Adhikari-Chandra_Class-4_Pandas-20211230.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:3.75em;color:purple; font-style:bold\"><br>\n",
    "Pandas</p><br>\n",
    "\n",
    "**Chandra M Adhikari, PhD**\n",
    "\n",
    "**Dec 2021, Fayetteville, NC**\n",
    "\n",
    "\n",
    "\n",
    "*Pandas* is a Python library for data analysis. It offers a number of data exploration, cleaning and transformation operations that are critical in working with data in Python. \n",
    "\n",
    "*Pandas* build upon *numpy* and *scipy* providing easy-to-use data structures and data manipulation functions with integrated indexing.\n",
    "\n",
    "**Additional Recommended Resources:**\n",
    "* *Pandas* Documentation: http://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "What kind of data does pandas handle? \n",
    "</p>\n",
    "\n",
    "Tabular data, such as data stored in spreadsheets or databases. In pandas, a data table is called a DataFrame.\n",
    "\n",
    "Pandas helps us to explore, clean, and process our data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "How to read and write tabular data   in pandas? \n",
    "</p>\n",
    "\n",
    "\n",
    "Let's say you have a dataset called movie in csv format then you read it as\n",
    "\n",
    "movie = pd.read_csv(\"data/movie.csv\")\n",
    "\n",
    "pandas supports many different file formats or data sources out of the box (csv, excel, sql, json, parquet, …), each of them with the prefix read_*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "How to create plots in pandas? \n",
    "</p>\n",
    "\n",
    "\n",
    "Pandas provides plotting our data out of the box, using the power of Matplotlib. We can pick the plot type (scatter, bar, boxplot,…) corresponding to our data.\n",
    "\n",
    "For a quick visual check of the data, we use DataFrameName.plot().\n",
    "\n",
    "If we want to plot only the columns of the data, we use DataFrameName[\"ColumnName\"].plot()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Introduction to pandas Data Structures</p>\n",
    "<br>\n",
    "*pandas* has two main data structures it uses, namely, *Series* and *DataFrames*. \n",
    "\n",
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "pandas Series</p>\n",
    "\n",
    "*pandas Series* one-dimensional labeled array. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.Series(data=[100, 200, 300, 400, 500], index=['Jetha', 'Maila', 'Saila', 'Kaila', 'Kancha'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jetha     100\n",
       "Maila     200\n",
       "Saila     300\n",
       "Kaila     400\n",
       "Kancha    500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Jetha', 'Maila', 'Saila', 'Kaila', 'Kancha'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser['Saila']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kancha    500\n",
       "Kaila     400\n",
       "Maila     200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[[4, 3, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'Kaila' in ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jetha      200\n",
       "Maila      400\n",
       "Saila      600\n",
       "Kaila      800\n",
       "Kancha    1000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser * 2 #What does multiplying by a number do to dataframe, i.e. for example dataframe * 2?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jetha      10000\n",
       "Maila      40000\n",
       "Saila      90000\n",
       "Kaila     160000\n",
       "Kancha    250000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser ** 2 #What does exponent do to dataframe, i.e. for example dataframe ** 2?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "pandas DataFrame</p>\n",
    "\n",
    "*Pandas DataFrame* is a 2-dimensional labeled data structure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Create DataFrame from dictionary of Python Series</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'one' : pd.Series([100., 200., 300.], index=['apple', 'mango', 'orange']),\n",
    "     'two' : pd.Series([111., 222., 333., 4444.], index=['apple', 'mango', 'banana', 'peach'])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          one     two\n",
      "apple   100.0   111.0\n",
      "banana    NaN   333.0\n",
      "mango   200.0   222.0\n",
      "orange  300.0     NaN\n",
      "peach     NaN  4444.0\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(d)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['apple', 'banana', 'mango', 'orange', 'peach'], dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index #Find indices "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns #What Columns present?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "          one    two\n",
       "banana    NaN  333.0\n",
       "ball      NaN    NaN\n",
       "apple   100.0  111.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a sub-region of dataframe specifying indices\n",
    "pd.DataFrame(d, index=['banana', 'ball', 'apple'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>222.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>111.0</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "         two five\n",
       "mango  222.0  NaN\n",
       "ball     NaN  NaN\n",
       "apple  111.0  NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If you call a columns which is not avialable to the dataframe, it creats that columns with NaN values\n",
    "pd.DataFrame(d, index=['mango', 'ball', 'apple'], columns=['two', 'five'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Create DataFrame from list of Python dictionaries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{'alex': 1, 'joe': 2}, {'ema': 5, 'dora': 10, 'alice': 20}]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <th>alex</th>\n",
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       "      <th>ema</th>\n",
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       "      <th>alice</th>\n",
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
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      "text/plain": [
       "   alex  joe  ema  dora  alice\n",
       "0   1.0  2.0  NaN   NaN    NaN\n",
       "1   NaN  NaN  5.0  10.0   20.0"
      ]
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     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>alex</th>\n",
       "      <th>joe</th>\n",
       "      <th>ema</th>\n",
       "      <th>dora</th>\n",
       "      <th>alice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>red</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
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      "text/plain": [
       "        alex  joe  ema  dora  alice\n",
       "orange   1.0  2.0  NaN   NaN    NaN\n",
       "red      NaN  NaN  5.0  10.0   20.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "pd.DataFrame(data, index=['orange', 'red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th>joe</th>\n",
       "      <th>dora</th>\n",
       "      <th>alice</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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      "text/plain": [
       "   joe  dora  alice\n",
       "0  2.0   NaN    NaN\n",
       "1  NaN  10.0   20.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=['joe', 'dora','alice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\">\n",
    "Basic DataFrame operations</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
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       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
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      "text/plain": [
       "          one     two\n",
       "apple   100.0   111.0\n",
       "banana    NaN   333.0\n",
       "mango   200.0   222.0\n",
       "orange  300.0     NaN\n",
       "peach     NaN  4444.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     100.0\n",
       "banana      NaN\n",
       "mango     200.0\n",
       "orange    300.0\n",
       "peach       NaN\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one'] #accessing an column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>44400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two    three\n",
       "apple   100.0   111.0  11100.0\n",
       "banana    NaN   333.0      NaN\n",
       "mango   200.0   222.0  44400.0\n",
       "orange  300.0     NaN      NaN\n",
