1. Basic Functionality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>All neccessary documentation can be found here:</b><br>\n",
    "https://pandas.pydata.org/docs/getting_started/index.html\n",
    "\n",
    "Access the link and on the page review everything highlighted\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>After familiarizing with basics review everything under USER GUIDE</b><br>\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Data Structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "https://pandas.pydata.org/docs/getting_started/dsintro.html"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>From np.array</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.266778\n",
       "b   -0.856062\n",
       "c    1.559315\n",
       "d   -1.092689\n",
       "e    0.758042\n",
       "dtype: float64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(5),index=list('abcde'))\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>From dictionary</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a': 0., 'b': 1., 'c': 2.}\n",
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "d    NaN\n",
       "e    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d,index=list('abcde'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    4\n",
       "b    4\n",
       "c    4\n",
       "d    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(4, index = list('abcd'))\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>A Series is like a fixed-size dict in that you can get and set values by index label:</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seeding random number generator to get the same randon numbers\n",
    "np.random.seed(100)\n",
    "\n",
    "s=pd.Series(np.random.randint(0,10,5), index=list('abcde'))\n",
    "s['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Series is similar to np arrays and support similar slicing as lists</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cheking variable type within series\n",
    "s.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>When working with raw NumPy arrays, looping through value-by-value is usually not necessary. The same is true when working with Series in pandas. Series can also be passed into most NumPy methods expecting an ndarray.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    16\n",
       "b    16\n",
       "c     6\n",
       "d    14\n",
       "e    14\n",
       "dtype: int32"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    24\n",
       "b    24\n",
       "c     9\n",
       "d    21\n",
       "e    21\n",
       "dtype: int32"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s * 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "<b>Series operations will align rows based on row indecies, so no need to worry about lining out the rows</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    8\n",
       "c    3\n",
       "d    7\n",
       "e    7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    8\n",
       "b    8\n",
       "c    3\n",
       "d    7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     NaN\n",
       "b    16.0\n",
       "c     6.0\n",
       "d    14.0\n",
       "e     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:] + s[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## DataFrame"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'one': a    1.0\n",
       " b    2.0\n",
       " c    3.0\n",
       " dtype: float64,\n",
       " 'two': a    1.0\n",
       " b    2.0\n",
       " c    3.0\n",
       " d    4.0\n",
       " dtype: float64}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'one': pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "     'two': pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": 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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "hidden": 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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0  1.0  4.0\n",
       "1  2.0  3.0\n",
       "2  3.0  2.0\n",
       "3  4.0  1.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'one': [1., 2., 3., 4.],\n",
    "     'two': [4., 3., 2., 1.]}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": 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>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "A  1.0  4.0\n",
       "B  2.0  3.0\n",
       "C  3.0  2.0\n",
       "D  4.0  1.0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d,index=list('ABCD'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hidden": 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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">a</th>\n",
       "      <th colspan=\"2\" halign=\"left\">b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>b</th>\n",
       "      <th>a</th>\n",
       "      <th>c</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">A</th>\n",
       "      <th>B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       a              b      \n",
       "       b    a    c    a     b\n",
       "A B  1.0  4.0  5.0  8.0  10.0\n",
       "  C  2.0  3.0  6.0  7.0   NaN\n",
       "  D  NaN  NaN  NaN  NaN   9.0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({('a', 'b'): {('A', 'B'): 1, ('A', 'C'): 2},\n",
