td>Ismael</td>\n",
       "      <td>Sciarra</td>\n",
       "      <td>2005-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Baida</td>\n",
       "      <td>2005-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Hunold</td>\n",
       "      <td>2006-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>106</td>\n",
       "      <td>valli</td>\n",
       "      <td>Pataballa</td>\n",
       "      <td>2006-02-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "      <td>Jose Manueal</td>\n",
       "      <td>Urman</td>\n",
       "      <td>2006-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>118</td>\n",
       "      <td>Guy</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>2006-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>107</td>\n",
       "      <td>Diana</td>\n",
       "      <td>Lorentz</td>\n",
       "      <td>2007-02-07</td>\n",
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       "      <th>4</th>\n",
       "      <td>104</td>\n",
       "      <td>Bruce</td>\n",
       "      <td>Ernst</td>\n",
       "      <td>2007-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>119</td>\n",
       "      <td>Karen</td>\n",
       "      <td>Colmenares</td>\n",
       "      <td>2007-08-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>113</td>\n",
       "      <td>Luis</td>\n",
       "      <td>Popp</td>\n",
       "      <td>2007-12-07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    emp_id    first_name   last_name  hire_date\n",
       "2      102           Lex     De Haan 2001-01-13\n",
       "9      109        Daniel      Feviet 2002-08-16\n",
       "8      108         Nancy   Greenberg 2002-08-17\n",
       "14     114           Den    Raphaely 2002-12-07\n",
       "15     115     Alexander        Khoo 2003-05-18\n",
       "0      100        Steven        King 2003-06-17\n",
       "5      105         David      Austin 2005-06-25\n",
       "17     117         Sigal      Tobias 2005-07-24\n",
       "1      101         Neena     Kochhar 2005-09-21\n",
       "10     110          John        Chen 2005-09-28\n",
       "11     111        Ismael     Sciarra 2005-09-30\n",
       "16     116        Shelli       Baida 2005-12-24\n",
       "3      103     Alexander      Hunold 2006-01-03\n",
       "6      106         valli   Pataballa 2006-02-05\n",
       "12     112  Jose Manueal       Urman 2006-03-07\n",
       "18     118           Guy      Himuro 2006-11-15\n",
       "7      107         Diana     Lorentz 2007-02-07\n",
       "4      104         Bruce       Ernst 2007-05-21\n",
       "19     119         Karen  Colmenares 2007-08-10\n",
       "13     113          Luis        Popp 2007-12-07"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to display list of employee between specific month and year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=z[(z['hire_date']>='jan-2005') & (z['hire_date']<='dec-2006')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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       "      <th>10</th>\n",
       "      <td>110</td>\n",
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       "      <td>Ismael</td>\n",
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       "      <td>2005-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "      <td>Jose Manueal</td>\n",
       "      <td>Urman</td>\n",
       "      <td>2006-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116</td>\n",
       "      <td>Shelli</td>\n",
       "      <td>Baida</td>\n",
       "      <td>2005-12-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>117</td>\n",
       "      <td>Sigal</td>\n",
       "      <td>Tobias</td>\n",
       "      <td>2005-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>118</td>\n",
       "      <td>Guy</td>\n",
       "      <td>Himuro</td>\n",
       "      <td>2006-11-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    emp_id    first_name  last_name  hire_date\n",
       "1      101         Neena    Kochhar 2005-09-21\n",
       "3      103     Alexander     Hunold 2006-01-03\n",
       "5      105         David     Austin 2005-06-25\n",
       "6      106         valli  Pataballa 2006-02-05\n",
       "10     110          John       Chen 2005-09-28\n",
       "11     111        Ismael    Sciarra 2005-09-30\n",
       "12     112  Jose Manueal      Urman 2006-03-07\n",
       "16     116        Shelli      Baida 2005-12-24\n",
       "17     117         Sigal     Tobias 2005-07-24\n",
       "18     118           Guy     Himuro 2006-11-15"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "# diamonds.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
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       "      <td>61.5</td>\n",
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       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
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       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
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       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=['carat','cut','x','y','z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat        cut     x     y     z\n",
       "0   0.23      Ideal  3.95  3.98  2.43\n",
       "1   0.21    Premium  3.89  3.84  2.31\n",
       "2   0.23       Good  4.05  4.07  2.31\n",
       "3   0.29    Premium  4.20  4.23  2.63\n",
       "4   0.31       Good  4.34  4.35  2.75\n",
       "5   0.24  Very Good  3.94  3.96  2.48\n",
       "6   0.24  Very Good  3.95  3.98  2.47"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds[c].head(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to create new \"qulity-color\" series(use bracket notaion define series)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds['Quality-color']=diamonds.cut +','+ diamonds.color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat      cut color clarity  depth  table  price     x     y     z  \\\n",
      "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
      "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
      "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   \n",
      "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   \n",
      "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   \n",
      "\n",
      "  Quality-color  \n",
      "0       Ideal,E  \n",
      "1     Premium,E  \n",
      "2        Good,E  \n",
      "3     Premium,I  \n",
      "4        Good,J  \n"
     ]
    }
   ],
   "source": [
    "print(diamonds.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat            float64\n",
       "cut               object\n",
       "color             object\n",
       "clarity           object\n",
       "depth            float64\n",
       "table            float64\n",
       "price              int64\n",
       "x                float64\n",
       "y                float64\n",
       "z                float64\n",
       "Quality-color     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>21551</td>\n",
       "      <td>11292</td>\n",
       "      <td>13065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cut  color clarity\n",
       "count   53940  53940   53940\n",
       "unique      5      7       8\n",
       "top     Ideal      G     SI1\n",
       "freq    21551  11292   13065"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
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       "      <td>0.23</td>\n",
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       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
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       "      <th>1</th>\n",
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       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
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       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
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       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
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       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
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       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds.rename(columns={'color':'my_color','clarity':'my_clarity'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
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       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
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       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
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       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
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       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
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       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
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       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut my_color my_clarity  depth  table  price     x     y  \\\n",
       "0       0.23      Ideal        E        SI2   61.5   55.0    326  3.95  3.98   \n",
       "1       0.21    Premium        E        SI1   59.8   61.0    326  3.89  3.84   \n",
       "2       0.23       Good        E        VS1   56.9   65.0    327  4.05  4.07   \n",
       "3       0.29    Premium        I        VS2   62.4   58.0    334  4.20  4.23   \n",
       "4       0.31       Good        J        SI2   63.3   58.0    335  4.34  4.35   \n",
       "...      ...        ...      ...        ...    ...    ...    ...   ...   ...   \n",
       "53935   0.72      Ideal        D        SI1   60.8   57.0   2757  5.75  5.76   \n",
       "53936   0.72       Good        D        SI1   63.1   55.0   2757  5.69  5.75   \n",
       "53937   0.70  Very Good        D        SI1   62.8   60.0   2757  5.66  5.68   \n",
       "53938   0.86    Premium        H        SI2   61.0   58.0   2757  6.15  6.12   \n",
       "53939   0.75      Ideal        D        SI2   62.2   55.0   2757  5.83  5.87   \n",
       "\n",
       "          z  \n",
       "0      2.43  \n",
       "1      2.31  \n",
       "2      2.31  \n",
       "3      2.63  \n",
       "4      2.75  \n",
       "...     ...  \n",
       "53935  3.50  \n",
       "53936  3.61  \n",
       "53937  3.56  \n",
       "53938  3.74  \n",