       "peach     NaN  4444.0      NaN"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can create  an additional column from existing columns using mathematical operations  \n",
    "df['three'] = df['one'] * df['two']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>11100.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>44400.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two    three   flag\n",
       "apple   100.0   111.0  11100.0  False\n",
       "banana    NaN   333.0      NaN  False\n",
       "mango   200.0   222.0  44400.0  False\n",
       "orange  300.0     NaN      NaN   True\n",
       "peach     NaN  4444.0      NaN  False"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['flag'] = df['one'] > 250 #we can use flag to check whether any outlier in data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "three = df.pop('three')# To remove a column called three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     11100.0\n",
       "banana        NaN\n",
       "mango     44400.0\n",
       "orange        NaN\n",
       "peach         NaN\n",
       "Name: three, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two   flag\n",
       "apple   100.0   111.0  False\n",
       "banana    NaN   333.0  False\n",
       "mango   200.0   222.0  False\n",
       "orange  300.0     NaN   True\n",
       "peach     NaN  4444.0  False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['flag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two\n",
       "apple   100.0   111.0\n",
       "banana    NaN   333.0\n",
       "mango   200.0   222.0\n",
       "orange  300.0     NaN\n",
       "peach     NaN  4444.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>copy_of_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two  copy_of_one\n",
       "apple   100.0   111.0        100.0\n",
       "banana    NaN   333.0          NaN\n",
       "mango   200.0   222.0        200.0\n",
       "orange  300.0     NaN        300.0\n",
       "peach     NaN  4444.0          NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.insert(2, 'copy_of_one', df['one'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "Project 1 </p><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have a file name 'NFLwinloss.xlsx' in my desktop. I have dowloaded it on May 19, 2021 from [this Wikipedia page](https://en.wikipedia.org/wiki/List_of_all-time_NFL_win%E2%80%93loss_records). You either download it or ask me, I will provide it to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Rank                      Team    GP  Won  Lost  Tied   Pct.   Division\n",
      "0      1         Green Bay Packers  1384  769   577    38  0.569  NFC North\n",
      "1      2            Dallas Cowboys   930  526   398     6  0.569   NFC East\n",
      "2      3          Baltimore Ravens   400  225   174     1  0.564  AFC North\n",
      "3      4             Chicago Bears  1418  777   599    42  0.563  NFC North\n",
      "4      5      New England Patriots   932  519   404     9  0.562   AFC East\n",
      "5      6            Miami Dolphins   848  467   377     4  0.553   AFC East\n",
      "6      7         Minnesota Vikings   918  495   412    11  0.545  NFC North\n",
      "7      8        Kansas City Chiefs   933  495   426    12  0.537   AFC West\n",
      "8      9       Pittsburgh Steelers  1220  643   556    21  0.536  AFC North\n",
      "9     10       San Francisco 49ers  1050  551   485    14  0.531   NFC West\n",
      "10    11           New York Giants  1353  702   618    33  0.531   NFC East\n",
      "11    12     Indianapolis Colts[d]  1014  534   473     7  0.530  AFC South\n",
      "12    13            Denver Broncos   932  488   434    10  0.529   AFC West\n",
      "13    14         Las Vegas Raiders   932  481   440    11  0.522   AFC West\n",
      "14    15          Seattle Seahawks   708  367   340     1  0.519   NFC West\n",
      "15    16          Los Angeles Rams  1183  587   575    21  0.505   NFC West\n",
      "16    17  Washington Football Team  1250  610   612    28  0.499   NFC East\n",
      "17    18      Los Angeles Chargers   932  459   462    11  0.498   AFC West\n",
      "18    19       Cleveland Browns[e]  1002  486   505    11  0.491  AFC North\n",
      "19    20       Philadelphia Eagles  1236  590   619    27  0.488   NFC East\n",
      "20    21          Tennessee Titans   932  451   475     6  0.487  AFC South\n",
      "21    22         Carolina Panthers   416  200   215     1  0.482  NFC South\n",
      "22    23             Buffalo Bills   932  438   486     8  0.474   AFC East\n",
      "23    24        New Orleans Saints   834  387   442     5  0.467  NFC South\n",
      "24    25             Detroit Lions  1281  567   681    33  0.456  NFC North\n",
      "25    26        Cincinnati Bengals   820  363   452     5  0.446  AFC North\n",
      "26    27             New York Jets   932  410   514     8  0.444   AFC East\n",
      "27    28            Houston Texans   304  135   169     0  0.444  AFC South\n",
      "28    29           Atlanta Falcons   848  369   473     6  0.439  NFC South\n",
      "29    30         Arizona Cardinals  1378  566   771    41  0.426   NFC West\n",
      "30    31      Jacksonville Jaguars   416  177   239     0  0.425  AFC South\n",
      "31    32      Tampa Bay Buccaneers   708  278   429     1  0.393  NFC South\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_excel('NFLwinloss.xlsx') # load the data\n",
    "print (df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Team</th>\n",
       "      <th>GP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Green Bay Packers</td>\n",
       "      <td>1384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dallas Cowboys</td>\n",
       "      <td>930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Baltimore Ravens</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago Bears</td>\n",
       "      <td>1418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Miami Dolphins</td>\n",
       "      <td>848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Minnesota Vikings</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kansas City Chiefs</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Pittsburgh Steelers</td>\n",
       "      <td>1220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>San Francisco 49ers</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>New York Giants</td>\n",
       "      <td>1353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Indianapolis Colts[d]</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Denver Broncos</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Las Vegas Raiders</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Seattle Seahawks</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Los Angeles Rams</td>\n",
       "      <td>1183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Washington Football Team</td>\n",
       "      <td>1250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Los Angeles Chargers</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Cleveland Browns[e]</td>\n",
       "      <td>1002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Philadelphia Eagles</td>\n",
       "      <td>1236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Tennessee Titans</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Carolina Panthers</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Buffalo Bills</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>New Orleans Saints</td>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Detroit Lions</td>\n",
       "      <td>1281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Cincinnati Bengals</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>New York Jets</td>\n",
       "      <td>932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Houston Texans</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Atlanta Falcons</td>\n",
       "      <td>848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Arizona Cardinals</td>\n",
       "      <td>1378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Jacksonville Jaguars</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Tampa Bay Buccaneers</td>\n",
       "      <td>708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Team    GP\n",
       "0          Green Bay Packers  1384\n",
       "1             Dallas Cowboys   930\n",
       "2           Baltimore Ravens   400\n",
       "3              Chicago Bears  1418\n",