    "    ('a', 'a'): {('A', 'C'): 3, ('A', 'B'): 4},\n",
    "    ('a', 'c'): {('A', 'B'): 5, ('A', 'C'): 6},\n",
    "    ('b', 'a'): {('A', 'C'): 7, ('A', 'B'): 8},\n",
    "    ('b', 'b'): {('A', 'D'): 9, ('A', 'B'): 10}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attributes and underlying data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Common attributes:</b>"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    df.columns\n",
    "    df.index\n",
    "    df.info()\n",
    "    df.head()\n",
    "    df.tail()\n",
    "    df.dtypes\n",
    "    df.s.astype()\n",
    "    df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(np.random.randn(4,3),columns=list('abc'),index=range(1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    float64\n",
       "b    float64\n",
       "c    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 1 to 4\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   a       4 non-null      float64\n",
      " 1   b       4 non-null      float64\n",
      " 2   c       4 non-null      float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 228.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.282432</td>\n",
       "      <td>0.606086</td>\n",
       "      <td>-0.509986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.687711</td>\n",
       "      <td>1.151077</td>\n",
       "      <td>0.630561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.179703</td>\n",
       "      <td>-0.234715</td>\n",
       "      <td>-1.439737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.593651</td>\n",
       "      <td>0.007750</td>\n",
       "      <td>-0.632766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.191420</td>\n",
       "      <td>0.177294</td>\n",
       "      <td>-0.243205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.119799</td>\n",
       "      <td>0.775630</td>\n",
       "      <td>-0.120425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.432817</td>\n",
       "      <td>2.304473</td>\n",
       "      <td>-0.113795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              a         b         c\n",
       "count  4.000000  4.000000  4.000000\n",
       "mean  -0.282432  0.606086 -0.509986\n",
       "std    0.687711  1.151077  0.630561\n",
       "min   -1.179703 -0.234715 -1.439737\n",
       "25%   -0.593651  0.007750 -0.632766\n",
       "50%   -0.191420  0.177294 -0.243205\n",
       "75%    0.119799  0.775630 -0.120425\n",
       "max    0.432817  2.304473 -0.113795"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c'], dtype='object')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=1, stop=5, step=1)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Column names can be changed as following</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.179703</td>\n",
       "      <td>0.088571</td>\n",
       "      <td>-0.363776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.398300</td>\n",
       "      <td>-0.234715</td>\n",
       "      <td>-1.439737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -1.179703  0.088571 -0.363776\n",
       "2 -0.398300 -0.234715 -1.439737"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=[x.upper() for x in df.columns]\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.179703</td>\n",
       "      <td>0.088571</td>\n",
       "      <td>-0.363776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.398300</td>\n",
       "      <td>-0.234715</td>\n",
       "      <td>-1.439737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "1 -1.179703  0.088571 -0.363776\n",
       "2 -0.398300 -0.234715 -1.439737"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:2]\n",
    "#slice first 2 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.179703</td>\n",
       "      <td>0.088571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.398300</td>\n",
       "      <td>-0.234715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.432817</td>\n",
       "      <td>2.304473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.015459</td>\n",
       "      <td>0.266016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B\n",
       "1 -1.179703  0.088571\n",
       "2 -0.398300 -0.234715\n",
       "3  0.432817  2.304473\n",
       "4  0.015459  0.266016"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A','B']]\n",
    "#slice columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>To get the actual data inside index or col use .array or .to_numpy()</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "[1, 2, 3, 4]\n",
       "Length: 4, dtype: int64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>a</th>\n",
       "      <td>1.011066</td>\n",
       "      <td>1.376996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-1.440771</td>\n",
       "      <td>-0.230285</td>\n",
       "      <td>0.271005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.980569</td>\n",
       "      <td>0.480924</td>\n",
       "      <td>2.140763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.266911</td>\n",
       "      <td>0.475369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three\n",
       "a  1.011066  1.376996       NaN\n",