       "53939  3.64  \n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
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       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
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       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
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       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
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     "execution_count": 185,
     "metadata": {},
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   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds.drop('cut',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   carat color clarity  depth  table  price     x     y     z\n",
       "0   0.23     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds.drop(['carat','cut','x'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>clarity</th>\n",
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       "      <th>price</th>\n",
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       "      <td>3.84</td>\n",
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       "      <th>2</th>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  color clarity  depth  table  price     y     z\n",
       "0     E     SI2   61.5   55.0    326  3.98  2.43\n",
       "1     E     SI1   59.8   61.0    326  3.84  2.31\n",
       "2     E     VS1   56.9   65.0    327  4.07  2.31\n",
       "3     I     VS2   62.4   58.0    334  4.23  2.63\n",
       "4     J     SI2   63.3   58.0    335  4.35  2.75"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds.drop([2,4],axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>y</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>E</td>\n",
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       "      <td>3.84</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
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       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>J</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>I</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53938 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      color clarity  depth  table  price     y     z\n",
       "0         E     SI2   61.5   55.0    326  3.98  2.43\n",
       "1         E     SI1   59.8   61.0    326  3.84  2.31\n",
       "3         I     VS2   62.4   58.0    334  4.23  2.63\n",
       "5         J    VVS2   62.8   57.0    336  3.96  2.48\n",
       "6         I    VVS1   62.3   57.0    336  3.98  2.47\n",
       "...     ...     ...    ...    ...    ...   ...   ...\n",
       "53935     D     SI1   60.8   57.0   2757  5.76  3.50\n",
       "53936     D     SI1   63.1   55.0   2757  5.75  3.61\n",
       "53937     D     SI1   62.8   60.0   2757  5.68  3.56\n",
       "53938     H     SI2   61.0   58.0   2757  6.12  3.74\n",
       "53939     D     SI2   62.2   55.0   2757  5.87  3.64\n",
       "\n",
       "[53938 rows x 7 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27749    18823\n",
       "27748    18818\n",
       "27747    18806\n",
       "27746    18804\n",
       "27745    18803\n",
       "         ...  \n",
       "4          335\n",
       "3          334\n",
       "2          327\n",
       "1          326\n",
       "0          326\n",
       "Name: price, Length: 53940, dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.price.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# diamonds.sort_values('price', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
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       "      <th>z</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False, False, False, False, True, False, False, False, False, False, True, False, False, True, False, True, True, True, True, True]\n"
     ]
    }
   ],
   "source": [
    "L=[]\n",
    "for i in diamonds.carat:\n",
    "    if i>=0.3:\n",
    "        L.append(True)\n",
    "    else:\n",
    "        L.append(False)\n",
    "print(L[0:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# we can also import any file from the internet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "diamonds = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
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       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
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       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
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       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
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       "      <td>0.86</td>\n",
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       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
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       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
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      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find details of diamonds where length>5,width>5 & depth>5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=diamonds[(diamonds.x>5)&(diamonds.y>5)&(diamonds.z>5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.90</td>\n",
       "      <td>7.97</td>\n",
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       "      <td>8.50</td>\n",
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      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x      y      z\n",
       "11778   1.83       Fair     J      I1   70.0   58.0   5083  7.34   7.28   5.12\n",
       "13002   2.14       Fair     J      I1   69.4   57.0   5405  7.74   7.70   5.36\n",
       "13118   2.15       Fair     J      I1   65.5   57.0   5430  8.01   7.95   5.23\n",
       "13562   1.96       Fair     F      I1   66.6   60.0   5554  7.59   7.56   5.04\n",
       "13757   2.22       Fair     J      I1   66.7   56.0   5607  8.04   8.02   5.36\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...    ...    ...\n",
       "27748   2.00  Very Good     G     SI1   63.5   56.0  18818  7.90   7.97   5.04\n",
       "27749   2.29    Premium     I     VS2   60.8   60.0  18823  8.50   8.47   5.16\n",
       "48410   0.51  Very Good     E     VS1   61.8   54.7   1970  5.12   5.15  31.80\n",
       "49189   0.51      Ideal     E     VS1   61.8   55.0   2075  5.15  31.80   5.12\n",
       "49905   0.50  Very Good     G    VVS1   63.7   58.0   2180  5.01   5.04   5.06\n",
       "\n",
       "[1457 rows x 10 columns]"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to display premium or ideal values from cut column only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
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       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
       "       'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       carat      cut color clarity  depth  table  price     x     y     z\n",
      "0       0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "1       0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "3       0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "12      0.22  Premium     F     SI1   60.4   61.0    342  3.88  3.84  2.33\n",
      "14      0.20  Premium     E     SI2   60.2   62.0    345  3.79  3.75  2.27\n",
      "...      ...      ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
      "53928   0.79  Premium     E     SI2   61.4   58.0   2756  6.03  5.96  3.68\n",
      "53930   0.71  Premium     E     SI1   60.5   55.0   2756  5.79  5.74  3.49\n",
      "53931   0.71  Premium     F     SI1   59.8   62.0   2756  5.74  5.73  3.43\n",
      "53934   0.72  Premium     D     SI1   62.7   59.0   2757  5.69  5.73  3.58\n",
      "53938   0.86  Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
      "\n",
      "[13792 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(diamonds[(diamonds.cut=='Premium')|(diamonds.cut=='Ideal').head()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
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       "      <td>3.84</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
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       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [carat, cut, color, clarity, depth, table, price, x, y, z]\n",
       "Index: []"
      ]
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     "execution_count": 211,
     "metadata": {},
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   ],
   "source": [
    "diamonds[diamonds.cut.isin(['fair','good','premium'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to show only numeric values from data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat    float64\n",
       "depth    float64\n",
       "table    float64\n",
       "price      int64\n",
       "x        float64\n",
       "y        float64\n",
       "z        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.select_dtypes(include=[np.number]).dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to display out mean of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat       0.797940\n",
       "depth      61.749405\n",
       "table      57.457184\n",
       "price    3932.799722\n",
       "x           5.731157\n",
       "y           5.734526\n",
       "z           3.538734\n",
       "dtype: float64"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find out mean of each column of diamond data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    64.727143\n",
       "1    65.292857\n",
       "2    65.651429\n",
       "3    66.535714\n",
       "4    66.864286\n",
       "dtype: float64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.mean(axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find out mean of each row of diamond data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat       0.797940\n",
       "depth      61.749405\n",
       "table      57.457184\n",
       "price    3932.799722\n",
       "x           5.731157\n",
       "dtype: float64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.mean(axis=0).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to calculate count, maximum, minimum price foe=r each cut of diamond data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
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       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