       "4       New England Patriots   932\n",
       "5             Miami Dolphins   848\n",
       "6          Minnesota Vikings   918\n",
       "7         Kansas City Chiefs   933\n",
       "8        Pittsburgh Steelers  1220\n",
       "9        San Francisco 49ers  1050\n",
       "10           New York Giants  1353\n",
       "11     Indianapolis Colts[d]  1014\n",
       "12            Denver Broncos   932\n",
       "13         Las Vegas Raiders   932\n",
       "14          Seattle Seahawks   708\n",
       "15          Los Angeles Rams  1183\n",
       "16  Washington Football Team  1250\n",
       "17      Los Angeles Chargers   932\n",
       "18       Cleveland Browns[e]  1002\n",
       "19       Philadelphia Eagles  1236\n",
       "20          Tennessee Titans   932\n",
       "21         Carolina Panthers   416\n",
       "22             Buffalo Bills   932\n",
       "23        New Orleans Saints   834\n",
       "24             Detroit Lions  1281\n",
       "25        Cincinnati Bengals   820\n",
       "26             New York Jets   932\n",
       "27            Houston Texans   304\n",
       "28           Atlanta Falcons   848\n",
       "29         Arizona Cardinals  1378\n",
       "30      Jacksonville Jaguars   416\n",
       "31      Tampa Bay Buccaneers   708"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2[['Team','GP']]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Team'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df3[:].plot(kind='bar', x='Team', y='GP', figsize=(12,6), grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:green; font-style:bold\"><br>\n",
    "Homework </p>\n",
    "    \n",
    "1. Plot a bar chart to show the difference of the # of win and the # of loss of each team.\n",
    "  \n",
    "2. Group the team by division, calculate the average game won by each division, and present it in a bar chart. (Challanging Problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "Project 2 </p><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Data Cleaning: Iris plant species dataset </p>\n",
    "<br>\n",
    "\n",
    "**Data Source:** https://www.kaggle.com/uciml/iris?select=Iris.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris = pd.read_csv('./iris.csv') # load the data\n",
    "df_iris.shape # shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the first five rows of dataset \n",
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the first two rows of dataset \n",
    "df_iris.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0    1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1    2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2    3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3    4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4    5            5.0           3.6            1.4           0.2  Iris-setosa\n",
       "5    6            5.4           3.9            1.7           0.4  Iris-setosa\n",
       "6    7            4.6           3.4            1.4           0.3  Iris-setosa\n",
       "7    8            5.0           3.4            1.5           0.2  Iris-setosa\n",
       "8    9            4.4           2.9            1.4           0.2  Iris-setosa\n",
       "9   10            4.9           3.1            1.5           0.1  Iris-setosa\n",
       "10  11            5.4           3.7            1.5           0.2  Iris-setosa\n",
       "11  12            4.8           3.4            1.6           0.2  Iris-setosa\n",
       "12  13            4.8           3.0            1.4           0.1  Iris-setosa\n",
       "13  14            4.3           3.0            1.1           0.1  Iris-setosa\n",
       "14  15            5.8           4.0            1.2           0.2  Iris-setosa"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris[:15] # the first 15 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SepalWidthCm</th>\n",
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       "      <th>Species</th>\n",
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       "  </thead>\n",
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       "      <th>145</th>\n",
       "      <td>146</td>\n",
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       "      <th>148</th>\n",
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       "      <td>6.2</td>\n",
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       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the last five rows of dataset \n",
    "df_iris.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SepalLengthCm</th>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
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      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying the last two rows of dataset \n",
    "df_iris.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "140  141            6.7           3.1            5.6           2.4   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "142  143            5.8           2.7            5.1           1.9   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "144  145            6.7           3.3            5.7           2.5   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "140  Iris-virginica  \n",
       "141  Iris-virginica  \n",
       "142  Iris-virginica  \n",
       "143  Iris-virginica  \n",
       "144  Iris-virginica  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris[-10:] #last 10 rows "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Data Rebuild "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0    False          False         False          False         False    False\n",
       "1    False          False         False          False         False    False\n",
       "2    False          False         False          False         False    False\n",
       "3    False          False         False          False         False    False\n",
       "4    False          False         False          False         False    False\n",
       "..     ...            ...           ...            ...           ...      ...\n",
       "145  False          False         False          False         False    False\n",
       "146  False          False         False          False         False    False\n",
       "147  False          False         False          False         False    False\n",
       "148  False          False         False          False         False    False\n",
       "149  False          False         False          False         False    False\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.isnull() # To check whether any entry empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
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       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
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       "      <th>147</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species\n",
       "0    False          False         False          False         False    False\n",
       "1    False          False         False          False         False    False\n",
       "2    False          False         False          False         False    False\n",
       "3    False          False         False          False         False    False\n",
       "4    False          False         False          False         False    False\n",
       "..     ...            ...           ...            ...           ...      ...\n",
       "145  False          False         False          False         False    False\n",
       "146  False          False         False          False         False    False\n",
       "147  False          False         False          False         False    False\n",
       "148  False          False         False          False         False    False\n",
       "149  False          False         False          False         False    False\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.isna() # To check whether we have any NaN(non a number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               False\n",
       "SepalLengthCm    False\n",
       "SepalWidthCm     False\n",
       "PetalLengthCm    False\n",
       "PetalWidthCm     False\n",
       "Species          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.isna().any() # to check is there any nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.isna().sum() # It gives how many nan we have "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Iris-setosa</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "      <th>147</th>\n",