       "b -1.440771 -0.230285  0.271005\n",
       "c  0.980569  0.480924  2.140763\n",
       "d       NaN -1.266911  0.475369"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "'one': pd.Series(np.random.randn(3), index=['a', 'b', 'c']),\n",
    "'two': pd.Series(np.random.randn(4), index=['a', 'b', 'c', 'd']),\n",
    "'three': pd.Series(np.random.randn(3), index=['b', 'c', 'd'])})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.451837</td>\n",
       "      <td>1.607281</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>2.421340</td>\n",
       "      <td>0.711209</td>\n",
       "      <td>1.869758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.036625</td>\n",
       "      <td>0.204364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three\n",
       "a  2.451837  1.607281       NaN\n",
       "b  0.000000  0.000000  0.000000\n",
       "c  2.421340  0.711209  1.869758\n",
       "d       NaN -1.036625  0.204364"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row=df.iloc[1]\n",
    "col=df['two']\n",
    "df.sub(row, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>a</th>\n",
       "      <td>-0.365930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-1.210486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.499645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.659839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.742280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one  two     three\n",
       "a -0.365930  0.0       NaN\n",
       "b -1.210486  0.0  0.501290\n",
       "c  0.499645  0.0  1.659839\n",
       "d       NaN  0.0  1.742280"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(col,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "multiIndex=list(zip(np.arange(2).repeat(2),list('abcd')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index=pd.MultiIndex.from_tuples(multiIndex, names=['first','second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>a</th>\n",
       "      <td>-0.365930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-1.210486</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.501290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>c</th>\n",
       "      <td>0.499645</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.659839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.742280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   one  two     three\n",
       "first second                         \n",
       "0     a      -0.365930  0.0       NaN\n",
       "      b      -1.210486  0.0  0.501290\n",
       "1     c       0.499645  0.0  1.659839\n",
       "      d            NaN  0.0  1.742280"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(col,axis=0,level=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.get_level_values(0).unique().to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">0</th>\n",
       "      <th>a</th>\n",
       "      <td>0.011066</td>\n",
       "      <td>0.376996</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-2.440771</td>\n",
       "      <td>-1.230285</td>\n",
       "      <td>-0.728995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>c</th>\n",
       "      <td>-4.019431</td>\n",
       "      <td>-4.519076</td>\n",
       "      <td>-2.859237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.266911</td>\n",
       "      <td>-4.524631</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   one       two     three\n",
       "first second                              \n",
       "0     a       0.011066  0.376996       NaN\n",
       "      b      -2.440771 -1.230285 -0.728995\n",
       "1     c      -4.019431 -4.519076 -2.859237\n",
       "      d            NaN -6.266911 -4.524631"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(pd.Series([1,5],index=df.index.get_level_values(0).unique())\n",
    "       ,axis=0,level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Data"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    df.replace()\n",
    "    df.fillna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.droplevel(0,axis=0)\n",
    "df2=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.022132</td>\n",
       "      <td>2.753993</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-2.881542</td>\n",
       "      <td>-0.460570</td>\n",
       "      <td>0.542010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>1.961138</td>\n",
       "      <td>0.961848</td>\n",
       "      <td>4.281526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>-2.533821</td>\n",
       "      <td>0.950738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              one       two      three\n",
       "second                                \n",
       "a        2.022132  2.753993  20.000000\n",
       "b       -2.881542 -0.460570   0.542010\n",
       "c        1.961138  0.961848   4.281526\n",
       "d       20.000000 -2.533821   0.950738"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.replace(np.nan,10,inplace=True)\n",
    "df.add(df2,fill_value=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean Reduction "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    empty\n",
    "    any()\n",
    "    all()\n",
    "    bool()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "second\n",