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       "      <td>59.8</td>\n",
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       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
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       "      <td>58.0</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
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       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           count    max  min\n",
      "cut                         \n",
      "Fair        1610  18574  337\n",
      "Good        4906  18788  327\n",
      "Ideal      21551  18806  326\n",
      "Premium    13791  18823  326\n",
      "Very Good  12082  18818  336\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.groupby('cut').price.agg(['count','max','min']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find how many times each values of cut series of daimonds data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal        21551\n",
      "Premium      13791\n",
      "Very Good    12082\n",
      "Good          4906\n",
      "Fair          1610\n",
      "Name: cut, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.cut.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ideal        0.399537\n",
      "Premium      0.255673\n",
      "Very Good    0.223990\n",
      "Good         0.090953\n",
      "Fair         0.029848\n",
      "Name: cut, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.cut.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ideal' 'Premium' 'Good' 'Very Good' 'Fair']\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.cut.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
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       "      <td>0.72</td>\n",
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       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
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       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
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       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    53940.000000\n",
      "mean         0.797940\n",
      "std          0.474011\n",
      "min          0.200000\n",
      "25%          0.400000\n",
      "50%          0.700000\n",
      "75%          1.040000\n",
      "max          5.010000\n",
      "Name: carat, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.carat.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              carat         depth         table         price             x  \\\n",
      "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
      "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
      "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
      "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
      "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
      "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
      "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
      "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
      "\n",
      "                  y             z  \n",
      "count  53940.000000  53940.000000  \n",
      "mean       5.734526      3.538734  \n",
      "std        1.142135      0.705699  \n",
      "min        0.000000      0.000000  \n",
      "25%        4.720000      2.910000  \n",
      "50%        5.710000      3.530000  \n",
      "75%        6.540000      4.040000  \n",
      "max       58.900000     31.800000  \n"
     ]
    }
   ],
   "source": [
    "print(diamonds.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat    cut  color  clarity  depth  table  price      x      y      z\n",
      "0  False  False  False    False  False  False  False  False  False  False\n",
      "1  False  False  False    False  False  False  False  False  False  False\n",
      "2  False  False  False    False  False  False  False  False  False  False\n",
      "3  False  False  False    False  False  False  False  False  False  False\n",
      "4  False  False  False    False  False  False  False  False  False  False\n",
      "5  False  False  False    False  False  False  False  False  False  False\n",
      "6  False  False  False    False  False  False  False  False  False  False\n",
      "7  False  False  False    False  False  False  False  False  False  False\n",
      "8  False  False  False    False  False  False  False  False  False  False\n",
      "9  False  False  False    False  False  False  False  False  False  False\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.isnull().head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to set an existing columns as an index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
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       "      <td>3.84</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>58.0</td>\n",
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       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
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       "      <td>SI2</td>\n",
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       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
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       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
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       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
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       "      <th>y</th>\n",
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       "      <th>color</th>\n",
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       "      <th>E</th>\n",
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       "      <td>2.31</td>\n",
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       "      <th>E</th>\n",
       "      <td>0.23</td>\n",
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       "      <td>65.0</td>\n",
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       "      <td>4.07</td>\n",
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       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.29</td>\n",
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       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
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       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
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       "      <td>0.31</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut clarity  depth  table  price     x     y     z\n",
       "color                                                                 \n",
       "E       0.23      Ideal     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "E       0.21    Premium     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "E       0.23       Good     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "I       0.29    Premium     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "J       0.31       Good     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...     ...    ...    ...    ...   ...   ...   ...\n",
       "D       0.72      Ideal     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "D       0.72       Good     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "D       0.70  Very Good     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "H       0.86    Premium     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "D       0.75      Ideal     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 9 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.set_index('color',inplace=True)\n",
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/diamonds.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to sort the series by its values and index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "series sorted by its values\n",
      "Fair          1610\n",
      "Good          4906\n",
      "Very Good    12082\n",
      "Premium      13791\n",
      "Ideal        21551\n",
      "Name: cut, dtype: int64\n",
      "series sorted by its index\n",
      "326      2\n",
      "327      1\n",
      "334      1\n",
      "335      1\n",
      "336      2\n",
      "        ..\n",
      "18803    1\n",
      "18804    1\n",
      "18806    1\n",
      "18818    1\n",
      "18823    1\n",
      "Name: price, Length: 11602, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('series sorted by its values')\n",
    "print(diamonds.cut.value_counts().sort_values())\n",
    "print('series sorted by its index')\n",
    "print(diamonds.price.value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# x,y,z,are length width and depth of diamond respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    38.202030\n",
      "1    34.505856\n",
      "2    38.076885\n",
      "3    46.724580\n",
      "4    51.917250\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print((diamonds.x*diamonds.y*diamonds.z).head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to contadinate color attribute with diamonds data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat        cut color clarity  depth  table  price     x     y     z color\n",
      "0   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43     E\n",
      "1   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31     E\n",
      "2   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31     E\n",
      "3   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63     I\n",
      "4   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75     J\n",
      "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48     J\n",
      "6   0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47     I\n",
      "7   0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53     H\n",
      "8   0.22       Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49     E\n",
      "9   0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39     H\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([diamonds,diamonds.color],axis=1).head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       carat        cut color clarity  depth  table  price     x     y     z\n",
      "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...\n",
      "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50\n",
      "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61\n",
      "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56\n",