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       "      <td>6.5</td>\n",
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       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.fillna(0)# Filling NaN with 0 \n",
    "# but we do not have any NaN in our iris plant dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iris.dropna() # to drop rows with NaN to any entry.\n",
    "# We do not have any NaN\n",
    "# becareful with dropping NaN from the entire dataframe. You are going to lose the row otherwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's take our previous dataframe df as an example again"
   ]
  },
  {
   "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",
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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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>copy_of_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>NaN</td>\n",
       "      <td>333.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two  copy_of_one\n",
       "apple   100.0   111.0        100.0\n",
       "banana    NaN   333.0          NaN\n",
       "mango   200.0   222.0        200.0\n",
       "orange  300.0     NaN        300.0\n",
       "peach     NaN  4444.0          NaN"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>copy_of_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>0.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peach</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4444.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one     two  copy_of_one\n",
       "apple   100.0   111.0        100.0\n",
       "banana    0.0   333.0          0.0\n",
       "mango   200.0   222.0        200.0\n",
       "orange  300.0     0.0        300.0\n",
       "peach     0.0  4444.0          0.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "    .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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>copy_of_one</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>100.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>200.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         one    two  copy_of_one\n",
       "apple  100.0  111.0        100.0\n",
       "mango  200.0  222.0        200.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "Project 3 </p><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\">\n",
    "Does Life Expectancy Depend on GDP?: A Study Taking WDI Data of India and the USA</p>\n",
    "<br>\n",
    "\n",
    "### Objective:\n",
    "\n",
    "**Study of the life expectancy at birth and GDP per capita in India.**\n",
    "\n",
    "**Compare the  life expectancy in India trend with that of the USA.**\n",
    "\n",
    "**Are there any correlation between the life expectancy and GDP per capita in India.**\n",
    "\n",
    "\n",
    "**Data Source:** (*Not avialable now, unfortunately*) https://www.kaggle.com/worldbank/world-development-indicators\n",
    "\n",
    "#### Download the Dataset\n",
    "\n",
    "If the link does not work, let me know, I will be happy to provide the data to you. \n",
    "\n",
    "**Folder:** 'world-development-indicators' \n",
    "\n",
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial exploration of the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5656458, 6)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator = pd.read_csv('./world-development-indicators/Indicators.csv') # load the data\n",
    "df_indicator.shape # shape of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Adolescent fertility rate (births per 1,000 wo...</td>\n",
       "      <td>SP.ADO.TFRT</td>\n",
       "      <td>1960</td>\n",
       "      <td>1.335609e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio (% of working-age populat...</td>\n",
       "      <td>SP.POP.DPND</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.779760e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, old (% of working-age po...</td>\n",
       "      <td>SP.POP.DPND.OL</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.634579e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Age dependency ratio, young (% of working-age ...</td>\n",
       "      <td>SP.POP.DPND.YG</td>\n",
       "      <td>1960</td>\n",
       "      <td>8.102333e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms exports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.XPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>3.000000e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Arms imports (SIPRI trend indicator values)</td>\n",
       "      <td>MS.MIL.MPRT.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.380000e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>Birth rate, crude (per 1,000 people)</td>\n",
       "      <td>SP.DYN.CBRT.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>4.769789e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (kt)</td>\n",
       "      <td>EN.ATM.CO2E.KT</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.956399e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions (metric tons per capita)</td>\n",
       "      <td>EN.ATM.CO2E.PC</td>\n",
       "      <td>1960</td>\n",
       "      <td>6.439635e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arab World</td>\n",
       "      <td>ARB</td>\n",
       "      <td>CO2 emissions from gaseous fuel consumption (%...</td>\n",
       "      <td>EN.ATM.CO2E.GF.ZS</td>\n",
       "      <td>1960</td>\n",
       "      <td>5.041292e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CountryName CountryCode                                      IndicatorName  \\\n",
       "0  Arab World         ARB  Adolescent fertility rate (births per 1,000 wo...   \n",
       "1  Arab World         ARB  Age dependency ratio (% of working-age populat...   \n",
       "2  Arab World         ARB  Age dependency ratio, old (% of working-age po...   \n",
       "3  Arab World         ARB  Age dependency ratio, young (% of working-age ...   \n",
       "4  Arab World         ARB        Arms exports (SIPRI trend indicator values)   \n",
       "5  Arab World         ARB        Arms imports (SIPRI trend indicator values)   \n",
       "6  Arab World         ARB               Birth rate, crude (per 1,000 people)   \n",
       "7  Arab World         ARB                                 CO2 emissions (kt)   \n",
       "8  Arab World         ARB             CO2 emissions (metric tons per capita)   \n",
       "9  Arab World         ARB  CO2 emissions from gaseous fuel consumption (%...   \n",
       "\n",
       "       IndicatorCode  Year         Value  \n",
       "0        SP.ADO.TFRT  1960  1.335609e+02  \n",
       "1        SP.POP.DPND  1960  8.779760e+01  \n",
       "2     SP.POP.DPND.OL  1960  6.634579e+00  \n",
       "3     SP.POP.DPND.YG  1960  8.102333e+01  \n",
       "4     MS.MIL.XPRT.KD  1960  3.000000e+06  \n",
       "5     MS.MIL.MPRT.KD  1960  5.380000e+08  \n",
       "6     SP.DYN.CBRT.IN  1960  4.769789e+01  \n",
       "7     EN.ATM.CO2E.KT  1960  5.956399e+04  \n",
       "8     EN.ATM.CO2E.PC  1960  6.439635e-01  \n",
       "9  EN.ATM.CO2E.GF.ZS  1960  5.041292e+00  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator[:10] # display the first 10 rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Country name and country code are the same, the year is also the same, but indicators are many. I would like to see on the  GDP per capita and the life expentancy. Try to see a few more rows to see more the IndicatorName."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us make sure we have the data of India:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator['CountryCode'].str.contains(\"IND\").any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indicator['CountryName'].str.contains(\"India\").any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many years of data do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many years of data do we have ?\n",
    "years = df_indicator['Year'].unique().tolist()\n",
    "len(years)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's the range of years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1960  to  2015\n"
     ]
    }
   ],
   "source": [