       "a    False\n",
       "b    False\n",
       "c     True\n",
       "d    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df>0).all(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df>0).all().all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([0,1,1,1,0]).map(lambda x: bool(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one   two  three\n",
       "second                    \n",
       "a        True  True  False\n",
       "b        True  True   True\n",
       "c        True  True   True\n",
       "d       False  True   True"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df\n",
    "df==df2\n",
    "#can't compare Nans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one   two  three\n",
       "second                    \n",
       "a        True  True  False\n",
       "b        True  True   True\n",
       "c        True  True   True\n",
       "d       False  True   True"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.eq(df2) #compares elemnt wise same as =="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to compare nan's\n",
    "df.equals(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['foo','bar','barz'])=='foo'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combining overlapping tables"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    pd.concat() \n",
    "    df.append()\n",
    "    df/pd.merge()\n",
    "    df.combine_first()\n",
    "    df.update(df2)\n",
    "    df.transform()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': [1., np.nan, 3., 5., np.nan],\n",
    "'B': [np.nan, 2., 3., np.nan, 6.]})\n",
    "\n",
    "df2 = pd.DataFrame({'A': [5., 2., 4., np.nan, 3., 7.],\n",
    "'B': [np.nan, np.nan, 3., 4., 6., 8.]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B\n",
       "0  1.0  NaN\n",
       "1  NaN  2.0\n",
       "2  3.0  3.0\n",
       "3  5.0  NaN\n",
       "4  NaN  6.0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B\n",
       "0  5.0  NaN\n",
       "1  2.0  NaN\n",
       "2  4.0  3.0\n",
       "3  NaN  4.0\n",
       "4  3.0  6.0\n",
       "5  7.0  8.0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['d', 'c', 'b', 'a'], dtype='object', name='second')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=df.index[::-1]\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=df1.combine_first(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B\n",
       "0  1.0  NaN\n",
       "1  2.0  2.0\n",
       "2  3.0  3.0\n",
       "3  5.0  4.0\n",
       "4  3.0  6.0\n",
       "5  7.0  8.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive Statistics"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    sum()\n",
    "    mean()\n",
    "    quantile()\n",
    "    cumsum()\n",
    "    cumprod()\n",
    "    value_counts()\n",
    "    idxmax()\n",
    "    idxmin()\n",
    "    pd.cut()\n",
    "    describe()\n",
    "    cut()\n",
    "    qcut()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      0.183621\n",
       "two      0.090181\n",
       "three    0.962379\n",
       "dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "second\n",
       "d    1.194031\n",
       "c   -0.466684\n",
       "b    1.200752\n",
       "a   -0.395771\n",
       "dtype: float64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      0.550864\n",
       "two      0.360725\n",
       "three    2.887137\n",
       "dtype: float64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one           NaN\n",
       "two      0.360725\n",
       "three         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum(skipna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one  two  three\n",
       "second                 \n",
       "d         0    1      2\n",
       "c         3    4      5\n",
       "b         6    7      8\n",
       "a         9   10     11"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,:]=np.arange(12).reshape(4,3)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      4.5\n",
       "two      5.5\n",
       "three    6.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>-4.5</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one  two  three\n",
       "second                 \n",
       "d      -4.5 -4.5   -4.5\n",
       "c      -1.5 -1.5   -1.5\n",
       "b       1.5  1.5    1.5\n",
       "a       4.5  4.5    4.5"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(df.mean(axis=0),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df.isnull()]=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one  two  three\n",
       "second                 \n",
       "d         0    1      2\n",
       "c         3    4      5\n",
       "b         6    7      8\n",
       "a         9   10     11"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "      <td>57</td>\n",
       "      <td>114</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>107</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>92</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107</td>\n",
       "      <td>132</td>\n",
       "      <td>71</td>\n",
       "      <td>132</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a    b   c    d    e\n",
       "0   64   28  57  114   48\n",
       "1  110   39  19   18   73\n",