      "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74\n",
      "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64\n",
      "\n",
      "[53940 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.loc[0:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat        cut color clarity  depth  table  price     x     y     z\n",
      "2   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "3   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "4   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.loc[2:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   carat        cut color clarity  depth  table  price     x     y     z\n",
      "0   0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
      "1   0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
      "2   0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
      "3   0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
      "4   0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75\n",
      "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.loc[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  color  price\n",
      "0     E    326\n",
      "1     E    326\n",
      "2     E    327\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.loc[:2,['color','price']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To read rows in cut which is preium and to display 'color' column with it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1        E\n",
      "3        I\n",
      "12       F\n",
      "14       E\n",
      "15       E\n",
      "        ..\n",
      "53928    E\n",
      "53930    E\n",
      "53931    F\n",
      "53934    D\n",
      "53938    H\n",
      "Name: color, Length: 13791, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.loc[diamonds.cut=='Premium','color'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53940 non-null  float64\n",
      " 1   cut      53940 non-null  object \n",
      " 2   color    53940 non-null  object \n",
      " 3   clarity  53940 non-null  object \n",
      " 4   depth    53940 non-null  float64\n",
      " 5   table    53940 non-null  float64\n",
      " 6   price    53940 non-null  int64  \n",
      " 7   x        53940 non-null  float64\n",
      " 8   y        53940 non-null  float64\n",
      " 9   z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 4.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find actual memory usage by any data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53940 non-null  float64\n",
      " 1   cut      53940 non-null  object \n",
      " 2   color    53940 non-null  object \n",
      " 3   clarity  53940 non-null  object \n",
      " 4   depth    53940 non-null  float64\n",
      " 5   table    53940 non-null  float64\n",
      " 6   price    53940 non-null  int64  \n",
      " 7   x        53940 non-null  float64\n",
      " 8   y        53940 non-null  float64\n",
      " 9   z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 12.2 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.info(memory_usage='deep'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index          128\n",
      "carat       431520\n",
      "cut        3413674\n",
      "color      3128520\n",
      "clarity    3242590\n",
      "depth       431520\n",
      "table       431520\n",
      "price       431520\n",
      "x           431520\n",
      "y           431520\n",
      "z           431520\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.memory_usage(deep=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       carat    cut color clarity  depth  table  price     x     y     z\n",
      "39906   0.50   Fair     G     SI1   67.1   59.0   1103  4.96  4.84  3.29\n",
      "6326    0.71  Ideal     D     VS2   61.2   55.0   4029  5.74  5.78  3.53\n",
      "20152   1.22  Ideal     G     VS1   62.5   57.0   8608  6.82  6.77  4.25\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.sample(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to find duplicate rows from diamonds data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "146\n"
     ]
    }
   ],
   "source": [
    "print(diamonds.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# date time library also importing date from date time library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "now=pd.to_datetime(str(date.today()),format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-16 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(now)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to deal with date and time both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to create a date time object for jan 10  2011 of 9:20 pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-10 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(datetime(2011,1,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011-01-10 21:20:00\n"
     ]
    }
   ],
   "source": [
    "print(datetime(2011,1,10,21,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-16 14:57:05.795378\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-01-11 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(datetime(2012,1,11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-01-11 21:20:00\n"
     ]
    }
   ],
   "source": [
    "print(datetime(2012,1,11,21,20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# print date only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2012-01-11\n"
     ]
    }
   ],
   "source": [
    "print(datetime.date(datetime(2012,1,11)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-06-16\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now().date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14:57:05.915395\n"
     ]
    }
   ],
   "source": [
    "print(datetime.now().time())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to print date time after and before specifies date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date is : 2012-10-03 00:00:00\n",
      "Tomorrow : 2012-10-04 00:00:00\n",
      "Yesterday : 2012-10-02 00:00:00\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from datetime import datetime,date\n",
    "\n",
    "today=(datetime(2012,10,3))\n",
    "print(\"Date is :\",today)\n",
    "\n",
    "tomorrow=today+pd.Timedelta(days=1)\n",
    "print(\"Tomorrow :\",tomorrow)\n",
    "\n",
    "yesterday=today - pd.Timedelta(days=1)\n",
    "print(\"Yesterday :\",yesterday)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Also find days between dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 days, 0:00:00\n"
     ]
    }
   ],
   "source": [
    "date1=datetime(2016,8,1)\n",
    "date2=datetime(2016,7,4)\n",
    "print(date1-date2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data visualization using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "#optional\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(0,5,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# single ( * ) means adding elements and double( ** ) means multiply in a array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# functional method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23942d34b50>]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qkUh/qiIRpKCknFufX8Unm3KZMqYH9148gJgGmqyMVCpwkQix62AxN8xNY3NuIQ9dkcyEkd28jiRBpgIXiQDpOw4xfV4apRU+5l6fypg+8V5HknqgAhcJc6+uzuKul9aS2KoJC6edoUUYoogKXCRMOef4ywdf89cPvya1e1v+MXE4bZvrnibRRAUuEoZKyiu566W1vL4mmx8O68Lvf5BM44a6LD7aqMBFwkxuQSnT5qWxaudh7h7XnxvP6anL4qOUClwkjGzcm8+UOWkcKCrlHxOGMS450etI4qGTrshjZrPNLMfMMqpta2tm75vZ1/7Puj5XJMg+2riPHz6xlAqfjxenn6nylhotqTYHGHfMtnuAD51zfYAP/V+LSBA455j92TZumJtG9/jmvHrzGE7r0srrWBICTlrgzrklwMFjNl8OzPU/ngtcEdhYIgJQXunjvn9m8OAb6zlvQAdevHGU1q2Uo2p7DLyDc24PgHNuj5m1P9ETzWwaMA0gKSmplrsTiT55R8q5ef5KPtu8nxvP6cUvL+xHA10WL9UEfRLTOTcDmAGQkpLigr0/kUiw40ARk+esYOfBYv74o8FcldLV60gSgmpb4PvMLNE/+k4EcgIZSiSaLdt6gBufS8cB86aMYGTPdl5HkhBVk0nM43kNmOR/PAl4NTBxRKLbS+m7mTBrGW2aNeKVm0arvOU7nXQEbmbPA+cC8Wa2G7gfeBh4wcymADuBHwczpEik8/kcf3pvE098soUze7XjyWuG06qZVouX73bSAnfOjT/Bt74X4CwiUam4rII7F63hncy9jE9N4sHLBxEbU9t/HEs00ZWYIh7al1/CDXPTyMjO475LBjBlTA9dFi81pgIX8cjnm/dzx6LVFJZWMHNiCucN7OB1JAkzKnCRelZW4ePR9zYx49Ot9IxvztzJqQxIjPM6loQhFbhIPdqcU8htC1eRmZ3P1SOS+M0lA2naSLeBldpRgYvUA+cczy/fxYNvZNI0NoanJg7nwkEdvY4lYU4FLhJkh4rKuGfxWt7N3Mfo3u147KohdIjT/Uyk7lTgIkH0+eb93PnCag4WlfHri6vOMtH9TCRQVOAiQXDsROWsSWeQ3Fm3gJXAUoGLBNjmnEJuX7SKjCxNVEpwqcBFAkQTlVLfVOAiAaCJSvGCClykjqpPVN57cX9uGNNTE5VSL1TgIrVUfaKyhyYqxQMqcJFa2JJbdUWlJirFSypwkVPgnGPhil08+Pp6Gsc20ESleEoFLlJDmqiUUKMCF6kBTVRKKFKBi3wHTVRKKFOBi5zAsROV910ygGaN9FdGQod+G0WOoYlKCRcqcJFqNFEp4UQFLuK3dPN+7nxhDQeKSjVRKWFBBS5Rr7Siksfe/4oZS6omKp+eNFoTlRIWVOAS1T7auI8HX1/P9gPFjE9N4jeXaqJSwod+UyUqbckt5HdvrOeTTbn0TKhaGf6cvglexxI5JSpwiSoFJeX870ebeebzbTRpGMN9lwzg2lHdadSwgdfRRE6ZClyigs/nWLwqi4ff3sj+wlKuSunCXRf2J6FlY6+jidSaClwi3updh7n/tUzW7DrM0KTWzJqUwuldW3sdS6TOVOASsXIKSnjknU28mL6bhJaNefTHp3Pl0M46NVAihgpcIk5ZhY+5S7fz1w+/prSikunn9OTWsX1o0Vi/7hJZ6vQbbWbbgQKgEqhwzqUEIpRIbX2yKYcH31jP1twi/qNfAr+5dCA9E1p4HUskKAIxJPkP59z+ALyOSK1t31/EQ2+u54MNOfSIb87s61IY27+D17FEgkr/ppSwVlRawd8+3sysT7cRG2Pcc1F/rh/dncYNtbyZRL66FrgD3jMzBzzlnJtx7BPMbBowDSApKamOuxOp4pzjn6urTgvcl1/KD4Z15p5x/WmvG09JFKlrgY92zmWbWXvgfTPb6JxbUv0J/lKfAZCSkuLquD8R1u3O44HXM0nfcYjBXVrx5IThDEtq43UskXpXpwJ3zmX7P+eY2StAKrDku39KpHb2F5byp3c3sShtF+2aN+KPPxzMj4Z30WmBErVqXeBm1hxo4Jwr8D++AHgwYMlE/MorfTz7xQ7+8sFXHCmrZMroHvzsvD7ENYn1OpqIp+oyAu8AvGJm37zOAufcOwFJJeL32df7eeD1TDbnFHJWn3ju//5Aerdv6XUskZBQ6wJ3zm0FTg9gFpGjdh0s5qE31/Nu5j6S2jZjxsThnD+wA/4Bg4ig0wglxBSXVfDkJ1t4aslWYsy468J+TBnTgyaxOi1Q5FgqcAkJ5ZU+Xl+TzSPvbmJPXgmXD+nEPRf1J7FVU6+jiYQsFbh4qrC0goXLd/LM59vJOnyEgYlxPD5+KGd0b+t1NJGQpwIXT+zNK+GZpdtYsGwnBSUVpPZoy28vG8TY/u11WqBIDanApV5t2lvAjCVbeW1NFpU+x0XJiUw9uydDdH9ukVOmApegc86xdMsBZizZyr++yqVpbAxXpyYxZUxPkto18zqeSNhSgUvQlFf6eGvdHmYs2Upmdj7xLRrxiwv6cs2IbrRp3sjreCJhTwUuAXfsxGTPhOY8/IPTuGJoZ50OKBJAKnAJmH35JTzz+XbmL9uhiUmReqAClzrbtLeAmZ9u5dXV/z8xecNZPRiqOwSKBJUKXGrFOccXWw7wlCYmRTyjApdT8s3E5MxPt5KRpYlJES+pwKVGNDEpEnpU4PKdNDEpErpU4HJcmpgUCX0qcDmqoKScjzbmsHhl1rcmJieP6UG3ds29jicix1CBR7nDxWW8v34f72Ts5dOv91NW6aN9y8b8/Py+TBipiUmRUKYCj0K5BaW8t34v72Ts5YstB6jwOTq3bsrEUd24KLkjw5La6Pi2SBhQgUeJvXklvJOxh7cz9rJi+0F8Drq3a8YNZ/XkouSODO7SSsuViYQZFXgE23WwmLf9pb1q52EA+nZowS1j+3BRckf6d2yp0hYJYyrwCLM5p/DoSDszOx+AQZ3iuOvCfoxL7kivhBYeJxSRQFGBhznnHBv3FvB2xl7eydjDV/sKARia1Jp7L+7PuEGJurRdJEKpwMOQc461u/OOlvb2A8U0MDije1se+P5ALkzuqMWARaKACjxM+HyOlTsP8da6vbybuZesw0eIaWCc2asdU8/uyQUDO5LQsrHXMUWkHqnAQ1hFpY/l2w7ydkZVaecUlNIopgFn9Ynn9vP6cP7ADrRupvO0RaKVCjyEHC4uIzM7n4ysPNZl5bF0ywEOFpXRJLYB5/Ztz0WndWRs//a0bBLrdVQRCQEqcI/kFJSQmVVV1hnZeWRk5ZN1+MjR73du3ZQxveO5KLkj5/RLoFkj/VGJyLepFYLMOUfW4SNkZueTmZVHhn+EnVNQevQ5PeKbMzSpNRNHdSO5UysGdYrTJewiclIq8ADy+Rw7DhYfHVVnZuWTkZ3H4eJyABoY9GnfkjF94hnUqRXJneIY2ClOh0REpFZU4LVUUelj6/6iqrL2F/X67HwKSysAiI0x+nVsybhBHRnUuaqs+3eMo2kjLX4gIoGhAq+B0opKvt5X+K3j1Rv25FNa4QOgSWwDBibG8YNhnasOgXSOo0/7ljRq2MDj5CISyepU4GY2DvgrEAM87Zx7OCCpgqi0opK8I+XkHykn70gF+UfKyS8pJ+9IOXnF//84/0gFeUfKOVhUxtb9hZRXOgBaNm7IoM5xTBjZjeTOcSR3akXPhBbE6O59IlLPal3gZhYD/B04H9gNrDCz15xz6wMV7niccxSWVpVr9aLNL/mmlKt9Lqn+vKrP34yaT6RpbAytmsYS17QhrZrG0rVtM8YOaE9yp1Ykd46ja5tmutWqiISEuozAU4HNzrmtAGa2ELgcCHiBP/7h17yUvvtoSfvciZ9rVjVKbtUstqqIm8TSp30LfynHHv0c16SqoL+1vUmsDnuISNioS4F3BnZV+3o3MOLYJ5nZNGAaQFJSUq121L5lY4Z0bV2tcKuVb5NvF3PLxg01QhaRqFCXAj9eS/7b2Ng5NwOYAZCSkvIdY+cT+2lqEj9NrV35i4hEqrocL9gNdK32dRcgu25xRESkpupS4CuAPmbWw8waAT8FXgtMLBEROZlaH0JxzlWY2S3Au1SdRjjbOZcZsGQiIvKd6nQeuHPuLeCtAGUREZFToHPmRETClApcRCRMqcBFRMKUClxEJEyZc7W6tqZ2OzPLBXbU8sfjgf0BjBMO9J6jg95zdKjLe+7mnEs4dmO9FnhdmFmacy7F6xz1Se85Oug9R4dgvGcdQhERCVMqcBGRMBVOBT7D6wAe0HuODnrP0SHg7zlsjoGLiMi3hdMIXEREqlGBi4iEqbAocDMbZ2abzGyzmd3jdZ5gM7PZZpZjZhleZ6kPZtbVzD42sw1mlmlmt3mdKdjMrImZLTezNf73/FuvM9UXM4sxs1Vm9obXWeqDmW03s3VmttrM0gL62qF+DNy/ePJXVFs8GRgf7MWTvWRmZwOFwLPOuWSv8wSbmSUCic65lWbWEkgHrojwP2MDmjvnCs0sFvgMuM0596XH0YLOzO4EUoA459ylXucJNjPbDqQ45wJ+4VI4jMCPLp7snCsDvlk8OWI555YAB73OUV+cc3uccyv9jwuADVStuRqxXJVC/5ex/o/QHk0FgJl1AS4BnvY6SyQIhwI/3uLJEf2XO5qZWXdgKLDM4yhB5z+UsBrIAd53zkX8ewb+AvwS8Hmcoz454D0zS/cv8h4w4VDgNVo8WcKfmbUAXgZud87le50n2Jxzlc65IVStJ5tqZhF9uMzMLgVynHPpXmepZ6Odc8OAi4Cb/YdIAyIcClyLJ0cB/3Hgl4H5zrnFXuepT865w8AnwDhvkwTdaOAy/zHhhcBYM3vO20jB55zL9n/OAV6h6rBwQIRDgWvx5Ajnn9CbBWxwzj3mdZ76YGYJZtba/7gpcB6w0dNQQeac+5VzrotzrjtVf48/cs5N8DhWUJlZc//EPGbWHLgACNjZZSFf4M65CuCbxZM3AC9E+uLJZvY88AXQz8x2m9kUrzMF2WhgIlUjstX+j4u9DhVkicDHZraWqkHK+865qDitLsp0AD4zszXAcuBN59w7gXrxkD+NUEREji/kR+AiInJ8KnARkTClAhcRCVMqcBGRMKUCFxEJUypwEZEwpQIXEQlT/wcbc+33exb5sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,marker='o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(0,5,11)\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('x lable')\n",
    "plt.ylabel('Y lable')\n",
    "plt.title('Title')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23942f36ac0>]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.plot(x,y)\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(y,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Set Title')"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "axes=fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "axes.plot(x,y)\n",
    "axes.set_xlabel('X label')\n",
    "axes.set_ylabel('y label')\n",
    "axes.set_title('Set Title')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Smaller Plot')"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "#left bottom weidth and height & 0.1=10%.\n",
    "axes1=fig.add_axes([0.1,0.1,0.8,0.8])\n",
    "axes2=fig.add_axes([0.2,0.5,0.4,0.3])\n",
    "axes1.plot(x,y)\n",
    "axes1.set_title('Large Plot' )\n",
    "axes2.plot(y,x)\n",
    "axes2.set_title('Smaller Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# single line, Multiple lines,pie chart,scattered chart, bar chart-- with various styles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# functional method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "plt.plot(x,y,marker=\"o\",color='r',linestyle='--')\n",
    "plt.show()                                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "y1=[4,3,6,2,5]\n",
    "plt.plot(x,y)\n",
    "plt.plot(x,y1)\n",
    "plt.xlabel(\"X axis\")\n",
    "plt.ylabel(\"Y axis\")\n",
    "plt.title(\"graph Title\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(1,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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RtI/yz11dfVwoE4ADWms3gFJqMTAceM0fhhkM543HA++9J0KcnCwp7a+8ItEiXbrA5MnyOJUPfP5862y3AJ/PQ27uZ8TEjEQpG0ePvkJGxsu0bj2RxMTfEB8/k+DgOLzeAtLTX8Lnqxovmy2islZJS8GnfWw8vBFXqsy0D+YcxK7sjOk6hl9c8gtmJ82uIdDHCo5x6+BbmTt4Lgu2LCAjP8NvttRnEfMS4J/AxYgL5V/AZq31s6f6G7OIaWgwioqkE3tCAhQUgMMBt9wCf/1r1fHw8O//3cKF4gs/dEj+dv58uPbaxrXdAny+Uk6c+KC87shSysqyGDhwPTExl1JSchS7PZygoO+Xr83JWUd6+t8rtzt2vIXY2ObfCcjr8/LJoU9w7XCxZOcSjuQdIdgWzMQeE3EmOZnZZybxEfEN9vmnWsQ8bwEvf9NHgKsAD/AVcLPWuuRU5xsBN/gVn0/C/0DC+hwOeLd8GWbzZujfH8JMZGtt8vO/ZuvWMXg8OdjtUcTFTSc+3klc3FTs9sgzv0ELocxbxocHP6wUbXehm7CgMKb0nIIzycn03tOJDYttFFsaJApFa/1boGl2AzU0bebPh//+V2KylYLf/Aaio6uOD/ned71F4vUWcPz4atzuRURFDSAx8ddERPTF4biSuLgZtG49EbvdXOQqKPYUs3bfWlypLpbvWs6J4hNEhUQxrdc0nElOpvaaSlRIlNVmVmIyMQ1Ngw0bJHJk4ULpC9mnD4wfL66RiAgp0WqoJDNzEZmZ/yU7ew0+XxHBwQ4iI/sDYLOF0qfPAostDBwKSgtYvXc1rlQXK3evJK80j5jQGK7oewXOJCeTekwiLCgwL3JGwA2BycmTMsOeNAl69JCFyG+/laYIF10k/SJTUqy2MmAoKztOTs7HOBxzAMjMfJ3c3I106HAT8fFOYmNHtvhsyOrkluTy9u63caW6WL1nNUWeIuIj4rmq31U4k52M6zaOEHvgV0Sslw/8XDE+cMNpOXRIhLpHD2ng26mTLEL+/OdSWxuq+kcaKCk5SlbWEtxuFzk5HwFeLrnkAOHhXSkryyYoKBal6lUto1mRXZTNsp3LcKW6WLt/LaXeUjpEdWBO0hycSU5GJo4kyBaYc9qGysQ0GOpHQYGUX/V6ZSFy+nR49VWJ0d67t6qprxFuQOKOlVJkZS1n27ZZgCY8vDcJCfficDgJC0sEIDi4jaV2BgrH8o+xdOdSFqUu4sMDH+LVXhJiErjj4jtISU7h0s6XYmvCFzkj4AbruPZacYmsXy9NfF99Ffr2rTreo4d1tgUQRUX7Kiv8tW//Izp1up3o6OEkJv4GhyOFyMh+pltNNQ7nHmZx6mJcqS4+SfsEjaZXm17cM/wenMlOBncY3GzGywi4ofF4803429/gww9FsC+/HLKyxD2ilMy+DYDMtA8degK3+y3y87cCEBU1mKAgmVmHhMTTrdvD1hkYYOw/sb8yhX3jkY0A9HP046FRD5GSnEL/tv2bjWhXxwi4oeE4eBCefx7uuUditIOC5OF2S4GoFpAwc7ZI3ZGvKSj4hvbtb0ApxfHjq7DZIujR48/Ex88hPLyr1WYGFDuzduLa4WJR6iK2Ht0KwKAOg5g/bj7OJCd94vtYa2AjYBYxDf7D65WONR07ivtj61YYOhSWLpXZtqEGWmvy8jaVu0cWU1y8D5stnBEjsrDbI/D5Sk1vyGporfnm2DeVKew73DsAGNZ5GM4kJ3OS5tCtdTeLrWwYzCKmoWEoK4PsbCnBmpsL48bBXXfBk09KuF9mphSPMgCgtRetNTZbEIcP/4V9++5GqSBiY8eTkHAf8fFXYLdLwSgj3iLaX6R/gWuHi8U7F7M3ey82ZWNkwkiemfIMs5Nm0zm65VZCNAJuOHcqfNYgGY89esDixdC6tXSuGTxYjillxBspFpWT8xFZWS7c7iX07v0cDoeT+PjZ5Y18ZxIc3NpqMwMGn/bx+XefV4r2oZOHCLIFMa7bOO4Zfg+z+s6ibWRbq80MCIyAG86NJ56At9+Gzz6T7V/9CtpUC1kbM8YSswIRr7eAPXt+TlbWUjyebGy2COLiphESIg0PwsO7ER7ePG/5zxWPz8PHBz/GlSp1R47mHyXEHsKkHpN4dMyjzOgzgzbhJjSyNkbADadn0yZJpvnHP6QwVPv2EupXUiINfq+/3moLAwavt4js7DWUlR2nY8ebsdkiyM//ijZtpuJwpNCmzWTs9joqIrZQSr2lvL//fRbtWMSyXcs4XnSc8KBwLu91Oc4kJ9N6TyM6NPrMb9SCMQJuqElenrhDJkyQTMjsbPjgA0mq6d8fbrxRHgYAPJ48srNX4Xa7OH58JT5fIeHhvenQ4SaUUgwevKVZhq+dL0VlRbyz7x1cqS5W7FrByZKTtAppxYw+M3AmOZnScwoRwRFnfiMDYATcABLWV1IibcQyM+HHP4bnnoPbb4eJE+HwYYnbNgBQVnaCoKAYlLJx4MCDHDnyDMHB7Wjf/obyuiOjK0XbiDfkl+azcvdKXKkuVu1ZRUFZAa3DWjM7aTbOJCcTu08kNCjUajObJEbAWyplZdJ9vbQUevaEa66BF16oCv+78EI5zwg3AKWlbrKylpbXHXmfAQM+IiZmBJ063Y7DkUJMzHBTLKoaOcU5rNi1AleqizV711DiLaFtZFuuu/A6nElOxnQdQ7A92GozmzznLeBKqT7AG9V2dQd+o7V+ur5GGerB2XSYuf56OHZMmh+EhMBLL4l7pIKLLmpcmy3kTB1miou/Y+fOG8jJWQf4CAvrTufOv6xciIyI6ENERPNPGKlORl4GV7uu5o2UN2q0DnMXuFm2S4pFvb//fcp8ZXRq1Ym5g+fiTHJyWcJl2G0t7yLXkE2f6tOVfhcwAEDJ1OMIsMQ/ZhnOi9o9HtPSZHvTJmnsu2yZhPaNGAE5OVV/d/XVlphrNV5vAdu2zcbjya7cl529ki5d7iU4OJ6OHW8mJKQdPl8xiYkPEB/vJCrqohbvFnls3WN8euhTHv34UR4a9RBLdi5h0Y5FfJz2MT7to1tsN+685E6cyU6GdhrapItF1ZdT/STBPyLul0xMpdQk4Lda6xGnO89kYjYwXbvKN6Q2cXHiJlmxQlLaDQDs3/8Ahw//tUaT3gri4+fQv7/LAqsCl/D54RR7ius81ieuDynJKTiTnAxoP6DFX+QqONVPMjFRKk2cLQ2diXk18PopPnguMBcgISHBTx9nqIHW8OWXco9WF9nZ0tHGAEh2X1HRXtLTX6hTvO32aCPetdhzfA+/vPSXvLTlJbKL5I5Foejftj/PTn2WUYmjjGjXQV3iDaf+qZ4r9b63UUqFADOBt+o6rrVeoLUeorUe4jCzP//h81W5QTIz4eKLa/aErI65cJbXHdnC/v0PsGlTXzZt6o3DcTU2W82QNZstnE6d/s8iKwMHrTXbM7fz6MePctGLF9H7b7154tMnsCs7CkWoPRSlFJclXMborqONeJezdy9ccolE3gJ06FD3ef76SfpjBj4V+FJrfcwP72U4G7SW5gcXXACvvSZ1SJYtky42d91V5XAD6Rc5f751tgYAubmb2bHjSoqLDwJ2YmPH0LnzL4iLm4Hb/WaNWbjNFkFi4jzLbLUSrTVfHf2qsizrruO7UCiGdxnOU5OeYk7SHH75zi/pENWBuYPnsmDLAjLyM6w221J27ACXS1ImfvITqeMWGgoejxz/4x9r+sDBzz9JrXW9HsD/gBvP5tzBgwdrw3ny+99rPW1a1fZzz2ntcn3/vNde0zoxUWul5Pm11xrLwoDA5/Po7OwP9e7d/6czMl7VWmtdWnpcf/31NJ2e/g9dUuKucf6JEx/r7duvq3ycOPGxBVZbh9fn1eu/W6/vfudu3e3pbpqH0bZHbHrcq+P0c5ue0+m56VabGFD4fFp/+aXW8+Zp3bev1jKb0vonPzn13/jjJwls1nVoar0WMZVSEcB3QHet9ckznW8WMc+BzZslff3ZZ6WG9rPPSueaV1+V+G1DDbKz38PtfousrCWUlbmx2cLo0uVeunV7xGrTAg6vz8unhz7FlepicepijuQdIdgWzITuE3AmObmi7xXER8RbbWbA4PNJIJfLJUnK+/eDzQajR4PTCbNny8y7ITnVIqapBx4oFBbCqlXyrXA45Nty881SXzspyWrrAg6vt5j8/K3ExFwKwFdfjSQ/fytt2kzD4UghLm4qdnukxVYGDmXeMj46+BGLdixi6a6lZBZkEhYUxuQek3EmOZnRZwaxYbFWmxkweL0i0krBz34mjaSCgqTChNMJV1zRuAFdRsADkZMnJYW9bVv45htJoPn730W4y8rkHDPbrsTrLSA7e0153ZG38fmKGD48k+Dg1hQVHSAkpAN2e5jVZgYMJZ4S1u5fiyvVxbKdyzhRfILI4Eim9Z6GM8nJ5b0uJyokymozA4516+AHP4D334d+/SSAa/dumDFDKiZbgWnoECj4fHJpLy6W2iM//Sn86U+yIPnpp3CpzCiNcNfE7V5Maup1+HxFBAfH07btVcTHO7HbRYBMWVahsKyQ1XtW40p18fbut8krzSMmNIYZfWaQkpTCpB6TCA82FRErKCmB996DRYtg0iT44Q+hd28YNUq82yA/yYqfZaBhBLwx+fGPpdqfyyWlWZ96qmbzgxGnzYNqMZSVHScrazlut4sOHW7C4ZhNZOSFtG//ExwOJzExI7HZzFe3gtyS3BrFooo8RcSFx/GDfj/AmeRkfPfxhNhNd58KCgthzRr5Gb79tjSSio6W2TZIxeQ337TWxrPF/AoakiVL5PHvf8t2v34144luucUauwIQrb1kZLyM272IEyc+BLyEhibi9RYAEBHRk969/2atkQFEdlE2y3ctx5Xq4t1971LqLaV9VHtuHHAjzmQnoxJHEWQucpXk5cHKlSLaq1bJzzAuDlJSxKc9fryE/zU1zP+wP0lPh//+F+64A8LDZfurr+DECXGe3XOP1RYGFMXFhyksTKVNm4mAje+++zMACQn3EB/vpFWrwSZBpBrH8o+xdOdSXKkuPjz4IR6fh4SYBG4fcjvOZCfDuwxv0XVHapOfD1HlLv6rroLVq2V2/aMfiWiPHi0Lk00Zs4hZX1JTIT5elqTffRcmT5bVj3HjqvzdhkqKig7gdrvIynKRm7uBoKDWDB9+DJstmNLSLIKD44xoV+NI7hEWpy7Glerik0Of4NM+erbpiTPJiTPJyZCOQ8x41cGrr8ryUlqa5Ll99pn4tIcNa5oVks0ipr/QWhYgw8OloEFysqRb/epXMHasVP3r2lXONeINSLKYUopDh55k//5fAxAVNYhu3ebjcDix2WTBNiTExB4DHDhxAFeqZENuOCw1bPo5+vHgyAdxJju5oO0FRrSrkZ4u8dkul9zkXn45DB0Kd95ZtRDZXJeXjICfC1rDoEHy7XjpJSlosHBhVSPf4OAq8W7BaK0pKPgGt9uF2+2id+8XiY0dSevWk+jRI4j4+DkmaqQWu7J24Up1sWjHIr46+hUAA9sP5Hdjf4cz2Unf+L4WWxhYHDwogu1ySX4bSLpESUnV6yeftMy8RsMI+Jn485+lQ81//iORIldfLbUgK7jmGstMCzQ8nlzS0h7H7V5EcfE+wEZMzEhApkGtWg2kVauBltoYKGit+Tbz28q6I9vd2wG4tPOl/HHiH5mTNIfurbtbbGVgsXt3lWhv2SL7BgyAxx4Tn3ZLzHczAl6brVvh9dfhiSeq4rULCiQ1y26H++6z2sKAQWsfJ09+jseTQ3z8dGy2cI4efYWoqAEkJNxLfPwsQkLaWm1mwKC1ZnP65kr3yN7svSgUIxNH8tcpf2VO0hw6R3e22syAQWs4ckTSJUBEets2qfb3hz/AnDnSAbAlYxYxS0pk0XHECIiJkZC/226Dr7+WJgiGGvh8Hk6eXIfbvYisrCWUlh4lIqIfQ4duKz9egs3WBOOxGgif9rH+u/WVdUfSTqZhV3bGdRuHM8nJrL6zaBfVzmozA4YKOVJK2pA984xUSw4Ph40bpeZIly7W2mgFZhGzOkVFItyxsRLmN22auEiuu05yaFNSpOajAQCfr6xyoXH37rkcPfoKNlsEbdpMxeFwEhc3rfJcI97g8XlYl7YO1w4XS3YuISM/gxB7CJN6TOLhMQ8zs89M2oS3sdrMgMHnk3T1imJRb74p5e1nzRKxrhD1Sy6x1MyApOUIuNZyWc/Pl+K9v/gFPPKILEiuXi0RJCAZkga83iKys98hK8tFVtYKBg/+goiIXnToMJe4uOm0aTMFu91c5Coo9ZbywYEPcO1wsXTXUrIKswgPCmdqr6k4k5xM7z2d6NBTNNxogXg88MknItpLlkgkSUgITJwoP1MQEb/4YmvtDHRahoDffLNc5v/5T4ns/81vYPhwOWazwZQp1toXQBQVHWT//ns5fnwVPl8BQUGtcThmVx6vqP5ngKKyIt7d9y6uVBcrdq8gpziHViGtmN57Os4kJ1N6TiEyxFRErMDrlbojLhcsXQput7hGpkwR//b06eLFNJw9zVPAV6yAtWvFgQbS16i6r//uu62xKwApK8vh+PEVBAW1Jj5+OkFBMeTmrqddu+twOJzExo6pdJ8YIL80n9V7VrModRErd6+koKyA1mGtuaLPFTiTnEzsMZGwIHMXV0FxMezbV1Vn5IYbJI19+nQR7alTIdJc486b5iHgmZniPLvpJonFTk2Vggfz50OrVhJnZKiktDSLrKylZGW5OHHifbQuIz5+NvHx0wkObs2llx4yiSLVOFl8khW7V+BKdbFm7xqKPcU4Ihxce8G1OJOdjO06lmC7uchVUFxc5Ym84Qbxb6elSRDX2rVS7c94Kv1EXW16zvYBxAKLgJ1AKjDsdOefV0u1U/UjOnhQ6xMn5PXixdLXaN062S4tld5HLZBTtQgrLT1Rec5XX43RH36IXr++u9679x598uQG7fN5LbLYetJz0/WoV0bpjLyMyn3uArd+ecvLeuprU3Xwo8Gah9Gd/txJ/2zVz/RHBz7SHq/HQout41Q/x5wceT17ttYREVqnpcn+zz/Xes0arb0t9+slpKdrPWqU1hkZZz63DmiglmqvAp9orV8u704fobXOOdX55xxGuHBh3R1Bf/c7ad77/PMS8ldUJCnsycnn/W9pDni9Baxfn4DHk125z2aLIDLyQvLzv2T48HSCg+M4eXI9NlsYUVEDzEwbuH3l7by05SWuv/B6Lul0Ca5UFx8d/Aiv9tI1tivOJCcpySkM7TS0RReLquvnGBIiCTSpqVBaKmF+c+ZISru/Oq83C26/XbK3f/pT0a1zxO8deZRS0cDXSD/Ms3qTcxbwrl3l3qs2CQlw//3iQKueFdnC2b//AQ4f/muNLusAQUEOOnf+Pzp1uoPg4DiLrAs8wueHU+wp/t5+heL+y+7HmexkYPuB5iJXzql+jna71B1JSZFQP1MCqBphYVX5/bX3FxWd9ds0hIAPABYAO4CLgC3AnVrrglrnzQXmAiQkJAxOq+sbcCpstpqLj1VvKlElBrTWFBbuwO12cejQH/H58r93TlBQLJdddsIC6wKTfdn7cKW6eH3b62w9urVyf5AtiMk9JvP3GX+nQ6sO1hkYYHz3HRw7JhG35ud4Fhw7JmtvERHw29/Co4/KVc7rlX2zZ0sXrvbtz/otTyXg9blWBgGDgBe01gOBAuDXtU/SWi/QWg/RWg9xnGsX0FPdg7XwezOtNXl5X7F//zw2bUriiy/6c/Dgw0RGXojNVjM222YLp2PH2y2yNHDY4d7BYx8/xoAXB9Dz2Z7c9959BNmCuKTTJSgUYUFh+LSPhJgEI95IXEAFV14pnkrzczwNhw9L1NuoURL1tmSJ7L/9dvEpaS2z7uJiaf9zDuJ9OuoThXIYOKy13li+vYg6BLxezJ9ftw98/ny/fkxTQGsfxcUHCQ+XAkc7dvyQoqK9xMaOpnPnnxMfP5ugoGjWr0+o4UKx2SJITJxnldmWobVm69GtlXVHdmbtBGBElxE8Nekp5iTNITE2kTlvzOG2Ibcxd/BcFmxZQEZ+hsWWW0dqalWxqNRUidNu1QqefhratIEvvjA/x++RlyfZRxvLZbBfP3jooaq00XbtRLxvvVUGb8ECyPDfd6y+i5ifADdrrXcppR4GIrXWp2w7c161UBYulKIIhw7JpX7+fLj22vO2uSmhtZeTJz8tL8u6GK83nxEjMrHZQsjL20JoaOL3amjn5KwjPf3vldsdO95CbOyoxjbdErTWbDqyqVK095/Yj03ZGJ04GmeSk9lJs+nYqqPVZgYMWkvJn+qiDZLj5nRK/lt0reTRFvxzFHbulMEqKRHXCEi7n4sukkHr06dBPtbvPvDyNx0AvAyEAPuBG7XWp3S2BmQxqwAlM3MRe/bcQVlZJjZbGK1bT8bhSKFt2ytNvZFqeH1ePvvuM1w7XCzeuZjDuYcJtgUzvvt4nElOruhzBY7Ic3TdNXPS0+GvfxUd2rdPlppGjxb9mT1bIkkM1dixA/73PxmwHTtk3+TJUoKjkRa4G6SYldZ6K/C9NzWcGz5fCdnZa8nKctGhwy3ExAwnLKwLsbFjcDictGlzOUFBUVabGTCUecv4OO1jFu1YxNKdSzlWcIxQeyiTe07m8XGPM6PPDGLDYq02M2DQWlqKBQfLnb3PJ26RsWOlOvKsWdIR0FCO1rB5MwwcKE0zX3kFnnoKRo6EZ5+Vq1ynTlZbCZhyspbh85Vx/Phy3G4Xx4+/jdebh90eQ69ez9C+/Q1WmxdwlHhKeG//e7hSXSzbtYzsomwigiOY1msaziQnl/e6nFahraw2M2DweGDvXujbV/Soa1dpfrBsmRzPyxP/tqEcnw8+/7yqJOKhQ1K4Zfx4OHpUblPaWlfb3pSTDQA8nlyKiw8QFXURoNi1ay6gcDh+gMPhpHXr8dhsIVabGTAUlhWyZu8aXKku3t79NrkluUSHRjOzz0ycSU4m95hMeHC41WYGDBWl7RctEqG222W9zG6X4lHVy9sb8a7G3r0yuz56VDKTJk2SSqWDB8txP0WMNARGwBuYsrJssrKWk5XlIjv7XUJDO3PJJXux2YIYOPAzwsN7YrOZ/4YK8kryWLlnJa5UF6v2rKKwrJC48DhSklJwJjsZ3208oUFmDaCCoiJYs0YmjitWQG6uiPOMGeLTrrjBHmg62QmlpXKVc7kkCfChh6BbN/FpT54svQFqr9wGMEY5GpC0tN9z8OBDaO0hNDSBTp1ux+FIqTweGWka1QKcKDrB8l3LcaW6eHffu5R4S2gX2Y4fXfQjnElORncdTZC5yNXgm2+kosTKlRLW16aNCLbTCRMmQKi5xtVkzRoJoVmxAk6elKvcT38qx+x2+Ne/LDXvfDG/Cj9RUnIEt3txZRf2yMi+tGo1mM6d78bhcNKq1RCTkl2NzIJMlu5ciivVxQcHPsDj89Alugu3DrkVZ5KT4V2GY7fZrTYzYCguhrfekrojQ4ZAWRl8/LFU+3M6JYok2BRErCI/Hz78UOrWKiXivWqVJNU0o6ucWcSsBx7PSdLT/05Wlovc3A0AREQk07v388TGjrbYusDjSO4RluxcgivVxbq0dfi0jx6te+BMcuJMdnJxx4vNRa4abrespQ0eLALetq3EZj/1lLhGfD6ZPBrKycmRGbbLBe+8I4P27bfQvz9kZUm3iCZ6lTOLmH6isHAXHk8O0dGSaXXgwDwiI/vRrdvviI93GrdILQ7mHMS1QxJr1h9eD0BSfBIPXPYAKckpXNjuQiPa1UhPlyzsRYtg3TqJItm+XbKwv/pK3LUgk0oj3tX46CNZfCwrkxC/uXNlpp2UJMfj40/7500VI+BnQGtNQcE23O5FuN0uCgu3Ex09nEGDPiMoKIZhww4REmK6ildn9/HduHa4WJS6iC8zvgRgQPsBPDb2MZxJTpIcSRZbGFgcPCiRay6XRLKBCPf991ctRCoFPXpYambgkJEhVzmXSxYd77pLblPuvFMGbOjQFlMS0Qh4HWitK2eFO3f+mGPH/g3YiIkZSc+ezxAfX9Uj0oi3jNe2zG2VKezbMrcBcEmnS/jDhD8wJ2kOPdoY9anNqlXSnnXLFtm+6CLJznY6W3xp+7r5298kI/Lzz+Wq1qdPVTxkq1bwxz9aa58FGAEvR2sfubkbcLsXkZW1nEGDNhASEo/DcSUxMcOJj59lxLoaWmu2ZGypdI/syd6DQnFZwmU8Pflp5iTNoUtMF6vNDCiysuC556S6X4VA22zw5JOytlY9TtuAxGdv2ADXXSfby5ZJBtLDD0vxcXOVMwJeXJzGoUN/JCtrCaWl6SgVQuvWE/F4sgkJiSc+frrVJgYMPu1jw+ENlaKddjINu7IztttY7hp2F7P6zqJ9VOAmPTQ2WovfuqxMUti1luJP7duL9lx+uTwM1dixQxYAXC6JlbTbZZDatBEBj4g483u0IFqcgPt8pZw48QFBQbHExFyK1j6OHv0nbdpMxeFwEhc3naCgphPI39B4fB4+SfsEV6qLJTuXkJ6XTrAtmIk9JvKb0b/hij5XEBdhuvxU4PPBpk2iQYsXS6e/CROkma/DIbX+W7e22soAono4zT//KY3JlYIRI+Avf5FbkzZt5Fwj3t+jRQi411vEiRPv4na7yMpajtd7krZtryYm5lLCw7sxYsRx7HaTkl1BqbeUDw98KMWidi0lqzCLsKAwpvacijPJyfTe04kJi7HazIDB64VPP60qo3HkiESrTZgADz4IM2dWnWvEm6qrXMWAPfwwXH+9ZEI+95wUi+pgmmqcDc1WwH0+T2WK+tato8nL+4KgoNbEx88qrzsysfJcI95Q7Cnm3X3v4kp1sXzXcnKKc4gKiWJar2mkJKcwtedUIkMirTYz4Hj+eSmbkZkpoX5TpsDvfy/5I7GxVlsXYJSVwd1317zKjR9fVWukUyfpYGM4a5qVgHs8J8nKWkFWlouTJ9czbFgaNlsoiYkPYrOFERs7FputaQbyNwQFpQWs2rMKV6qLlXtWkl+aT2xYbGWxqEk9JhEWFGa1mQHFrl3wxBPw+ONSNzs2FsaMkTW1qVMhylT9raKsTOKz09IkAyk4WBYlhwyRQZwxw1zl6kmzyMTMzd3EwYOPcOLEWrQuIySkEw7HHLp2fZjg4DZ+/7ymzMnik7y9+21cqS5W711NsaeY+Ih4ZvWZRUpyCmO7jSXEbioiVlBQIGU0unSR8OLUVBg2TNLaJ04889+3OEpKxOHvcsHy5ZCdLTPsw4fFz+3ztZgYbX/SIJmYSqmDQB7gBTx1fUB9qatFWHh4b7KylhAdPZRWrQajtY/Cwh106vRzHI4UoqOHolTL/ZJk5GVwtetq3kh5g/ZR7TleeJxlu5bhSnXx3v73KPWW0iGqAzcNvAlnkpORiSNbbLGoulqEzZgBb78tGrR6tVT8u/lmEfCkJElxb6IZ2f4hIwOuvhreeEPEubBQyrAGBUkg++OPS9r6zJkS1D5pUlXaqBFvv1LflmoHgSFa66yzOf9cZ+BebwHr1yfg8WRX+8wgtPYAkJj4IN26PUbFv8GkZAu3r7ydFze/yGUJlxFiD+Gjgx/h1V4SYxIr645c2vlSbC34Igci3rWb9FbojNcr62izZ4t7ZORI0ScD4qd+6SUYN06EevVqyYycNElit/fsEd92iLmT8xdNshZKWtp8fL7iGvu09hIdPZLevZ8nMrIfYIS7grDfhVHiLanc/uTQJwDYlZ0vbvmCwR0Gm7Gqxrx5NcUbRLhbtRJNGjbMTBhrEB4uBaIqeO89ebbbq1qM9expMpIakfp+PTXwrlJqi1Jqbl0nKKXmKqU2K6U2u93uc3rz9PQX8Plq/cLQFBZ+S1RUfyNGwL7sffzhsz9wycuXVIq3QsYlzB7GNRdcw+G7DjOkoylnW0FF3aNDh+o+np8vYchGvJHA9ZdeggULYP9++OEPqwYmNBSuuUb82/36WWtnC6W+X9ERWutBwFTgDqXUqNonaK0XaK2HaK2HOM6xc2rHjrdhs9UM3rfZwunYsWWHGqW6U/ndut8x4MUB9Hy2J/e9dx8en4fHxz3O1f2vRilFWFAYpb5SYkJjWnx25IED8Kc/wddfy7bWUvXvVJ2yEhIaz7aA5MgRad47ZoyE2tx6K7z+uviUYsrj/8PCJMokJiagW441d+ol4Frr9PLnTGAJMNQfRlWQmDgPm61mGJvNFkFi4jx/fkzAo7Vm69GtPPTBQyQ/l0zy88k89OFDRARH8KeJf2L/z/ezZe4W7h95PyWeEm4dfCsbbtrArYNv5Wj+UavNt4SdO2VBctAg6N4d7rlHgiNAdGnbNql9VDu5LyJC/q7FcehQVf+1efPg5z+X1dp582DrVvjgAzl27JgI+oYN8ny0ZX6/AoXzXsRUSkUCNq11XvnrtcCjWus1p/qb8wkjrCsKJTb2exP9ZofWmk1HNuFKdbE4dTH7TuzDpmyMShyFM8nJ7L6z6RTdyWozAwatpXZ/RRmNHTtk/7BhEggxZ05VLe3q1BWFcu21jWu7ZezaJYPlcsGXX8otyoUXwu7dEu7X19S2DxROtYhZHwHvjsy6QRZD/6u1Pu3cpbl15PE3Xp+Xz7/7vFK0v8v9jiBbEOO6jcOZ5GRW31m0jWxrtZkBydix4tu22SRiJCVFIkg6mWvc99mxA666Sm5DAC69VK5yN9wgbX8MAYffo1C01vuBi+pllQGPz8NHBz/CtUOKRR0rOEaoPZRJPSbx2NjHmNFnBm3CTTJSbf79b0lj//xzEe2rrpL1tVmzjAbVQGvYvFlm2b16SbGohATxW99yi9yadO5stZWG8ySgwwibKyWeEt4/8D6uHS6W7VrG8aLjRARHcHmvy3EmOZnWaxqtQltZbWbA4PFIA1+XS3zZ3bqJr9rhkES/+HhxxxqqsWEDvPmm1B1JS5NQv4o6I1FRVQsChiaNEfBGoqisiDV71+BKdbFi9wpyS3JpFdKKGX1m4ExyMqXnFCKCTbnMCkpL4f33RbSXLoXjx0W0J00SAU9JkYehHI9HfNiDB8v2ww9LV/aJE+G3v5WsyDhT9re5YQS8AckryatRLKqwrJA24W0kGzLJyYTuEwgNCrXazIChqEiaibtc0lz85ElJqpkxQ1y0U6aYktA1KC2V6JDqV7mK+MjnnpNbkxhT9rc5YwTcz5woOsGK3Stwpbp4Z+87lHhLaBvZlhsuvAFnspPRiaMJtrfkQho1qaht5PXKzLqi4cHs2SLaEydKvoihFu++K47/nBy5yk2fLgNWUd3PdEBuERgB9wPuAjdLdy7Fleri/QPv4/F56BzdmZ8O/inOZCcjuozAbrNbbWbA8eCD4iZZv15ctI89Bl27Spx2iy4WVZv8fMntd7lklfbqq6Un2xVXVF3lwkzZ35aIEfDzJD0vnSWpS3Cluvg47WN82kf31t355aW/xJnk5OJOF7f4YlHVycqSu/ylS+G112Si2LMn5OZKQl9wsARFGMrRWoLUXS6pZ1tcLKu2o8pzIDp3hn/9y1ITDdbTLOqBNxZpOWm4UqWh7/rv1qPR9I3vS0pSCs5kJxe1u8jUG6lGRoYUqXO5JEbb5xM3yaJFkiFpqEVWlixEjh8v2wMGSDbknDky0x45sqpcoqFF0SSrEQYCe47vwZXqYtGORWzJ2ALARe0u4pExj+BMdpLsSLbYwsDi0KGq5L7PP5eJZJ8+cP/9okEDBkjPWkM5R4/WvMqFhopoR0SI26RdO1NVy3BKjIDXQmvNdvd2XDtkpv1t5rcADO00lCcnPMmcpDn0bGPKZVanqEgqjR4/LjNsn08ysh9+WEQ7OdmIdp28+KLEZldc5e67TwYsvLxHq2nsazgDRsAR0f4y48tK98ju47tRKEYkjOAvk//CnKQ5JMS09BJ1dTN7tgj4mjUSZvzKK1J/pFcvqy0LMPbtq7o1eeQRiYkcOdJc5Qz1osUKuE/72Hh4Y6VoH8w5iF3ZGdN1DL+45BfM6juLDq3MDKgCreGrr0R/PvgA1q2ThcepUyUcuYIbbrDOxoCjqAj+/GcZtK1bZd+QIVVV//r1M3W0DfWiRQm41+flk0OfVNYdOZJ3hGBbMBO6T+ChUQ8xs89M4iPirTYzYPD5YNOmqonjgQPijh09GjIzpVDU3DrbeLRQtBahPnJE4rJDQ8VNkpgoQj5njsRJGgx+otkLeJm3jA8Pflgp2u5CN2FBYUzpOYXfJ/2e6b2nExsWa7WZAYPXC59+KoK9eLFoUXCwBEY88ICEHp9jX47mjdY1r3L790uxqGnT5Gq3axdERlptpaGZ0iwFvNhTzNp9a3Gluli+azknik8QFRLFtF7TcCY5mdprKlEhUVabGTCUlUk8dlycVBodM0Ymj1OmwBNPSCp7RYKfAbnK2Wzis77rLnj6ael4PH48/PrXkmxT4c824m1oQJqNgBeUFrBm7xoWpS5i5e6V5JXmERMaw8w+M0lJTmFi94mEB4dbbWbAoLVojNayfjZihOSF9O8vyTbjxkmGtqEcj0fC/FwuCftbs0ZiIq+9FgYOlKtc69ZWW2loYdRbwJVSdmAzcERrPb3+Jn2fjLwMrnZdzRspb9To75hbksvbu9/Glepi9Z7VFHmKiI+I56p+V+FMdjKu2zhC7CENYVLAcroOM4WFojsuF+zZAxs3iojfc09VH0ilxE3SosjIkPT0N974fn/Ho0fFd7RsmdSujYioco+ALEoO+V5+hcHQKPhjBn4nkApE++G96uSxdY/x6aFPefTjR/nduN+xbOcyXKku1u5fS6m3lA5RHfjJwJ/gTHIyMnEkQbZmc2NxTixcKIuKhYWynZYm6emffy5FolavlmNxcXKXX1wsIcctfiHyscfE8f/oo9L9+J13JONx5ky5DVmzRsJtUlJg8uSqOG2DwWLqlUqvlOoMvArMB+460wz8XFPpw+eHU+wprvNYQkwCziQnKckpXNr5UlN3BAlwSEur+1j79lUV/kaPFpdtiyc8XK5idTF2bFUj34qSiQaDRTRUKv3TwL3AKb2lSqm5wFyAhIRzS4bZ//P9/OrdX/HG9jfwai8KRZIjiT9P+jOTe0w2dUeqkZkpbpO6UAoOHzZlNGqQny8RI7/6lbhOvF7Z36uX+J1mz64614i3IUA572+mUmo6kKm13nK687TWC7TWQ7TWQxznGH/WoVUHokOj8WkfIfYQlFKMThzNlJ5TjHhTlUCTny8+7FMtOiYkGPEGpMbI3/8u4TVxcTKzjo6W55AQEeoJE+DKK80tiqFJUJ+pxQhgplLqIPA/YJxS6jW/WFWNYwXHuG3IbWy6eRO3Dr6Vo/lH/f0RTYqDByUnZPhwaS8G0uLwxRdl8bJ2x5qICJlQtmi+/VZcIu3bi8N/7174xS9EsI8dg9tuk1juW2+VRUuDoYngl3KySqkxwK/87QM3CLt2VeWJfPml7Bs4EH7wA6l/VP1m5HRRKC2GtDQZrD59JGIkPV2udrNny0LkhReauiOGJoUpJ9vE2LcP/vMf0aFt22TfJZfAH/8oGdndu9f9d9de2wIFGyQu0uWSYuNbyr16d9whAt6xY9UgGgzNCL8IuNb6I+Ajf7xXS0Vr0Z3ERElV/+wziWobORL++lcR7c6drbYygNBaVma7dJHtq66SaltDh8KTT0q4jekLaWjmmBm4hfh80nm9dWsJiLj4YvjLX8Q963RKyHG7dlZbGUBoLT6kipn24cOyMBkZCS+9JD7uCkE3GFoARsAbGY8HPvmkqljUyJESxdajh2jSmDFyXmSkKaNRgzVrZLHx4EEJqRk7VuqQVHDxxZaZZjBYhRHwRqC0VHJCXC6pM5KVJTkkU6fKQmQFTqdlJgYWXm/VVW7OHBHrjh2ldvZvfiMZknFxVltpMFiOEfAG5JNP4OWXYflyyMmRcL/p0yUQYsoUM8Ougc8H770ntyFLl4prJCxMIknGjpXIkbffttpKgyGgMALuRwoKYNUq8V1HR8OGDbBihRSHcjph4kTRJEM5xcUSPXLBBbL9k5/IosC0aXKVmzrVXOUMhtPglzjws6U5xoGfPCmds9q3l3pII0fC//4nQREFBZLgFxxstZUBREGBVNVatAhWrpTbkiNHJKlm2zbo2dNc5QyGWpwqDtwUeTgPjh+Hf/5TJooOBzz+uOwfPlxKRqekyHZkpBHvGrzwggzYlVfC++9LCddXXqnqEdm/vxFvg+EcMC6Us+ToUanj73KJSHu9Uv3v5z+HH/5QzqnoF2lArnLLl8uAPfaYpI4mJ4ubxOmUWxVTb8RgqBfmF3QGXnsNFiwQ94jWsqZ2332iQQMHmozsGhQWSvrookXw4YdylUtMlIYJAwfK1c1c4QwGv2EEvBaHD8Nbb8nM2m6X5L6cHPjtb8U1kpxsRLsGhw9LrZGhQ6t6RHbqBPfeK1e5QYPMgBkMDYRZxEQa+bZpIwuRixaJi3bjRtGksjLjx/4e+/dXVdfauFFC/L7+Wo59953k/BvRNhj8hlnErIbWsHUrPPSQzKj79ZNFSZCFyQMHRLzBiPf3uPNOSRu99165uj3+uNyyVNClixFvg6GRaDEuFK2l5HPFxHH//qpFxzvukIQ/kAzJrl0tNTUw0Bq++aYq53/NGplZT5kidWrnzIFu3ay20mBo0bQIAX/oIfjXv8RdGxQE48fD/fdLgs05Nglq/hw9KhW1XC6paWuzwahRElXSubMk10ydarWVBoOBZirgmzdLBuQjj8h2erqspc2fDzNmSPU/Qzk+n7Stt9th2DDZ99e/SlWt++6Tq1zbtpaaaDAY6qZZCHhJiZTRGDZMFiM3bIA//Uk6ZHXoIPVIjFu2Gh4PfPyxzLKXLJFZ9+WXS2Zk+/ZSbSsqymorDQbDGahPU+MwpdQmpdTXSqntSqlH/GlYBQsXik/aZpPnhQtlf2GhaM+118oEcfp0WLZMjt14o9RC6tChwtaGsCyAycgQ5371/o4+X9XrqVOlee+rr8Jll8Hrr0v+fwVGvA2GJsF5hxEqaQsfqbXOV0oFA58Cd2qtN5zqb841jHDhQulBW1hYtS8kBAYMkLIZhYUy464oFjVhAoSGntc/p3lx++3S4OCmm0SsXS65Rdm7V8R5xQqJIJky5ftdkA0GQ8Dh956YWpQ/v3wzuPzh16DyefNqijdIbe3Nm0XYnU6ZaJpQv3LCw6XCXwV//7s8AH78Y8jNFQGfMcMS8wwGg3+pVyKPUsoObAF6As9pre+r45y5wFyAhISEwWlpaWf9/jZbVZ2jmu9Z0yPQ4snJkVl1XJzctixeLEJut8sV7l//Mq3GDIYmTIMk8mitvVrrAUBnYKhSqn8d5yzQWg/RWg9xnGPMXkLCue1vUWRlyers1KmyCHDDDRKrHR0ttylhYVXFW4x4GwzNEr9kYmqtc5Cu9FP88X4VzJ//fRdtRITsb5EUFMiz1pIqesstsHu3dEHesAGefhqOHZPwmw0b5Ln6QqbBYGhWnLcPXCnlAMq01jlKqXBgAvCk3yxDIkxAfOGHDsnMe/78qv0tgrQ0cYlUpI9+9524Rv72NwmzGTCgZpjN4sVVr597rtHNNRgMjUd94sA7AK+W+8FtwJtaa783Lbz22hYm2BW88w48+KCs2IIUjLrtNgl6j4iQuG2DwdCiqU8UyjfAQD/a0nLRWkoiulwwa5aItc0mM+vf/17CbXr2tNpKg8EQYDSLTMwmidZSbLyiutauXSLYcXEi4BMnysNgMBhOgRHwxsTnk0XGDh3EFTJ6tHREHjNGyrTOmlWVPmowGAxnwAh4Q+P1Sj+2irKsrVvDt99KmN+yZTLbjo+32kqDwdAEMQLekLz4ovRiy8wUwZ4yRfzZWou7ZNw4qy00GAxNGCPg/qK4GNaulZn2I49IM9+4OHGPOJ0SNWKKRBkMBj9iBLw+FBdLCVaXC95+G/LyICYGrr5aBPzKK+VhMBgMDYAR8HMlN1dq1fboIa9/8AMpiXjVVTLTHjdOSiYaDAZDA2ME/GzIzobly2Wm/e670mJs7VqpQbJpE1x0kfRqMxgMhkbEqM6ZuOsuePZZ6WKTkCAdkKu7RQYPts42g8HQojECXp3DhyXUb/lyeY6OljC/u+8W98iQIS2wvY/BYAhUjIC73dJazOWSCn4AyclSROqCC6QRgsFgMAQgfikn2+TYuVMeIDHa99wjNbTnz4fUVNi+XcTbYDAYApiWMQPXWrIfK+qObN8O110H//lP1WzbdIkwGAxNjJYh4JMmSVNfmw1GjoRnnoHZs+WYUka8DQZDk6R5CbjPB+vXyyz7k0/Ep223S4x2SooUi2rXzmorDQaDwS80DwFPTZUONUuWQEaGJNJMmgTHj0us9s03W22hwWAw+J3zXsRUSnVRSn2olEpVSm1XSt3pT8NqkJEhpVcr+juWlsLq1dIPEiA9HV55BYYNk67sbrd0aW/btsFMMhgMBqupzwzcA9yttf5SKdUK2KKUWqu13uEn26p47DEpyXrjjeBwSJz2yZNw333SsWbMGBHtyEi/f7TBYDAEKvVpqZYBZJS/zlNKpQKdAP8JeHi4FIyqYM0aebbbZYY9YULVthFvg8HQwvBLHLhSqivSH3NjHcfmKqU2K6U2u93uc3vj/fvhmmukiS9AaCj88IeSMTl9utTYNhgMhhZKvQVcKRUFuIBfaK1zax/XWi/QWg/RWg9xOBzn9uYdOkg6e3GxiHVZGcTGQvv29TXbYDAYmjz1EnClVDAi3gu11ov9Y1Itjh2DW2+VkMBbb61ayDQYDIYWznn7wJVSCvgHkKq1fsp/JtVicbXrwnPPNdjHGAwGQ1OjPjPwEcD1wDil1Nbyx+V+sstgMBgMZ6A+USifAqa2qsFgMFhEy6xGaDAYDM0AI+AGg8HQRDECbjAYDE0UI+AGg8HQRFFa68b7MKXcQNp5/nk8kOVHc/yFsevcMHadG8aucyNQ7YL62Zaotf5eJmSjCnh9UEpt1loPsdqO2hi7zg1j17lh7Do3AtUuaBjbjAvFYDAYmihGwA0Gg6GJ0pQEfIHVBpwCY9e5Yew6N4xd50ag2gUNYFuT8YEbDAaDoSZNaQZuMBgMhmoYATcYDIYmSkAJuFLqn0qpTKXUtlMcV0qpZ5RSe5VS3yilBgWIXWOUUierVWX8TSPZdcbG0laM2Vna1ehjppQKU0ptUkp9XW7XI3WcY8V4nY1dlnzHyj/brpT6Sin1dh3HLPlNnoVdVv0mDyqlvi3/zM11HPfveGmtA+YBjAIGAdtOcfxyYDVSBfFSYGOA2DUGeNuC8eoADCp/3QrYDSRbPWZnaVejj1n5GESVvw5GWgBeGgDjdTZ2WfIdK//su4D/1vX5Vv0mz8Iuq36TB4H40xz363gF1Axca70OyD7NKVcA/9bCBiBWKdUhAOyyBK11htb6y/LXeUBFY+nqNPqYnaVdjU75GOSXbwaXP2qv4lsxXmdjlyUopToD04CXT3GKJb/Js7ArUPHreAWUgJ8FnYDvqm0fJgCEoZxh5bfAq5VS/Rr7w9WpG0tbOmansQssGLPy2+6tQCawVmsdEON1FnaBNd+xp4F7Ad8pjlv1/Xqa09sF1oyXBt5VSm1RSs2t47hfx6upCXhdDSQCYabyJVKr4CLgWWBpY364On1jacvG7Ax2WTJmWmuv1noA0BkYqpTqX+sUS8brLOxq9PFSSk0HMrXWW053Wh37GnS8ztIuq36TI7TWg4CpwB1KqVG1jvt1vJqagB8GulTb7gykW2RLJVrr3IpbYK31KiBYKRXfGJ+tztxY2pIxO5NdVo5Z+WfmAB8BU2odsvQ7diq7LBqvEcBMpdRB4H9I+8TXap1jxXid0S6rvl9a6/Ty50xgCTC01il+Ha+mJuDLgRvKV3IvBU5qrTOsNkop1V4ppcpfD0XG9XgjfO7ZNJZu9DE7G7usGDOllEMpFVv+OhyYAOysdZoV43VGu6wYL631/VrrzlrrrsDVwAda6+tqndbo43U2dln0/YpUSrWqeA1MAmpHrvl1vM67J2ZDoJR6HVk9jldKHQZ+iyzooLV+EViFrOLuBQqBGwPErhTgNqWUBygCrtblS84NTEVj6W/L/acADwAJ1WyzYszOxi4rxqwD8KpSyo78oN/UWr+tlLq1ml1WjNfZ2GXVd+x7BMB4nY1dVoxXO2BJ+XUjCPiv1npNQ46XSaU3GAyGJkpTc6EYDAaDoRwj4AaDwdBEMQJuMBgMTRQj4AaDwdBEMQJuMBgMTRQj4AaDwdBEMQJuMBgMTZT/B6Og1PZ2qukSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "x=np.arange(1,6)\n",
    "print(x)\n",
    "plt.plot(x,x+1,marker='*',color='r',linestyle='--')\n",
    "plt.plot(x,x+2,marker='o',c='b',ls='-.')\n",
    "plt.plot(x,x+3,marker='*',c='g',ls='-')\n",
    "plt.plot(x,x+4,marker='H',c='y',ls='--')\n",
    "plt.plot(x,x+5,marker='o',c='r',ls=':')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "y1=[4,3,6,2,5]\n",
    "plt.plot(x,y,marker='*',markersize=20,c='r',ls='-.',label=\"line1\")\n",
    "plt.plot(x,y1,marker='o',markersize=20,c='b',ls=':',label=\"line2\")\n",
    "plt.xlabel(\"X axis\",fontsize=20,fontstyle='italic')\n",
    "plt.ylabel(\"Y axis\",fontsize=20,fontstyle='italic')\n",
    "plt.title(\"graph Title\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "y1=[4,3,6,2,5]\n",
    "plt.plot(x,y,marker='*',markersize=20,c='r',ls='-.',label=\"line1\")\n",
    "plt.plot(x,y1,marker='o',markersize=20,c='b',ls=':',label=\"line2\")\n",
    "plt.xlabel(\"X axis\",fontsize=20,fontstyle='italic')\n",
    "plt.ylabel(\"Y axis\",fontsize=20,fontstyle='italic')\n",
    "plt.title(\"graph Title\")\n",
    "plt.ylim(1,10)\n",
    "plt.xlim(0,10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,5,7,9]\n",
    "y=[5,7,9,5,8]\n",
    "y1=[4,3,6,2,5]\n",
    "plt.plot(x,y,marker='*',markersize=20,c='r',ls='-.',label=\"line1\")\n",
    "plt.plot(x,y1,marker='o',markersize=20,c='b',ls=':',label=\"line2\")\n",
    "plt.xlabel(\"X axis\",fontsize=20,fontstyle='italic')\n",
    "plt.ylabel(\"Y axis\",fontsize=20,fontstyle='italic')\n",
    "plt.title(\"graph Title\")\n",
    "plt.ylim(1,10)\n",
    "plt.xlim(0,10)\n",
    "plt.grid()\n",
    "plt.legend(bbox_to_anchor=(1.2,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to draw pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "plt.pie(profit,labels=company)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "plt.pie(profit,labels=company,startangle=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "myc=[\"r\",\"b\",\"y\",\"g\"]\n",
    "plt.pie(profit,labels=company,colors=myc)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "myc=[\"r\",\"b\",\"y\",\"g\"]\n",
    "plt.pie(profit,labels=company,colors=myc,autopct=\"%0.2f%%\",explode=[.2,0,0,0])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "plt.bar(x=company,height=profit,label=\"profit\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"company\")\n",
    "plt.ylabel(\"profit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# bar chart to compare different attributes of companies--using --xticks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit=[10,25,35,15]\n",
    "revenue=[100,200,150,50]\n",
    "company=(['TCS','WIPRO','INFOYSYS','CAPGEMINI'])\n",
    "pos=np.arange(len(company))\n",
    "print(pos)\n",
    "plt.xticks(pos,company)\n",
    "plt.bar(pos-0.2,height=profit,width=0.5,label=\"Profit\")\n",
    "plt.bar(pos+0.2,height=revenue,width=0.5,label=\"Revenue\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"company\")\n",
    "plt.ylabel(\"profit\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scatter plot is used to find correlation between -1 and  +1\n",
    "#-1 is perfectly negative correlated\n",
    "#+1 is perfectly positive correlated\n",
    "#0 means there is no correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[1,3,4,5,7,9]\n",
    "y=[5,7,7,8,5,8]\n",
    "plt.scatter(x,y)\n",
    "xlabel=(\"sunshine in hrs\")\n",
    "ylabel=(\"attendence in thousands\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# box plot means there are three quartile:\n",
    "q2 means median values of the series, \n",
    "q1 means median values of first half of the series, \n",
    "q3 means median of second half of the series, \n",
    "box indicates IQr Inter Quartile Range=q3-q1, \n",
    "Outlier means something that lies outside the range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp=np.array([2,3,4,4,5,6,7,8,9])\n",
    "plt.boxplot(bp)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# outlier means values outside the range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp=np.array([-5,-4,-6,-7,-4,9])\n",
    "plt.boxplot(bp)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# voilin plot is used to show only density ut to that outlier not outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp=np.array([-5,2,3,4,4,5,6,7,8,9])\n",
    "plt.violinplot(bp)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('C:/Users/Pratik Bharadwaj/Desktop/company_sales_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_number</th>\n",
       "      <th>facecream</th>\n",
       "      <th>facewash</th>\n",
       "      <th>toothpaste</th>\n",
       "      <th>bathingsoap</th>\n",
       "      <th>shampoo</th>\n",
       "      <th>moisturizer</th>\n",
       "      <th>total_units</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2500</td>\n",
       "      <td>1500</td>\n",
       "      <td>5200</td>\n",
       "      <td>9200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1500</td>\n",
       "      <td>21100</td>\n",
       "      <td>211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2630</td>\n",
       "      <td>1200</td>\n",
       "      <td>5100</td>\n",
       "      <td>6100</td>\n",
       "      <td>2100</td>\n",
       "      <td>1200</td>\n",
       "      <td>18330</td>\n",
       "      <td>183300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2140</td>\n",
       "      <td>1340</td>\n",
       "      <td>4550</td>\n",
       "      <td>9550</td>\n",
       "      <td>3550</td>\n",
       "      <td>1340</td>\n",
       "      <td>22470</td>\n",
       "      <td>224700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3400</td>\n",
       "      <td>1130</td>\n",
       "      <td>5870</td>\n",
       "      <td>8870</td>\n",
       "      <td>1870</td>\n",
       "      <td>1130</td>\n",
       "      <td>22270</td>\n",
       "      <td>222700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3600</td>\n",
       "      <td>1740</td>\n",
       "      <td>4560</td>\n",
       "      <td>7760</td>\n",
       "      <td>1560</td>\n",
       "      <td>1740</td>\n",
       "      <td>20960</td>\n",
       "      <td>209600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2760</td>\n",
       "      <td>1555</td>\n",
       "      <td>4890</td>\n",
       "      <td>7490</td>\n",
       "      <td>1890</td>\n",
       "      <td>1555</td>\n",
       "      <td>20140</td>\n",
       "      <td>201400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2980</td>\n",
       "      <td>1120</td>\n",
       "      <td>4780</td>\n",
       "      <td>8980</td>\n",
       "      <td>1780</td>\n",
       "      <td>1120</td>\n",
       "      <td>29550</td>\n",
       "      <td>295500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3700</td>\n",
       "      <td>1400</td>\n",
       "      <td>5860</td>\n",
       "      <td>9960</td>\n",
       "      <td>2860</td>\n",
       "      <td>1400</td>\n",
       "      <td>36140</td>\n",
       "      <td>361400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3540</td>\n",
       "      <td>1780</td>\n",
       "      <td>6100</td>\n",
       "      <td>8100</td>\n",