    "print(min(years),\" to \",max(years))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lets pick a country and an indicator to explore: Life expentancy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select Life expectancy for India\n",
    "hist_indicator = 'Life expectancy at birth, total \\(years\\)'\n",
    "hist_country = 'IND'\n",
    "\n",
    "m_LE = df_indicator['IndicatorName'].str.contains(hist_indicator) \n",
    "m_CC = df_indicator['CountryCode'].str.contains(hist_country)\n",
    "\n",
    "# df_LE is just those indicators matching the USA for country code and Life expectancy at birth, total (years) over time.\n",
    "df_LE= df_indicator[m_LE & m_CC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11684</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1960</td>\n",
       "      <td>41.171951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36635</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1961</td>\n",
       "      <td>41.790488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64177</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1962</td>\n",
       "      <td>42.417415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92622</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1963</td>\n",
       "      <td>43.052732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121419</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>Life expectancy at birth, total (years)</td>\n",
       "      <td>SP.DYN.LE00.IN</td>\n",
       "      <td>1964</td>\n",
       "      <td>43.698415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       CountryName CountryCode                            IndicatorName  \\\n",
       "11684        India         IND  Life expectancy at birth, total (years)   \n",
       "36635        India         IND  Life expectancy at birth, total (years)   \n",
       "64177        India         IND  Life expectancy at birth, total (years)   \n",
       "92622        India         IND  Life expectancy at birth, total (years)   \n",
       "121419       India         IND  Life expectancy at birth, total (years)   \n",
       "\n",
       "         IndicatorCode  Year      Value  \n",
       "11684   SP.DYN.LE00.IN  1960  41.171951  \n",
       "36635   SP.DYN.LE00.IN  1961  41.790488  \n",
       "64177   SP.DYN.LE00.IN  1962  42.417415  \n",
       "92622   SP.DYN.LE00.IN  1963  43.052732  \n",
       "121419  SP.DYN.LE00.IN  1964  43.698415  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_LE.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How life expectancy have changed over time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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": [
    "# get the years\n",
    "years =df_LE['Year'].values\n",
    "# get the values \n",
    "LifeExp = df_LE['Value'].values\n",
    "\n",
    "# create\n",
    "plt.xlabel('Year',size=12, color='k')\n",
    "plt.ylabel('Life expectancy at birth (Years)',size=12, color='k')\n",
    "plt.bar(years,LifeExp,color='b')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "plt.title('Life expectancy at birth in India')\n",
    "plt.axis([1959, 2014,0,82])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Turns out Life expectancy at birth have been consistently increaseing. From 1960 to 2014, the life expectancy increased consistently from about 40 to 70 years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How about life expectancy in USA? Does the USA have the same trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select Life expectancy for the USA\n",
    "hist_indicator_2 = 'Life expectancy at birth, total \\(years\\)'\n",
    "hist_country_2 = 'USA'\n",
    "\n",
    "m_LE_2 = df_indicator['IndicatorName'].str.contains(hist_indicator_2) \n",
    "m_CC_2 = df_indicator['CountryCode'].str.contains(hist_country_2)\n",
    "\n",
    "# df_LE_2 is just those indicators matching the USA for country code and Life expectancy at birth, total (years) over time.\n",
    "df_LE_2= df_indicator[m_LE_2 & m_CC_2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": [
    "# get the years\n",
    "years_2 =df_LE_2['Year'].values\n",
    "# get the values \n",
    "LifeExp_2 = df_LE_2['Value'].values\n",
    "\n",
    "# create\n",
    "plt.xlabel('Year',size=12, color='k')\n",
    "plt.ylabel('Life expectancy at birth (Years)',size=12, color='k')\n",
    "plt.bar(years_2,LifeExp_2,color='b')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "plt.title('Life expectancy at birth in the USA')\n",
    "plt.axis([1959, 2014,0,82])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the life expectancy has increased over the year but the rate of increase in life ecpectancy is not as fast as in India. Indeed, it was about 70 years in the USA in 1960, which was only 40 years in India at that time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's get line plots in the same graph to make a comparision easy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hww8/hJmZGbf/P//5T3zwwQfYsmUL+vfvj+nTpyM9PR2ArD74q6++wp49e2BhYcHdnezbtw9lZWUYMmQIZs+ejYULF8LOzq5Zn7mOjg4iIyNhaWmJqVOnQiQSYePGjQ16fwcFBaGmpkbuSrGzGT58OPbu3Ytdu3bB1dUVJ0+efGGv388++wympqYYPXo0Jk2ahOHDh2P06NFN7rN+/Xq88sormDRpEsaMGQMdHR3MnTu32eU9cOAAevfujfHjx2Pq1KmYM2fOC/9+06dPx+TJk+Hl5QUTExMcPXpUqXNpa2vj4sWL6NOnD2bOnAknJycEBgaisLAQBgYGCvd78803sX//fuzfvx9Dhw7FxIkTkZ2djUuXLsHc3BwAYGVlhT59+mDz5s0YPnw4Bg4ciJ07d2L16tX4+uuvAQB79+7FuHHjYGRk1OAcM2fOxOPHjxEeHq5ULJ0Jj7VF5R/psi5cuIBx48bhyZMnCqtcuoNt27Zh7969SEhI6OiiENJp0B0C6VHKyspw69YtfPnll3jvvfc6ujiEdCqUEEiPsmLFCnh4eMDDw4N75kEIkaEqI0IIIQDoDoEQQshTlBAIIYQA6OId0xR1ejE2Nm71cec7s54WL0Ax9xQUc+trqtk63SEQQggBQAmBEELIU5QQCCGEAGjHhJCSkoLJkyfDwMAA5ubmWLFiBerq6gAAERERcHJygra2NsaNG4fHjx+3V7EIIYQ81W4JYdmyZTA1NUVWVhbu3r2LyMhI/Pvf/0ZeXh78/f2xZcsWFBQUYOjQoZg1a1Z7FYsQQshT7ZYQkpOT8cYbb0BTUxPm5uaYOHEiYmNjcfLkSYhEIsycOROampoIDg5GdHQ04uPj26tohBBC0I7NTt977z2EhoZi7NixKCwsxOnTp7FlyxZcuHBBbsYuHR0d2NvbIzY2Fk5OTnLHCA8P50YQDAkJUTj4Gp/P79YDsz2vp8ULUMw9BcXczudurxN5enri+++/h56eHiQSCQIDAzFt2jT88ssvMDExkdtWKBSitLS0wTG8vb3h7e3NvVfUVrentV3uafECFHNPQTG3vqb6IbRLQpBKpXjttdewePFiXLlyBWVlZVi4cCE++OADCAQClJSUyG1fUlICXV3d9igaIYR0LowBVVXgVVRApbISvIoK2au8/K/fny5XqX//7OuZfQp37YK0Vy+lT90uCaGgoABpaWlYsWIFNDQ0oKGhgbfeegvr16/Hu+++i4MHD3LblpeXQywWQyQStUfRCCGkYzAG1fR0qN2/L3vFxEAtNhYqT57AopVmW1MpLu58CcHY2Bi9e/fGrl27sHr1apSVleHgwYNwc3ODn58f1qxZg7CwMPj4+GDz5s1wdXVt8PyAEEI6pbo6qDx5ApWysoZX6xUVUCkpgUpJCXjFxVB5+uIVFUEtMREqRUWNHpKpq4Npa4NpaUGqrS37vf6lpSX7qaMDpq391/r65fU/tbUhsbFpVijt9gzh5MmTWLlyJbZt2wZVVVWMGzcOn3/+OUxMTBAWFoYVK1YgICAA7u7uCA0Nba9iEUKIQrzKSqhkZUE1OxuqT3/Kvc/KgsqTJ+C18IpeYmSEWldX1IpEqHVxQa2LCwzc3JCnIFG0tXZLCAMHDsSFCxcaXeft7U3NTAkhbYcxqOTngy8Wg//oEfhiMVTFYqhmZoLX2JQwtbVQzc1VeAUvd2geDxITE0j19Bq9kpfq6YEJhZAKhbLf9fUhFQpRZ2Mjq855br5v8DtuzNEuPdopIaSbY0zuIWmjr8rKvx6ulpX9VT1TUgKVoiLwSkqg+uQJVIqLm396dXVIzM0hMTeH9OlPSa9e3EvaqxckpqaAmlobBN/+KCEQQloFr7IS/Ph4qMXFyerUi4vBKymR1Zs//WLm1dY23JExQCIBr7YWvJoaoLZWtl1NDXjV1ejVSpM6SvX0UGdvj7revWU/+/SBxMYGrLErchUVSM3MIDU0bHgF3429MCHk5OTgjz/+QHR0NIqKiqCvrw83NzdMmDAB5ubm7VFGQkhnwhhUMzPBT0iA2oMH4MfGQi02FvxHj1pcl97k6TQ0Gn+4+twDVGn9Q1Z9fTA9PVkVjVAoq64xMupxX+4toTAhxMXFYcOGDTh//jyGDBmC/v37w9zcHKWlpTh8+DBWrlyJcePGYfPmzXB2dm7PMhNCFOBVVkI1IwOqGRngFRXJvghNTSExNQUTCht+IUqlsmqWZ67geXV1sqvzujrZ1XpVFfjJyeAnJspeSUlQqahocG6mqopaJyfUOjtDYmEhV29e/8XMNDQaLzifD6amJrtaV1cHU1MD1NVhZGHRYQ9YeyKFCWHBggVYs2YNfvjhB2g08kesqanBTz/9hKCgIFy9erVNC0lIj1VdDZX8fKjm54NXVwfNtLS/mi4+rSNXKSiAanq6LBEUFCg8FFNXh8TEBExfH7zy8r+SQAuu6iXGxqhzcEBt//6oFYlQJxKh1sEB0NR8mWgb6sAHrD0Rj7FWqqDrADSFpkxPixfoIjEzBl5FBVBT0+g6lZISrvmiyjPNGFVzc6GSlweV/HyoNDKES5OnVFeHxMICEktLSPX1ZckiJ0dWp6/gWFIdnb+u4NXVZVfr9T/V1GTHtLZGnYMD6hwdUdu3L5iBQUs+kWbrEn/nVtblhq44f/48VFVVMWbMmBYXipB2VVfXsMPQC1quNNockTHZsWprZT+feQjKKyuTe4CqUlIiq3Z5CYzP5+q/Vc3MUF1fR/5MVYzU0BASS0tZEjAxAVQaH8SYV1EBldxcWe9VgUDW/FFPr9u0kCEvT6mE4OnpiU8//RQeHh7Ytm0bPvvsM/D5fCxfvhz/+Mc/2rqMhChHKoVqVhbXxpxf/3r0SFan3gE3w1ItLUBBvblUW1vWbLH+9bRJo9TMDFJjY0iMjGT1/k+/4I2NjVH4EleOTFsbEjs7SFp8BNLdKZUQYmJiMHz4cADA999/jwsXLkAgEMDDw4MSAml1vJISqMXGQi0mBipPnoDp6v7VuUdPD1I9PfAMDKAVFSXf0Sg5GSpVVY0ek/F4sqvi51qpSBW0WGFaWoCqauPHelqVAjW1v37y+WACgXzLFqEQUFdvy4+KkFalVEKQSqXg8XgQi8VgjKF///4AgMLCwjYtHOleeOXlUMnN/avK5pkOR6oZGbLBvWJiwE9JUep4jdViS4yNZW3M6199+qDO3l42pgtVjRDSJKUSwqhRo7BixQpkZWXBz88PACAWi3vcxBVESVVV4D96BLWHD2UdlR4+BP/hQ/BTU5XanWloyJovDhgAiZXVX3Xzz/RA5QOotrKS+9Kv691bVsVCCGkRpRLCgQMHsHPnTpiYmGDt2rUAgPj4eLz33nttWjjSeank50P98mVZ/fzzA38paPrI1NQg6dWrYYcibW1IjY1RO2AAagcMQJ2Dwwuv5l+2Pp0Q0tALE4JEIsHf//53fPfdd3L9EXx8fNq0YKSTqauD+p070Dh/HhqRkVC7d0/hQ1rG50NiZYXa/v1R168fap2cUOfkhDo7O6q2IaQTe2FCUFVVxR9//AEVBU3ZSDfFGPhiMdQvX4bGn39C