       "2   14  107  31   18   54\n",
       "3   69   92  79    1   14\n",
       "4  107  132  71  132  139"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.randint(1,145,(40,5)),columns=list('abcde'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73.900000</td>\n",
       "      <td>65.650000</td>\n",
       "      <td>65.375000</td>\n",
       "      <td>82.725000</td>\n",
       "      <td>79.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.795758</td>\n",
       "      <td>38.152561</td>\n",
       "      <td>34.116458</td>\n",
       "      <td>44.209865</td>\n",
       "      <td>43.176969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>36.500000</td>\n",
       "      <td>36.750000</td>\n",
       "      <td>41.250000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>46.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>89.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>100.500000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>129.250000</td>\n",
       "      <td>116.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                a           b           c           d           e\n",
       "count   40.000000   40.000000   40.000000   40.000000   40.000000\n",
       "mean    73.900000   65.650000   65.375000   82.725000   79.425000\n",
       "std     42.795758   38.152561   34.116458   44.209865   43.176969\n",
       "min      1.000000    1.000000    9.000000    1.000000    3.000000\n",
       "25%     36.500000   36.750000   41.250000   39.000000   46.750000\n",
       "50%     77.000000   62.000000   66.500000   86.500000   89.500000\n",
       "75%    111.000000  100.500000   89.000000  129.250000  116.250000\n",
       "max    143.000000  139.000000  124.000000  144.000000  144.000000"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73.900000</td>\n",
       "      <td>65.650000</td>\n",
       "      <td>65.375000</td>\n",
       "      <td>82.725000</td>\n",
       "      <td>79.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.795758</td>\n",
       "      <td>38.152561</td>\n",
       "      <td>34.116458</td>\n",
       "      <td>44.209865</td>\n",
       "      <td>43.176969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>33.800000</td>\n",
       "      <td>30.800000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>42.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>89.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>100.500000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>129.250000</td>\n",
       "      <td>116.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>143.000000</td>\n",
       "      <td>139.000000</td>\n",
       "      <td>124.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>144.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                a           b           c           d           e\n",
       "count   40.000000   40.000000   40.000000   40.000000   40.000000\n",
       "mean    73.900000   65.650000   65.375000   82.725000   79.425000\n",
       "std     42.795758   38.152561   34.116458   44.209865   43.176969\n",
       "min      1.000000    1.000000    9.000000    1.000000    3.000000\n",
       "20%     30.000000   33.800000   30.800000   37.000000   42.400000\n",
       "50%     77.000000   62.000000   66.500000   86.500000   89.500000\n",
       "75%    111.000000  100.500000   89.000000  129.250000  116.250000\n",
       "max    143.000000  139.000000  124.000000  144.000000  144.000000"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(percentiles=[.2,.50,.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.a=df.a.astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    object\n",
       "b     int32\n",
       "c     int32\n",
       "d     int32\n",
       "e     int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=map(str.upper,df.columns)\n",
    "df.columns\n",
    "df.columns=[x.lower() for x in df.columns]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    13\n",
       "c    30\n",
       "d    20\n",
       "e    37\n",
       "dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:].idxmax(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35.5, 70.0]      15\n",
       "(104.5, 139.0]     9\n",
       "(0.861, 35.5]      9\n",
       "(70.0, 104.5]      7\n",
       "Name: b, dtype: int64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.b.value_counts(bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    int32\n",
       "b    int32\n",
       "c    int32\n",
       "d    int32\n",
       "e    int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.a=df.a.astype(dtype='int')\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0              NaN\n",
       "1              NaN\n",
       "2      (0.0, 20.0]\n",
       "3              NaN\n",
       "4              NaN\n",
       "5              NaN\n",
       "6              NaN\n",
       "7              NaN\n",
       "8              NaN\n",
       "9     (20.0, 50.0]\n",
       "10    (20.0, 50.0]\n",
       "11             NaN\n",
       "12     (0.0, 20.0]\n",
       "13    (20.0, 50.0]\n",
       "14             NaN\n",
       "15     (0.0, 20.0]\n",
       "16    (20.0, 50.0]\n",
       "17             NaN\n",