       "      <td>2100</td>\n",
       "      <td>1780</td>\n",
       "      <td>23400</td>\n",
       "      <td>234000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1990</td>\n",
       "      <td>1890</td>\n",
       "      <td>8300</td>\n",
       "      <td>10300</td>\n",
       "      <td>2300</td>\n",
       "      <td>1890</td>\n",
       "      <td>26670</td>\n",
       "      <td>266700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2340</td>\n",
       "      <td>2100</td>\n",
       "      <td>7300</td>\n",
       "      <td>13300</td>\n",
       "      <td>2400</td>\n",
       "      <td>2100</td>\n",
       "      <td>41280</td>\n",
       "      <td>412800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2900</td>\n",
       "      <td>1760</td>\n",
       "      <td>7400</td>\n",
       "      <td>14400</td>\n",
       "      <td>1800</td>\n",
       "      <td>1760</td>\n",
       "      <td>30020</td>\n",
       "      <td>300200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month_number  facecream  facewash  toothpaste  bathingsoap  shampoo  \\\n",
       "0              1       2500      1500        5200         9200     1200   \n",
       "1              2       2630      1200        5100         6100     2100   \n",
       "2              3       2140      1340        4550         9550     3550   \n",
       "3              4       3400      1130        5870         8870     1870   \n",
       "4              5       3600      1740        4560         7760     1560   \n",
       "5              6       2760      1555        4890         7490     1890   \n",
       "6              7       2980      1120        4780         8980     1780   \n",
       "7              8       3700      1400        5860         9960     2860   \n",
       "8              9       3540      1780        6100         8100     2100   \n",
       "9             10       1990      1890        8300        10300     2300   \n",
       "10            11       2340      2100        7300        13300     2400   \n",
       "11            12       2900      1760        7400        14400     1800   \n",
       "\n",
       "    moisturizer  total_units  total_profit  \n",
       "0          1500        21100        211000  \n",
       "1          1200        18330        183300  \n",
       "2          1340        22470        224700  \n",
       "3          1130        22270        222700  \n",
       "4          1740        20960        209600  \n",
       "5          1555        20140        201400  \n",
       "6          1120        29550        295500  \n",
       "7          1400        36140        361400  \n",
       "8          1780        23400        234000  \n",
       "9          1890        26670        266700  \n",
       "10         2100        41280        412800  \n",
       "11         1760        30020        300200  "
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# to read facewash and facecream from dataset and represent it with the help of BAR CHART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn=df['month_number']\n",
    "fc=df['facecream']\n",
    "fw=df['facewash']\n",
    "plt.bar([a-0.25 for a in mn],fc,width=0.25,label='Facecream',align='edge')\n",
    "plt.bar([a+0.0 for a in mn],fc,width=0.25,label='Facewash',align='edge')\n",
    "plt.xlabel('Month number')\n",
    "plt.ylabel('Sales in number')\n",
    "plt.legend()\n",
    "plt.title('Sales data')\n",
    "plt.xticks(mn)\n",
    "plt.grid()\n",
    "plt.savefig('C:/Users/Pratik Bharadwaj/Desktop/abc.png',dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# read total profit for each month and represent it with the help of HISTOGRAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month_number</th>\n",
       "      <th>facecream</th>\n",
       "      <th>facewash</th>\n",
       "      <th>toothpaste</th>\n",
       "      <th>bathingsoap</th>\n",
       "      <th>shampoo</th>\n",
       "      <th>moisturizer</th>\n",
       "      <th>total_units</th>\n",
       "      <th>total_profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2500</td>\n",
       "      <td>1500</td>\n",
       "      <td>5200</td>\n",
       "      <td>9200</td>\n",
       "      <td>1200</td>\n",
       "      <td>1500</td>\n",
       "      <td>21100</td>\n",
       "      <td>211000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2630</td>\n",
       "      <td>1200</td>\n",
       "      <td>5100</td>\n",
       "      <td>6100</td>\n",
       "      <td>2100</td>\n",
       "      <td>1200</td>\n",
       "      <td>18330</td>\n",
       "      <td>183300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2140</td>\n",
       "      <td>1340</td>\n",
       "      <td>4550</td>\n",
       "      <td>9550</td>\n",
       "      <td>3550</td>\n",
       "      <td>1340</td>\n",
       "      <td>22470</td>\n",
       "      <td>224700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3400</td>\n",
       "      <td>1130</td>\n",
       "      <td>5870</td>\n",
       "      <td>8870</td>\n",
       "      <td>1870</td>\n",
       "      <td>1130</td>\n",
       "      <td>22270</td>\n",
       "      <td>222700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3600</td>\n",
       "      <td>1740</td>\n",
       "      <td>4560</td>\n",
       "      <td>7760</td>\n",
       "      <td>1560</td>\n",
       "      <td>1740</td>\n",
       "      <td>20960</td>\n",
       "      <td>209600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2760</td>\n",
       "      <td>1555</td>\n",
       "      <td>4890</td>\n",
       "      <td>7490</td>\n",
       "      <td>1890</td>\n",
       "      <td>1555</td>\n",
       "      <td>20140</td>\n",
       "      <td>201400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2980</td>\n",
       "      <td>1120</td>\n",
       "      <td>4780</td>\n",
       "      <td>8980</td>\n",
       "      <td>1780</td>\n",
       "      <td>1120</td>\n",
       "      <td>29550</td>\n",
       "      <td>295500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3700</td>\n",
       "      <td>1400</td>\n",
       "      <td>5860</td>\n",
       "      <td>9960</td>\n",
       "      <td>2860</td>\n",
       "      <td>1400</td>\n",
       "      <td>36140</td>\n",
       "      <td>361400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3540</td>\n",
       "      <td>1780</td>\n",
       "      <td>6100</td>\n",
       "      <td>8100</td>\n",
       "      <td>2100</td>\n",
       "      <td>1780</td>\n",
       "      <td>23400</td>\n",
       "      <td>234000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1990</td>\n",
       "      <td>1890</td>\n",
       "      <td>8300</td>\n",
       "      <td>10300</td>\n",
       "      <td>2300</td>\n",
       "      <td>1890</td>\n",
       "      <td>26670</td>\n",
       "      <td>266700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2340</td>\n",
       "      <td>2100</td>\n",
       "      <td>7300</td>\n",
       "      <td>13300</td>\n",
       "      <td>2400</td>\n",
       "      <td>2100</td>\n",
       "      <td>41280</td>\n",
       "      <td>412800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2900</td>\n",
       "      <td>1760</td>\n",
       "      <td>7400</td>\n",
       "      <td>14400</td>\n",
       "      <td>1800</td>\n",
       "      <td>1760</td>\n",
       "      <td>30020</td>\n",
       "      <td>300200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    month_number  facecream  facewash  toothpaste  bathingsoap  shampoo  \\\n",
       "0              1       2500      1500        5200         9200     1200   \n",
       "1              2       2630      1200        5100         6100     2100   \n",
       "2              3       2140      1340        4550         9550     3550   \n",
       "3              4       3400      1130        5870         8870     1870   \n",
       "4              5       3600      1740        4560         7760     1560   \n",
       "5              6       2760      1555        4890         7490     1890   \n",
       "6              7       2980      1120        4780         8980     1780   \n",
       "7              8       3700      1400        5860         9960     2860   \n",
       "8              9       3540      1780        6100         8100     2100   \n",
       "9             10       1990      1890        8300        10300     2300   \n",
       "10            11       2340      2100        7300        13300     2400   \n",
       "11            12       2900      1760        7400        14400     1800   \n",
       "\n",
       "    moisturizer  total_units  total_profit  \n",
       "0          1500        21100        211000  \n",
       "1          1200        18330        183300  \n",
       "2          1340        22470        224700  \n",
       "3          1130        22270        222700  \n",
       "4          1740        20960        209600  \n",
       "5          1555        20140        201400  \n",
       "6          1120        29550        295500  \n",
       "7          1400        36140        361400  \n",
       "8          1780        23400        234000  \n",
       "9          1890        26670        266700  \n",
       "10         2100        41280        412800  \n",
       "11         1760        30020        300200  "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tp=df['total_profit']\n",
    "prange=[150000,175000,200000,225000,250000,300000,350000,450000]\n",
    "plt.hist(tp,prange,label='Profit Data',edgecolor='k')\n",
    "plt.xlabel('Range')\n",
    "plt.ylabel('Actual Profit')\n",
    "plt.legend()\n",
    "plt.xticks(prange)\n",
    "plt.title('Profit Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# calculate total ale for last year for each product and show it with the help of various PIE CHART. no of unit sold per year using pie chart in tearms of percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mn=df['month_number']\n",
    "labels=['facecream','facewash','ttoothpaste','bathingsoap','shampoo','moisturizer']\n",
    "salesdata=[df['facecream'].sum(),df['facewash'].sum(),df['toothpaste'].sum(),df['bathingsoap'].sum(),df['shampoo'].sum(),df['moisturizer'].sum()]\n",
    "plt.pie(salesdata,labels=labels,autopct='%2.2f%%')\n",
    "plt.legend()\n",
    "plt.title('Sales Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}