4/JlqDwzsx3T0ECVuztqXVwgsbCQDfxVP+CXsbHCpo+EkM5LqSqj999/Hxs3bsSmTZugRld43RKvshIqmZlQv3lTlgD+/BOqOTly29Ta26N67FhUjx2LmhEjZC1xCCHdhlIJ4euvv0Z2djY+++wzmJiYgPfMeCipSj4oJO1AKoVKdjb4aWlQTU2FalYWeNXVf40e+bRDFa+6GiqFhX/1hs3La3RsGomJCao9PFDj4YHq0aMhsbbugKAIIe1FqYRw5MiRlzqJQCCQe19ZWYlly5bh66+/BgBERERg+fLlSE1Nhbu7Ow4cOABbW9uXOmenJ5XKXs0Zq4Ux8AoLZV/4jx9zX/z87GyYisWyzleNDZOgzKE1NCAxNkatqyuXBOocHGh0SEJ6EKV7Kr+MsrIy7vfy8nKYmZlh5syZAIC8vDz4+/tjz549mDp1KjZs2IBZs2bh2rVrL3XOZ6kUFID/4AGgpiY/8qK2dvO/8KRS2UiQtbV/DVnwdFRIKBgkTKWwUK4DFT85GfzkZABAzdChqB41CtWjR6PWxUWuMxSvvBxqt29D4/p1qF+/LuuopWA8mvoae4mxsWzcGRsbSCwtZdU6z3Wggrq6bLgDIyNIjY0hNTYG09GhL39CejilL0/v3r2LS5cuIS8vD8+Oh7d58+ZmnfDEiRMwNTXF6NGjAcjmWhaJRFyCCA4OhrGxMeLj4+Hk5NSsYwMAqqvBu34dOufPQy0qCupRUeA/ftzopozPlyWIZ74YJU9/So2MwCsv55pTck0qc3Mbn+SjhTQuX4bG5ctASAikQiGqR4yAxNIS6rdvQ+3+ffAk8gMNSAWCv77wn760RSIU6utDYm0tS3KEENICSiWE7777Du+//z5effVVnD59GpMmTcIff/wBX1/fZp/w4MGDmD9/PvccIjY2Fm5ubtx6HR0d2NvbIzY2tkFCCA8PR3h4OAAgJCSkQcc4lW3boLp1K3g1NXi2exLT1gZzdZW1fCksBK+oCCgqko0dX1AgazefmKh0DExDQ9Z8Ul1d9lNNTVb1o2ioA4EAzNERcHQEc3AAc3QE69sXqKmBSmQkeOfOQeX8eag8egStM2f+2k9VFdKhQ8E8PCD18AAbPhwwNQV4PKgCqD+bKp8P/ZccRK2r4fP5Pa5jJMXcM3RkzEolhO3bt+PMmTMYPXo0DAwMcOrUKZw+fRqhoaHNOllqaioiIyOxd+9ebllZWRlMTEzkthMKhShtpGrE29sb3t7e3Pvnh4jV1taGsLYW0v79UeXqippBg1AzaBDqnJwar6uvrpb1gH36YFW1/gHrkydQyc+XDQZWP29q/XyqZmZAa7Suqa2VVdGMHSt7AVBNS5M178zNRe2gQagZMkRWlfOs/PwGh6IhgnsGirln6PTDX+fm5nJVPCoqKpBKpZg0aRLmzp3brIIcOnQIo0aNQu/evbllAoEAJc+0bweAkpIS6OrqNuvYAFD5+uuo9PGBUZ8+KFLmA9XQgNTUFFJT02afqy1IrK1R8eabHV0MQkgPpVTvISsrK6Q8HXDM0dERP/30Ey5dugT1Zo7keOjQIQQGBsotE4lEiI6O5t6Xl5dDLBZDJBI169gAwHR0wPT0mr0fIYQQJRPC2rVrERcXBwD4+OOPERAQgPHjx2Pjxo1Kn+jKlSvIyMjgHh7X8/PzQ0xMDMLCwlBVVYXNmzfD1dW1ZQ+UCSGEtJhSVUYLFizgfp80aRIKCwtRU1PToH9BUw4ePAh/f/8GVUEmJiYICwvDihUrEBAQAHd392Y/myCEEAKU15YjqSgJiUWJSCxKxN8H/x0aqo1P0NQYpZud5ufn47fffkNWVhbWrl2LvLw8FBUVwcrKSqn9d+/erXCdt7c34uPjlS0KIYT0aIVVhUgoTEBCUQISCxO5BJBVniW3nb+9P/oZ9lP6uEolhMjISEyfPh1Dhw7Fn3/+ibVr1yIxMRE7duzAzz//3LxICCGEKKWouggJhQmIL4jnEkBCYQKeVD5pdHt1FXX0EfZBX/2+cDBwgEBd+VocQMmEsHLlShw7dgxeXl4wMJDNU+Xu7o4bN24062SEEEIaqqyrlH3xF8YjNToVdzPvIr4wHtnl2Y1ur83XhqOBIxz0HeBo4ChLAPoOsNG1gapK4/2hlKFUQkhJSYGXlxcAcB3K1NXVUdfDOkMRQsjLqJXWIrk4GfEF8YgvjMfDgoeIL4zH45LHYGg4v4imqib6GfRDP8N+cNR3hKOBI/oZ9IOFwAIqvNYfYl6phODs7Izff/8dr732GrcsPDwcLi4urV4gQgjp6hhjyCjLQHxhPOIL4vGw8CHiCuIgLhKjRtpwAEo+jw97fXs4GTphkOUg2GjYoJ9hP9jo2rTJF78iSiWEnTt3YsqUKfDx8UFlZSUWL16Mn3/+GT/99FNbl48QQjotxhhyK3PxsPAhHhY8xMNC2RV/QmECymvLG93HRtcGjgaOcDJ0Qn+D/uhn2A/2Qnuoq8r6dXVk72ylEsLw4cMRHR2NH374AQsXLoS1tTVu3LihdAsjQgjpyhhjyK/Kx8PCh0goTJD7WVRd1Og+JlomDb74HfUdm/2gtz0plRDu3bsHV1dXrF27tq3LQwghHab+ir++KefDwodILJT9LKwubHQfobqQq9t3MnSS1fkb9IORllE7l/7lKZUQvLy8YGFhgfnz52Pu3LkwNzdv63IRQkibkTIp0kvTufb7SUVJSChMQFJREoprihvdR1dNFw4GDuhn0I9LAP0M+sFM20xuFsmuTKmEkJWVhV9//RVHjhzBxo0bMXLkSMyfPx/+/v7QpvH3CSGdVI2kBiklKbIv/sJErhdvUlESqiRVje4jVBfCwcCBa9VT/zLXNu82X/yKKJUQ+Hw+fH194evri+LiYhw/fhzbt2/H0qVL4efnh8WLF8PDw6Oty0oIIY2qrKuEuEjcoOduSnEK6ljjzeNNtUzRV7+vrD2/gQMc9GUvEy2Tbv/Fr0gzJvSVzV3w3//+F6GhoUhPT8fs2bNhY2ODuXPnwsfHB99++21blZMQQlBWU4bEokTuiz+hMAGJRYlIK01rtB0/DzzY6Nr89cWv78B14hJqCBs5Q8+mVEL49ddfcfjwYZw+fRoeHh5YtGgRpk2bBk1NTQDA8uXLYWNjQwmBENIq6nvuZmRm4Hbaba5FT0ZZRqPb83l89Bb2ll3l11/tGzjAXmgPLX4rTGjVQyiVENatW4fAwEB8/vnn6NWrV4P1hoaG+OKLL1q7bISQbq5OWoeUkhTEFcTJdeBS1HNXXUUd9vr23JANjgaOcNR3hJ3QDmoqah0QQfeiVEK4f//+C7dZtGjRSxeGENJ9FVQVIK4gDnEFcXiQ/wBxBXFIKExo9OFufc9dV3NX2Grbci177PTswFdpVk03aQaFn+xXX32FxYsXQ0ND8Vja1dXV2L17N9599902KRwhpOthjCG1NBWx+bFyr8zyzEa3txJYwcnQSfYykP2s77nbE+dU7kgKE0J2djb69u2LyZMnw9PTE/369YOuri5KS0uRkJCACxcu4PTp05g/f357lpcQ0olIpBKIi8W4n3cf9/PuIyY/BjF5MSitLW2wrRZfC06GTnA2dJa9jJzRz6AfPdztRBQmhE8//RR///vfceDAAezduxf3799HUVERDAwM4OrqismTJ+PTTz+FkZHyvfFCQ0OxadMmpKamwtzcHAcOHMDo0aMRERGB5cuXIzU1Fe7u7jhw4ABsbW1bJUBCSOuQSCV4VPwI0XnRuPfkHqLzohGbH4vKusoG25pqmUJkJILISARnI2eIjETordf7pYZmJm2Pxxhr+OSmDZw9exaLFi3CsWPH8MorryArSzazj4aGBuzt7bFnzx5MnToVGzZswKVLl3Dt2rUXHjMzs/Fb0J52m9nT4gUo5rZWX+1z98ld3H1yF/ee3MP9/PuNDthmJbCCi7ELBhgNkP00HgAzbbNWKQf9nVufhYWFwnXt9nRm48aN+PjjjzF8+HAAgKWlJQDgu+++g0gkwsyZMwEAwcHBMDY2Rnx8PJycnNqreIT0aAVVBYjKjeISQFRuVKNj91gKLOFm7AZXE1e4GrvCxdgFhpqGHVBi0hbaJSFIJBLcunULr7/+Ovr27YuqqipMmzYN//rXvxAbGws3NzduWx0dHdjb2yM2NpYSAiFtoFpSjdj8WETlRiEqNwp3cu/gcenjBtsZaRphkOkgDDQZCDcTN7gau8JYy7gDSkzaS7skhJycHNTW1uLEiRO4dOkS1NTU4Ovri61bt6KsrAwmJiZy2wuFQpSWNnwoFR4ejvDwcABASEgIjI0b/8fJ5/MVruuOelq8AMWsLMYYUktScSPjBq5nXseNjBuIyolCjUR+khZtNW0MNh+MYRbDMKzXMAy1GAobPZsOH8KB/s7tfO72OImWlqyn4N/+9jeuY9vf//53bN26FWPGjEFJSYnc9iUlJdDV1W1wHG9vb3h7e3PvFdWz9bR6x54WL0AxK1JeW47oJ9Hclf+d3DvIrcxtsJ2DvgMGmw7GINNBGGQ6CE4GTvLt+2uB/Pz81g6h2ejv3Ppa7RlCbm4uysrK5Jb16dPnhfsZGBjAysqq0asNkUiEgwcPcu/Ly8shFoshEomaUzRCehwpkyKpKAlRuVG4nXsbUblRiC+Mh5RJ5bbT19DHYNPBGGw6GENMh8DNxI2aepJGKZUQzpw5g6CgIGRnZ+PZRkk8Hg8SiUSpE7311lv4+uuvMXHiRKipqeGLL77AlClT4OfnhzVr1iAsLAw+Pj7YvHkzXF1d6fkBIc/JKsvCuZRzuPPkDqJyoxD9JBpltfIXaKo8VbgYu8iu/k0GYYjZEPTW693hVT+ka1AqISxfvhwbNmxAYGAgV/3TXBs2bEBeXh4cHR2hqamJN954Ax999BE0NTURFhaGFStWICAgAO7u7ggNDW3ROQjpLvIr83Ev757s9bTNf1Z5VoPteun0wiDTQRhiOgSDTQfDxdiFBnMjLaZUPwRDQ0Pk5+d3uqsM6ocg09PiBbpPzIwxZFdkIyYvBjH5MYjNj8X9vPtIL0tvsK2uui5cjV1l9f4mstY/5jrde/bC7vJ3bo5O/wwhKCgI+/fvx8KFC1utUIT0NNWSaiQVJXEje8bmxyImPwYFVQUNttXma8PF2AUuxi5ck89X7F9BQX7DbQlpLQoTwujRo7k7AsYYvvzyS4SEhDSYT/nixYttW0JCuhiJVILHpY+RUJiAhMIExBfGIy4/DuJiMSSs4TM3fQ19iIxEGGA0gPvZV79vg2EeVHgq7RUC6aEUJoTnh7Om4a0JkVcrrUVKcQo3T29iUSIeFj6EuEjc6JDOKjwV9BH2QX/D/txrgNEAWAosO111LOmZFCaEwMBA7vfr16/D3d29wTY3btxom1IR0omU1JRAXCRGUlESkoqTkFQoSwCPSx4rnK+3l04vbgx/JwMn9DfsDwcDB3rgSzo1pZ4hTJgwoUHnMQCYOHEiCgqoTpN0fYwxZJZnyr70n3mJi8XIqchpdJ9n5+vtq98XjvqO3CxeuuoNO1YS0tk1mRCkUikYY3KvemKxGHw+zVxEupb6+v3EQln1Tn1Vj7hIjIq6ikb30VTVRB9hH/TV78tN32ivb0/z9ZJup8lvdD6fDx6PB8ZYgy9/FRUVfPTRR21aOEJaqr45Z1xBHOLy4xBfGN9k/T4AGGsZo6+wL3fF76DvgL76fWEhsKAHuqRHaDIhJCcngzEGT09PudZEPB4PJiYmLe6kRkhrqpXWIqEwgWvLXz9vb1F1UaPb99LpJVe942DggL7CvjDQNGjfghPSyTSZEGxtbSGRSNC7d2+Ym5s3Ob8yIe2hRlKD+IJ4iNPEuJpyFTF5sgRQI61psK2+hj76G/aHs6EznAyduASgp67XASUnpPN74UMAVVVVJCcnQyqVvmhTQlpV/axdUblRiHoiG7s/Jj8G1ZLqBtva6dlxs3Y5Gzmjv2F/mGubU3NOQppBqafCGzduxNKlS7Fp06YGo5aqqFDdKmkd1ZJq3Mu7h1vZt3Ar5xZu5d5CXmXDLvx9hH3gbuUOR11HuBi7QGQkoqt+QlqBUgmhvlPa4cOHuWWMsWaNdkrI84qri3Ez5yauZ13HjZwbuPfkXoOqn2dn7RpsOhiuxq4w0DTokWPcENLWlEoIycnJbV0O0gPkV+bjevZ1XMu6hmvZ1/Ag/wEYnhlOHTw4GThhiNkQDDMbhqFmQ2GnZ0fVPoS0E6USgq2tbVuXg3RDxdXFuJZ1DX9m/YkrmVcQVxAnt15NRQ0DTQbCvZc73M3dMcR0CE3cQkgHUpgQ3nnnHXz33XcAgHnz5im8Sjt06FDblIx0OZV1lbiZfROXMi7hcuZlxOTHyM3epamqicGmgzGi1wgM7zUcg0wHUccuQjoRhQmhd+/e3O99+/Ztl8KQrkXKpIjJi8GljEu4mHERN3NuyrUAUlNRwzCzYRhpMRIeFh4YbDoYGqrUdJmQzkphQvjwww+53zdu3NguhSGdX3Z5NiIzIhGZHomL6RdRWF0ot97F2AWjLUZjlOUoDDMbBm017Q4qKSGkuZQejOjcuXM4evQoMjMzYWFhgdmzZ8PLy0vpE40dOxbXrl3jhsCwtLTEw4cPAQARERFYvnw5UlNT4e7ujgMHDtBzi06iWlKN69nXcSHtAiLTIxFfGC+33kpghTGWYzDKchRGW46GoaZhxxSUEPLSlEoIn332GUJCQvDWW29h0KBBSE1NxZw5c7B27VqsWrVK6ZN98803DeZVyMvLg7+/P/bs2YOpU6diw4YNmDVrFq5du9a8SEirSStNw7m0cziXdg5/Zv6JyrpKbp0WXwsje43EWKux8LTyRB9hH2oFREg3oVRC2LlzJ86dO4cBAwZwy+bNm4cJEyY0KyE05uTJkxCJRJg5cyYAIDg4GMbGxoiPj4eTk9NLHZsop1pSjRvZN7gkkFSUJLe+v2F/jLMaB08rTwwzH0bPAQjpppSuMnr+wXKfPs2/Mvzwww+xbt069OvXD5988gnGjh2L2NhYuLm5cdvo6OjA3t4esbGxDRJCeHg4wsPDAQAhISEwNjZu9Dx8Pl/huu6oJfGml6Tj90e/44z4DM6lnENZTRm3Tk9DD152XnjN/jW82udVWOpatnaRX1pP+xsDFHNP0ZExK0wIz45dFBwcjKCgIAQHB8PKygppaWnYsmULNm3apPSJtm3bBmdnZ6irqyM0NBRTp07F3bt3UVZWBhMTE7lthUIhSktLGxzD29sb3t7e3HtFPVV7Wi9WZeKtk9bhTu4dRKRGICItokGfgPq7gPE24zHUbCjUVNRkK6qBvOrO91n2tL8xQDH3FG0ds4WFhcJ1ChNC/VwIALiJcY4ePSq37Mcff1R6ruVnp+AMDAzE0aNH8dtvv0EgEDSYja2kpAS6ujTj1MsqqCrAhfQLiEiNwIX0C3LDQWvztTHacjTGW4/HOOtxsBR0vrsAQkj7UpgQ2nq4ivqJd0QiEQ4ePMgtLy8vh1gshkgkatPzd0eMMTwoeMDdBdzJvSPXMcxOzw5eNl7wtvaGey93ehZACJGjMCG0ZrPPoqIiXL9+HZ6enuDz+Th27BguXryIL774AoaGhlizZg3CwsLg4+ODzZs3w9XVlR4oK6mitgI/J/6MUzGncC7tHLLKs7h1aipqGNlrJLxsvOBl7QV7ffsOLCkhpLNrl0mRa2trsX79esTHx0NVVRVOTk7473//i379+gEAwsLCsGLFCgQEBMDd3R2hoaHtUawu63HJY+4u4GrWVbnewWbaZhhvPR7jrcdjjOUYCNQFHVhSQkhX0i4JwcTEBDdv3lS43tvbG/Hx8QrX93Q1khrczLnJJYFnm4XywMMrFq9gTK8x8LbxhshIRPP/EkJapF0SAmm+3IpcnE87j/C0cFxMv4iy2meaharrwdPKE17WXhhnPQ5O1k49riUGIaT1KZUQ3n33XXz11VcNlq9cuRJffPFFa5epR5IyKaKfRCMiLQIRqRG4l3dPbn0/g34Ybz0eXjZe8s1CCSGklfBYfZvSJujp6TVoGgoARkZGyM/Pb5OCKSMzM7PR5V2l7XJxdTEi0yMRkRaB82nnkV/112epqaqJkRZ/PRC21rVWeJyuEm9roph7Boq59bWoHwIA7Nu3DwBQV1fH/V7v0aNHPa4H4ctijCG+MB7nUmVDRNzMuQkJ+2sKUiuBFbxsvDDeejw8LDxorgBCSLtqMiHUz6FcU1MjN58yj8eDmZmZXP8B0rjy2nJczriMiLSIBs1C+Tw+RvQaAS9rWRJwNHCkgeIIIR2myYRw/vx5AMD69euxdevWdilQV8cYQ2JRIs6lncP5tPO4kX1DbuJ4Ey0TrlnoaMvRNGUkIaTTUOqh8rPJgDGGZx87qKhQE8eymjJcybrCJYH0snRuHQ88DDYdDC9rL3jZeFGzUEJIp6VUQsjMzMTy5ctx8eJFFBUVya2TSCSN79SN1T8LuJB2gXsWUCut5dYbaRphrNVYWecwqzE0aQwhpEtQKiEsXrwY2traiIiIgKenJy5evIjg4GBMnjy5rcvXaRRWFeJSxiVEpkfiQsYFZJdnc+tUeCoYYjoE46zHYZz1OLgau9JdACGky1EqIVy5cgWpqanQ0dEBj8eDm5sb9u7di5EjR+Ltt99u6zJ2iDppHaKeRMkSQPoFRD+JlhsozlTLFGOtx2Ks1ViaOpIQ0i0olRBUVVW5uZD19fXx5MkT6OnpISMjo00L197SStMQmR6JyIxI/JnxJ4prirl1aipqcDd3x1irsRhrPRYiQxG1CCKEdCtKJQR3d3f89ttv8PPzw2uvvYZZs2ZBS0sLQ4cObevytanSmlJcybyCixkXEZkeieQS+SG/7fTsuLmDPSw8oKOm00ElJYSQtqdUQjh8+DA3g9oXX3yBnTt3orS0FCtXrmzLsrW6+mqgS+mXEJkRiajcKLmOYXrqehhlMQpjrMZgjOUY2Oq13hDghBDS2SmVEPT19bnftbS0sH79+rYqT6tijEFcLMbljMu4mHERVzKvoLT2r6k5VXmqGGo2FJ6WnhhjNQYDTQaCr0Lj/RFCeqZu9+2XW5GLszFn8dvD33Ap45Jcz2AA6CPsgzGWsjuAERYjoKeu10ElJYSQzqVbJYRtN7fhq7vyo7IaaRphlOUojLYYjTFWY2juYEIIUaDdE0JiYiJcXFwwY8YMHDlyBAAQERGB5cuXIzU1Fe7u7jhw4ECLpvB0NHCEpqomxtiOgbuJO0ZbjkZ/w/7UJ4AQQpSg1DflvXv3XryRkpYvX45hw4Zx7/Py8uDv748tW7agoKAAQ4cOxaxZs1p07El2k/Ag8AF+nvUzlrguoWEiCCGkGZT6tvTy8oKbmxt27NiBrKysF++gQGhoKPT19eHl5cUtO3nyJEQiEWbOnAlNTU0EBwcjOjq6RVNqavI1oaGq0eLyEUJIT6ZUlVFWVhZ+/fVXHDlyBMHBwRg5ciTmz58Pf39/aGtrK3WikpISfPzxx4iIiMDevXu55bGxsXBzc+Pe6+jowN7eHrGxsXBycpI7Rnh4OMLDwwEAISEhCudj4PP5PWquhp4WL0Ax9xQUczufW6mN+Hz4+vrC19cXxcXFOH78OLZv346lS5fCz88PixcvhoeHR5PH2LBhA4KCgmBtLT/zV1lZGUxMTOSWCYVClJaW4nne3t7w9vbm3iuaVainzbLU0+IFKOaegmJufU3NmNasCvaysjL897//RWhoKNLT0zF79mw4ODhg7ty5WL58ucL97t69i/DwcLz//vsN1gkEggbTc5aUlEBXV7c5RSOEEPKSlLpD+PXXX3H48GGcPn0aHh4eWLRoEaZNmwZNTU0AsgfFNjY2+Pbbbxvd/8KFC0hJSYGNjQ0AWWKRSCR48OABlixZIjfzWnl5OcRiMUQi0cvGRgghpBmUSgjr1q3D/Pnz8fnnn6NXr14N1hsaGuKLL75QuP8777yD2bNnc+937NiBlJQU7Nq1CwCwZs0ahIWFwcfHB5s3b4arq2uD5weEEELallIJ4f79+y/cZtGiRQrXaWtryz18FggE0NTU5J4dhIWFYcWKFQgICIC7uztCQ0OVKRYhhJBWpNQzBH9/f1y6dElu2aVLlzBjxowWnTQ4OJjrlAbIHhbHx8ejsrISFy5cgJ2dXYuOSwghpOWUSgiRkZEYOXKk3LIRI0bg/PnzbVIoQggh7U+phKCpqYny8nK5ZWVlZVBTU2uTQhFCCGl/SiWE1157DYsXL+aah5aUlGDFihWYOHFimxaOEEJI+1EqIezcuRMlJSUwNDSEqakpDA0NUVxc3GTLIkIIIV2LUq2MDAwM8OuvvyI7OxtpaWmwtraGubl5W5eNEEJIO2pWT2UVFRUYGRmhoqICjx49wqNHj9qqXIQQQtqZUncIZ86cQVBQUIORTnk8HiQSiYK9CCGEdCVK3SEsX74cGzZsQHl5OaRSKfeiZEAIId2HUncIhYWFWLx4MXg8XluXhxBCSAdR6g4hKCgI+/fvb+uyEEII6UBK3SFcu3YNX331FUJCQhq0Lrp48WKbFIwQQkj7UiohLFq0qMnB6wghhHR9SiWEwMDAti4HIYSQDqZUQgCAnJwc3LhxA3l5eWCMccsXLlzYJgUjhBDSvpRKCP/9738REBAABwcHxMbGQiQSISYmBqNGjaKEQAgh3YRSrYzWr1+P/fv3IyoqCjo6OoiKisJ3332HIUOGtHX5CCGEtBOlEkJqaipmzpwptywwMBCHDh1S+kQBAQHo1asX9PT04OjoiD179nDrIiIi4OTkBG1tbYwbNw6PHz9W+riEEEJah1IJwdTUFDk5OQAAOzs7XL16FWKxuFk9lT/88EOkpKSgpKQE//vf/7B+/Xrcvn0beXl58Pf3x5YtW1BQUIChQ4di1qxZLYuGEEJIiymVEN5++21cvnwZAPD+++9j3LhxcHNzw9KlS5U+kUgkgoaGBgDZGEg8Hg9isRgnT56ESCTCzJkzoampieDgYERHRyM+Pr4F4RBCCGkpHnu2yZCSUlNTUV5ejv79+zdrv2XLluHAgQOorKzEoEGDcPHiRXz00UeoqanBrl27uO0GDBiATZs2Yfr06XL7h4eHIzw8HAAQEhKCmpqaRs/D5/NRV1fXzKi6rp4WL0Ax9xQUc+tTV1dXfG5lDuDr64uffvqJe29jYwMA8Pf3x8mTJ5UuyL///W98/fXXuHr1Ki5cuAANDQ2UlZXBxMREbjuhUIjS0tIG+3t7e8Pb25t7n5eX1+h5jI2NFa7rjnpavADF3FNQzK3PwsJC4TqlqozOnz/f6PILFy40uzCqqqoYNWoU0tPTsWvXLggEAm5qznolJSXQ1dVt9rEJIYS0XJN3CB9//DEAoKamhvu93qNHj2Bra9viE9fV1UEsFkMkEuHgwYPc8vLycm45IYSQ9tPkHUJaWhrS0tIglUq539PS0pCeng5ra2scP35cqZPk5uYiNDQUZWVlkEgk+P3333H06FGMHz8efn5+iImJQVhYGKqqqrB582a4urrCycmpVQIkhBCinCbvEOqHvB45ciTefvvtFp+Ex+Nh165dWLJkCaRSKWxtbfHFF1/A19cXABAWFoYVK1YgICAA7u7uCA0NbfG5CCGEtIxSD5U1NDRw7949uLq6csuio6Nx7949zJs374X7m5iYIDIyUuF6b29vamZKCCEdTKmHyhs2bIC1tbXcMmtra6xfv75NCkUIIaT9KZUQSkpKoKenJ7dMKBSiqKioLcpECCGkAyiVEJydnREWFia37NSpU83umEYIIaTzUuoZwrZt2zB58mQcO3YM9vb2SEpKQkREBH777be2Lh8hhJB2otQdwqhRoxATE4Nhw4ahvLwcr7zyCmJiYuDh4dHW5SOEENJOlJ4xzcbGBmvXrkVOTg569erVlmUihBDSAZS6QygqKsKcOXOgqamJvn37AgA3hDUhhJDuQamEsGTJEgiFQjx+/JgbKW/EiBE4duxYmxaOEEJI+1GqyigiIgKZmZlQU1MDj8cDIOtslpub26aFI4QQ0n6UukMQCoUNhmNNTU2lZwmEENKNKJUQFi1ahOnTp+P8+fOQSqW4evUqAgMDsWTJkrYuHyGEkHaiVJXRBx98AE1NTSxfvhy1tbVYuHAhFi9ejPfee6+ty0cIIaSdKJUQeDweVq5ciZUrV7ZxcQghhHQUpfshnDt3DkePHkVmZiYsLCwwe/ZseHl5tWXZCCGEtCOlniF89tlnmD17NgwNDeHj4wMjIyPMmTMHO3fubOvyEUIIaSdK3SHs3LkT586dw4ABA7hl8+bNw4QJE7Bq1ao2KxwhhJD2o9QdAgCuh3K9Pn36cH0SXqS6uhpBQUGwtbWFrq4uBg0ahNOnT3PrIyIi4OTkBG1tbYwbNw6PHz9WtliEEEJaiVIJITg4GEFBQUhMTERlZSUSEhLwzjvvYNOmTZBKpdxLkbq6OlhbWyMyMhLFxcXYsmUL3njjDaSkpCAvLw/+/v7YsmULCgoKMHToUMyaNavVAiSEEKIcHmOMvWgjFZW/8gaPx8Ozu9S/5/F4kEgkSp/Y1dUVGzduRH5+Pg4cOIArV64AAMrLy2FsbIyoqCg4OTk1eYzMzMxGlxsbGzfoSNed9bR4AYq5p6CYW5+FhYXCdUo9Q0hOTm61wgBATk4OEhISIBKJsGvXLri5uXHrdHR0YG9vj9jY2AYJITw8HOHh4QCAkJAQGBsbN3p8Pp+vcF131NPiBSjmnoJibudzK7NRr169uEHtnlVYWAgDA4NmnbC2thZz585FYGAgnJycUFZWBhMTE7lthEIhSktLG+zr7e0Nb29v7r2iLNrTrip6WrwAxdxTUMytr6k7BKWeIQwZMgT37t2TW3b69Gm4uLg0qyBSqRTz5s2Duro6vvnmGwCAQCBASUmJ3HYlJSXQ1dVt1rEJIYS8HKUSwrJlyzB27Fhs374dZWVlWLJkCd5++23s2bNH6RMxxhAUFIScnByEhYVBTU0NACASiRAdHc1tV15eDrFYDJFI1MxQCCGEvAylEsLSpUtx48YNHDhwAGZmZsjMzMS9e/cwceJEpU+0dOlSxMXF4eeff4aWlha33M/PDzExMQgLC0NVVRU2b94MV1fXFz5QJoQQ0rqUeoYglUrx448/IicnB5MmTcLVq1dx8+ZNvPbaa0qd5PHjx9i9ezc0NDRgbm7OLd+9ezfmzp2LsLAwrFixAgEBAXB3d0doaGjLonmqpqYG5eXlAKB0X4muLCcnB9XV1S/cjjEGVVVVaGpqtkOpCCFdjVIJwcPDAxoaGrhz5w5sbW3x22+/4a233oKfnx++/fbbF+5va2uLplq3ent7Iz4+XvlSN0EikSAvLw/a2to9IhkAslYJqqqqSm1bVVWF2tparsqOEELqKVVl5Ofnh/Pnz8PW1hYAMHnyZNy7dw/Z2dltWriWqKqqgpaWVo9JBs2loaGBmpqaji4GIaQTUiohrF27FjweD2lpabh27RoAWdOosLCwNi1cS1EyUIw+G0KIIkolhLS0NHh4eMDJyYnrB3DixAksWrSoTQvXEvSF92L0GRFCGqNUQnjnnXfg4+OD0tJSru55woQJOHv2bJsWjhBCSPtRKiHcuHED69atg4qKCnd1KRQKUVxc3KaFI3/ZuXMn/va3vwEAMjIy4ODg0Kyxowgh5EWUSghmZmZISkqSW/bgwQPY2Ni0SaG6M3d3d1y8ePGljmFpaYnExESlWxYRQogylEoIq1evxpQpU7B//37U1dXh6NGjmDVrFj744IO2Lh8hhJB2olRCWLhwIbZv347jx4/D2toahw4dwpYtWzB37ty2Ll+3dezYMUybNg2bN2+Gs7Mzhg8fjnPnznHrU1NTMX36dDg6OmL27NkoKCjg1qWlpcHS0hJ1dXUAgKNHj8LT0xOOjo4YMWIEDh8+3O7xEEK6PqU6pgHAtGnTMG3atDYsStuw/N6yXc6T8XZGs/eJiorCzJkzcf/+fRw5cgSrV6/G7du3wePxsHz5cgwZMgQ//vgjoqKiMH/+fIU9w42NjXHw4EHY2tri2rVrCAgIwMCBA5s9+CAhpGdTegpN0vqsrKwwd+5cqKqq4o033kBOTg6ePHmCjIwMREdHY+3atdDQ0MDw4cMxYcIEhceZMGEC7OzswOPxMGLECHh6euL69evtGAkhpDtQ+g6hq2rJlXt7eXYeiPoB/8rLy1FQUAChUAhtbW1uvZWVlcIZ4iIiIvCvf/0LycnJkEqlqKyspMEBCSHNRncInZCZmRmKi4tRUVHBLcvIaDyxVVdXIygoCEuWLMHdu3cRFxeH8ePHNzl2FCGENIYSQidkZWUFV1dX7NixAzU1Nbhx44bCToC1tbWorq6GkZER+Hw+zp07h8jIyHYuMSGkO1AqIVRXV+Ojjz5Cnz59IBQKAQB//PEHN+sZaX3ffvstoqKiIBKJ8Nlnn2HGjBmNbicQCPDJJ59gyZIlcHZ2xqlTp/Dqq6+2c2kJId0BjylRt7Bs2TJkZGRg3bp1mDRpEoqKipCRkYFXX30VsbGx7VHORjVWp15RUQE9PT2uSWZPwOfzmxVvRUWF3POJrojm2u0ZKObW19Scyko9VD516hSSkpKgo6MDFRXZTYWlpaXCem1CCCFdj1JVRurq6g2uQJ88eQIjIyOlT/TNN99g6NCh0NDQwIIFC+TWRUREwMnJCdra2hg3bhweP36s9HEJIYS0DqUSwsyZMxEYGIjk5GQAQFZWFlasWIHZs2crfSILCwusX78eCxculFuel5cHf39/bNmyBQUFBRg6dChmzZrVjBAIIYS0BqUSwqeffgo7Ozu4uLigqKgIDg4OsLCwwMaNG5U+kb+/P6ZNm9bgruLkyZMQiUSYOXMmNDU1ERwcjOjo6FabUpMQQohyFCaEZ1sQpaam4osvvkBZWRlycnJQWlqKzz//HOrq6i9dgNjYWLi5uXHvdXR0YG9v36EPqwkhpCdS+FD5o48+wooVKwAAgwcPRklJCQD53rWtoaysrMExhUIhSktLG2wbHh6O8PBwAEBISAiMjY0bbJOTkwNA1vKmJ2lOvBoaGo1+dl0Jn8/v8jE0F8XcM3RkzAq/Rezt7bFq1SqIRCLU1tZi3759jW73/DOB5hIIBFyyqVdSUgJdXd0G23p7e3NTeAJotGlWdXU1NDQ0qNlpE6qrq7t8Uz5qjtgzUMytr0XNTkNDQ7F9+3YcPXoUtbW1jQ6pzOPxXjohiEQiHDx4kHtfXl4OsVgMkUj0UsclhBDSPAoTgqOjI/bs2QMA8PLyQkRExEudqK6uDnV1dZBIJJBIJKiqqgKfz4efnx/WrFmDsLAw+Pj4YPPmzXB1daXB2QghpJ0p1croZZMBAGzduhVaWloICQnBkSNHoKWlha1bt8LExARhYWH46KOPYGBggOvXryM0NPSlz9dZWVpacs136z07XzIAfPXVVxg+fDgcHBwwZMgQLFmypMFxjh07BktLS/zvf/9r8zITQnoGhXcI/fv3R1xcHADA2toaPB6v0e1SU1OVOlFwcDCCg4MbXeft7U3NTJ/6z3/+g7CwMISGhsLOzg65ubn4448/Gmx3/Phx6Ovr4/jx4/D39++AkhJCuhuFCeH777/nfj9y5Ei7FIYA0dHR8PT0hJ2dHQDA1NQUAQEBctukp6fj2rVr2L17N5YuXYrc3FwYGhp2QGkJId2JwoQwatQo7ndPT88G6yUSCTZt2tTous7EwrJ9ptDMbKVxnQYPHoyPP/4YvXr1wsiRIzFgwACoqqrKbXP8+HG4ubnBx8cHDg4OOHnyJBYtWtQq5yeE9Fwtng+hrq4On3zySWuWhQCYPn06tmzZggsXLmD69OlwdXVtMMz4iRMnuPmtp02bhmPHjnVASQkh3c1L9d7qCrNytdaVe2tRVVVt0GegtrZWrmOZv78//P39UVtbizNnzuBvf/sbBgwYgLFjx+LmzZtIS0uDr68vAMDPzw/btm1DTEwMBgwY0K6xEEK6l5eaMU3Rg2aimKWlJdLS0uSWpaWlwcrKqsG2ampqmDp1Kvr37889dD9+/DgYY3j11VcxcOBATJkyBYDsroEQQl5Gk3cI586dU7iupqam1QvTE7z++uv48ssv4eTkBHNzc1y+fBlnz57lmo8eO3YMRkZGGD58OLS1tXHhwgUkJCRg8ODBqKqqws8//4zt27fDy8uLO+aZM2ewc+dOrF+/vscN2UEIaT1NfnsEBQU1ubONjU2rFqYnWLlyJXbs2AE/Pz8UFxfD1tYW33zzDdcRT1dXF19//TXeffddSCQSWFlZ4dNPP8Urr7yCn376CZqampgxYwbU1NS4Y86ZMwfbt2/H+fPnMWHChI4KjRDSxSk1hWZnRVNoytAUmj0DxdwzdORYRi/1DIEQQkj3QQmBEEIIAEoIhBBCnup2CaELPxJpN/QZEUIa0+0SAkBfeE2hz4YQoki3SwiampqorKykLz4FqqurW2UubEJI99PtejGpqqrCwMAAWVlZAHpGb2oNDQ1UV1e/cDvGGFRVVeX6MBBCSL1ulxAAQF1dHTo6Oh1djHbTE9tqE0JaX7erMiKEENIynSYhFBQUwM/PDzo6OrC1tcWPP/7Y0UUihJAepdNUGS1fvhzq6urIycnB3bt34ePjAzc3N4hEoo4uGiGE9Aid4g6hvLwcYWFh2LJlCwQCAUaNGoXXX38dhw8f7uiiEUJIj9Ep7hASEhKgqqoKR0dHbpmbmxsiIyPltgsPD0d4eDgAICQkpMlBmppa1x31tHgBirmnoJjbT6e4QygrK4NQKJRbJhQKUVpaKrfM29sbISEhCAkJafJ469ata/UydmY9LV6AYu4pKOb21SkSgkAgQElJidyykpIS6OrqdlCJCCGk5+kUCcHR0RF1dXVITEzklkVHR9MDZUIIaUedIiHo6OjA398fH3/8McrLy/Hnn3/ip59+wrx581p0PG9v71YuYefW0+IFKOaegmJuX51mxrSCggIsXLgQZ8+ehZGREUJCQjBnzpyOLhYhhPQYnSYhEEII6VidosqIEEJIx6OEQAghBEAnTwjffPMNhg4dCg0NDSxYsEBu3Z49e9C3b18IBAJMnDgRmZmZcuvv3LmDMWPGQCAQwMzMDF9++SW3LiUlBePGjYO2tjacnJy4zm4draXxTpo0CQKBgHupq6vDxcWFW99Z4wVaHnN1dTWWLFkCMzMzGBoaYurUqcjIyODWd8eYi4qKEBgYCFNTU5iamiI4OFhu384cc3V1NYKCgmBrawtdXV0MGjQIp0+f5tZHRETAyckJ2traGDduHB4/fsytY4zhgw8+gJGREYyMjLB27Vq5+U46a9wvE/P58+cxbtw4CIVC2NnZNTh2m8XMOrGwsDB26tQptmTJEhYYGMgtv3DhAjMxMWExMTGsurqaLVmyhI0ZM4Zb/+TJE2ZiYsKOHDnCqqqqWElJCXvw4AG3fvjw4ez9999nFRUV7MSJE0woFLLc3Nz2DK1RLY33eZ6enmzTpk3c+84aL2Mtj3nbtm3M1dWVZWdns8rKShYQEMD8/Py49d0x5gULFrAZM2aw8vJylpyczPr06cP27dvHre/MMZeVlbGNGzey5ORkJpFI2M8//8wEAgFLTk5mT548YXp6euw///kPq6ysZKtXr2bu7u7cvv/3f//HHB0dWVpaGktPT2f9+/dnu3bt4tZ31rhfJubr16+zQ4cOsd27dzNbW9sGx26rmDt1Qqj30Ucfyf3HWbVqFVu2bBn3PiMjgwFgSUlJjDHGPvzwQxYQENDosR4+fMjU1dVZSUkJt2zUqFFy/8A6WnPjfVZycjJTUVFhjx49Yox1jXgZa37MS5YsYWvWrOHW//LLL8zR0ZEx1n1jNjIyYjdu3ODWf/LJJ2zUqFGMsa4T87NcXFzYiRMn2O7du9mIESO45WVlZUxTU5PFxcUxxhgbMWIE2717N7d+z5493JdnV4tb2ZjrnT17tkFCaMuYO3WVkSJMlsjk3gNATEwMAODatWswNDTEyJEjYWpqiqlTpyI1NRUAEBsbiz59+sj1gnZzc0NsbGw7RtA8L4r3WYcOHcLo0aPRu3dvAF0zXuDFMQcFBeHPP/9EZmYmKioq8MMPP2DSpEkAum/Mzy6r/71+XVeLOScnBwkJCRCJRIiNjYWbmxu3TkdHB/b29lzZn1//bFxdKe7mxNyUtoy5SyaEyZMn4z//+Q/u3buHyspKbN68GTweDxUVFQCA9PR0HDx4EF9++SVSU1PRu3dvvPnmmwCUHzepM3lRvM86dOiQXL10V4wXeHHMjo6OsLGxgaWlJfT09BAXF4ePP/4YQPeNeeLEiQgJCUFpaSmSkpKwb98+bl1Xirm2thZz585FYGAgnJycXlj259cLhUKUlZWBMdZl4m5uzE1py5i7ZELw8vLCpk2bMH36dNja2sLOzg66urqwsrICAGhpacHPzw/Dhg2DpqYmNm7ciCtXrqC4uLhLjpv0onjrXb58GdnZ2ZgxYwa3rCvGC7w45qVLl6Kqqgr5+fkoLy+Hv78/d4fQXWP+6quvoKWlBQcHB/j6+uLNN9/k1nWVmKVSKebNmwd1dXV88803AF5c9ufXl5SUQCAQgMfjdYm4WxJzU9oy5i6ZEADZhDqJiYnIzc3F9OnTUVdXhwEDBgAAXF1dwePxuG3rf2eMQSQS4dGjR3LZtCuMm9RUvPUOHjwIf39/CAQCbllXjRdoOubo6GgsWLAAhoaG0NDQwN/+9jfcuHEDeXl53TZmQ0ND/PDDD8jOzkZsbCykUileeeUVAF3j78wYQ1BQEHJychAWFgY1NTUAsrJHR0dz25WXl0MsFnNlf379s3F19rhbGnNT2jTml34K0YZqa2tZZWUlW7duHQsICGCVlZXcsvv37zOpVMoeP37MPD092YcffsjtFxERwfT19VlUVBSrqalhK1eu5B6+McaYu7s7W7VqFausrGQnT57sNK0SWhovY4xVVFQwoVDIIiIiGhy3s8bLWMtjXrBgAfP392dFRUWspqaGffLJJ8zCwoJb3x1jTkpKYnl5eayuro799ttvzMjIiMXExHDrO3PMjDG2ePFi5u7uzkpLS+WW5+bmMj09PXbixAlWWVnJ1q5dK9fiZteuXczJyYmlp6ezjIwM5uzsLPcAtTPH3dKYJRIJq6ysZL/99huzsbFhlZWVrLq6mlvfVjF36oSwceNGBkDutXHjRlZYWMhcXFyYtrY2MzMzY+vWrWN1dXVy+/773/9mFhYWTF9fn02ZMoWlpqZy65KTk5mnpyfT1NRkjo6O7OzZs+0dWqNeJt4ff/yR2djYMKlU2uC4nTVexloec15eHpszZw4zMTFhQqGQeXh4sOvXr3Pru2PMx44dY7169WJaWlrMzc2NnTlzRu64nTnmlJQUBoBpaGgwHR0d7nXkyBHGmKw1Tb9+/Zimpibz9PRkycnJ3L5SqZStWbOGGRgYMAMDA7ZmzRq5f+edNe6Xifn8+fMN/o14enpy69sqZhrLiBBCCIAu/AyBEEJI66KEQAghBAAlBEIIIU9RQiCEEAKAEgIhhJCnKCEQQggBQAmBEELIU5QQCFEgICAAAwcORE1NjdzyiIgIqKmp4cqVKx1UMkLaBiUEQhT45ptvkJ+fj02bNnHLSkpKsHDhQqxZswYjR45s1fM9n3gIaW+UEAhRQF9fH/v378f27dtx48YNAMD7778PAwMDLF68GLNnz4aBgQEMDAzg4+ODxMREbl+xWAxfX1+Ym5tDR0cHgwcPxi+//CJ3fDs7OwQHB2PhwoXQ19fH3Llz2zU+Qp5HCYGQJnh7e2Pp0qWYP38+Tpw4gR9++AEHDx7EhAkToKmpicjISFy9ehW9evWCt7e33PwEkyZNwtmzZxEdHY3p06fD398f8fHxcsf/7LPP4OTkhFu3buHTTz/tiBAJ4dBYRoS8QGVlJQYNGoTExESEhITAyMgI//znP5GQkMANrS6RSGBqaopdu3bhjTfeaPQ4w4cPx5QpU7B+/XoAsjsEFxcX/Pzzz+0WCyFNoTsEQl5AS0sLq1evhoaGBlatWoXbt28jOTkZurq6EAgEEAgEEAqFKCwshFgsBiAb337t2rVwdnaGgYEBBAIBbt26xU3lWm/o0KEdERIhjeJ3dAEI6Qr4fD5UVFSgoqICqVSKgQMHIjQ0tMF2hoaGAIDVq1fjzJkz2LFjBxwcHKCtrY358+c3eHCso6PTLuUnRBmUEAhppsGDB+Po0aMwNjaGvr5+o9tcvnwZ8+fPx/Tp0wEAVVVVEIvFcHR0bMeSEtI8VGVESDPNnTsXZmZm8PX1RWRkJJKTk3Hx4kWsWrWKa2nk6OiIU6dO4c6dO7h//z4CAgJQVVXVwSUnpGmUEAhpJm1tbVy8eBF9+vTBzJkz4eTkhMDAQBQWFsLAwACArPWQqakpRo8ejUmTJmH48OEYPXp0B5eckKZRKyNCCCEA6A6BEELIU5QQCCGEAKCEQAgh5ClKCIQQQgBQQiCEEPIUJQRCCCEAKCEQQgh5ihICIYQQAMD/A1eEZByYQ3ukAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "style.use('ggplot')\n",
    "plt.plot(df_LE['Year'].values, df_LE['Value'].values,'g',label=\"India\", linewidth=2)\n",
    "plt.plot(df_LE_2['Year'].values, df_LE_2['Value'].values,'r',label=\"USA\", linewidth=2)\n",
    "\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year',size=14,color='k')\n",
    "#plt.ylabel(df_LE['IndicatorName'].iloc[0],size=12)\n",
    "plt.ylabel('Life expectancy at birth (years)',size=12,color='k')\n",
    "\n",
    "#label the figure\n",
    "plt.title('Life expectancy in India and the USA', size=14,color='k')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "#show legend\n",
    "plt.legend(fontsize=12)\n",
    "plt.grid(True, color='w')\n",
    "\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "plt.axis([1959, 2014,0,83])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between Life expectancy and GDP per capita in India"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select GDP Per capita  for India\n",
    "hist_indicator_G = 'GDP per capita \\(constant 2005'\n",
    "hist_country_G = 'IND'\n",
    "\n",
    "mk_G1 = df_indicator['IndicatorName'].str.contains(hist_indicator_G) \n",
    "mk_G2 = df_indicator['CountryCode'].str.contains(hist_country_G)\n",
    "\n",
    "gdp_India = df_indicator[mk_G1 & mk_G2]\n",
    "\n",
    "#plot gdp_India vs df_LE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "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>CountryName</th>\n",
       "      <th>CountryCode</th>\n",
       "      <th>IndicatorName</th>\n",
       "      <th>IndicatorCode</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11616</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1960</td>\n",
       "      <td>228.304470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36555</th>\n",
       "      <td>India</td>\n",
       "      <td>IND</td>\n",
       "      <td>GDP per capita (constant 2005 US$)</td>\n",
       "      <td>NY.GDP.PCAP.KD</td>\n",
       "      <td>1961</td>\n",
       "      <td>232.142053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CountryName CountryCode                       IndicatorName  \\\n",
       "11616       India         IND  GDP per capita (constant 2005 US$)   \n",
       "36555       India         IND  GDP per capita (constant 2005 US$)   \n",
       "\n",
       "        IndicatorCode  Year       Value  \n",
       "11616  NY.GDP.PCAP.KD  1960  228.304470  \n",
       "36555  NY.GDP.PCAP.KD  1961  232.142053  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_India.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# switch to a line plot\n",
    "plt.plot(gdp_India['Year'].values, gdp_India['Value'].values,linewidth=2)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('Year',fontsize=12,color='k')\n",
    "plt.ylabel(gdp_India['IndicatorName'].iloc[0],fontsize=12,color='k')\n",
    "\n",
    "#label the figure\n",
    "plt.title('GDP per capita of India')\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "# to make more honest, start they y axis at 0\n",
    "plt.axis([1959, 2015,0,1270])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GDP per capita increases with years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw a scatter plot to see corelation between GDP per capita and life expectancy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n",
      "54\n"
     ]
    }
   ],
   "source": [
    "gdp_India_Trunc = gdp_India[gdp_India['Year'] < 2014]\n",
    "print(len(gdp_India_Trunc))\n",
    "print(len(df_LE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "# Grid lines, Xticks, Xlabel, Ylabel\n",
    "\n",
    "axis.yaxis.grid(True)\n",
    "axis.set_title('Life expectancy vs. GDP per capita in India',fontsize=14, color='k')\n",
    "axis.set_xlabel(gdp_India_Trunc['IndicatorName'].iloc[0],fontsize=12, color='k')\n",
    "axis.set_ylabel('Life expectancy at birth (years)',color='k',fontsize=12)\n",
    "plt.xticks(fontsize=12,color='k')\n",
    "plt.yticks(fontsize=12,color='k')\n",
    "\n",
    "X = gdp_India_Trunc['Value']\n",
    "Y = df_LE['Value']\n",
    "\n",
    "\n",
    "axis.scatter(X, Y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows a good relationship. But how strong is it? We can test this by looking at correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.84850089],\n",
       "       [0.84850089, 1.        ]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(gdp_India_Trunc['Value'],df_LE['Value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The correlation of 0.85 is pretty strong.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We come to the End of the Jupiter notebook. Thank You!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.25em;color:green; font-style:bold\"><br>\n",
    "Homework </p>  How about doing the same in the case of Nepal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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