       "18     (0.0, 20.0]\n",
       "19    (20.0, 50.0]\n",
       "20             NaN\n",
       "21             NaN\n",
       "22    (20.0, 50.0]\n",
       "23             NaN\n",
       "24    (20.0, 50.0]\n",
       "25             NaN\n",
       "26             NaN\n",
       "27             NaN\n",
       "28             NaN\n",
       "29             NaN\n",
       "30             NaN\n",
       "31    (20.0, 50.0]\n",
       "32             NaN\n",
       "33             NaN\n",
       "34             NaN\n",
       "35             NaN\n",
       "36    (20.0, 50.0]\n",
       "37    (20.0, 50.0]\n",
       "38             NaN\n",
       "39    (20.0, 50.0]\n",
       "Name: a, dtype: category\n",
       "Categories (2, interval[int64]): [(0, 20] < (20, 50]]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df['a'],[0,20,50])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (36.499, 77.0]\n",
       "1                NaN\n",
       "2                NaN\n",
       "3     (36.499, 77.0]\n",
       "4                NaN\n",
       "5                NaN\n",
       "6     (36.499, 77.0]\n",
       "7                NaN\n",
       "8                NaN\n",
       "9                NaN\n",
       "10    (36.499, 77.0]\n",
       "11    (36.499, 77.0]\n",
       "12               NaN\n",
       "13               NaN\n",
       "14               NaN\n",
       "15               NaN\n",
       "16               NaN\n",
       "17               NaN\n",
       "18               NaN\n",
       "19               NaN\n",
       "20               NaN\n",
       "21               NaN\n",
       "22               NaN\n",
       "23               NaN\n",
       "24    (36.499, 77.0]\n",
       "25    (36.499, 77.0]\n",
       "26               NaN\n",
       "27               NaN\n",
       "28               NaN\n",
       "29               NaN\n",
       "30               NaN\n",
       "31    (36.499, 77.0]\n",
       "32               NaN\n",
       "33               NaN\n",
       "34               NaN\n",
       "35               NaN\n",
       "36    (36.499, 77.0]\n",
       "37    (36.499, 77.0]\n",
       "38               NaN\n",
       "39               NaN\n",
       "Name: a, dtype: category\n",
       "Categories (1, interval[float64]): [(36.499, 77.0]]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.qcut(df.a,[.25,.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     (0.0, inf]\n",
       "1     (0.0, inf]\n",
       "2     (0.0, inf]\n",
       "3     (0.0, inf]\n",
       "4     (0.0, inf]\n",
       "5     (0.0, inf]\n",
       "6     (0.0, inf]\n",
       "7     (0.0, inf]\n",
       "8     (0.0, inf]\n",
       "9     (0.0, inf]\n",
       "10    (0.0, inf]\n",
       "11    (0.0, inf]\n",
       "12    (0.0, inf]\n",
       "13    (0.0, inf]\n",
       "14    (0.0, inf]\n",
       "15    (0.0, inf]\n",
       "16    (0.0, inf]\n",
       "17    (0.0, inf]\n",
       "18    (0.0, inf]\n",
       "19    (0.0, inf]\n",
       "20    (0.0, inf]\n",
       "21    (0.0, inf]\n",
       "22    (0.0, inf]\n",
       "23    (0.0, inf]\n",
       "24    (0.0, inf]\n",
       "25    (0.0, inf]\n",
       "26    (0.0, inf]\n",
       "27    (0.0, inf]\n",
       "28    (0.0, inf]\n",
       "29    (0.0, inf]\n",
       "30    (0.0, inf]\n",
       "31    (0.0, inf]\n",
       "32    (0.0, inf]\n",
       "33    (0.0, inf]\n",
       "34    (0.0, inf]\n",
       "35    (0.0, inf]\n",
       "36    (0.0, inf]\n",
       "37    (0.0, inf]\n",
       "38    (0.0, inf]\n",
       "39    (0.0, inf]\n",
       "Name: a, dtype: category\n",
       "Categories (2, interval[float64]): [(-inf, 0.0] < (0.0, inf]]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(df.a,[-np.inf,0,np.inf])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function Application"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    pipe() - tablewise function\n",
    "    apply() - row, col based function\n",
    "    agg()/transform() - API\n",
    "    applymap() - elementwise function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### pipe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert_F (df):\n",
    "    df['F']=np.arange(len(df))\n",
    "    return df\n",
    "\n",
    "def cap_Col (df):\n",
    "    df.columns=map(str.upper,df.columns)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>28</td>\n",
       "      <td>57</td>\n",
       "      <td>114</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>107</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69</td>\n",
       "      <td>92</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>107</td>\n",
       "      <td>132</td>\n",
       "      <td>71</td>\n",
       "      <td>132</td>\n",
       "      <td>139</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B   C    D    E  F\n",
       "0   64   28  57  114   48  0\n",
       "1  110   39  19   18   73  1\n",
       "2   14  107  31   18   54  2\n",
       "3   69   92  79    1   14  3\n",
       "4  107  132  71  132  139  4"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pipe(insert_F).pipe(cap_Col)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    32\n",
       "B    13\n",
       "C    30\n",
       "D    20\n",
       "E    37\n",
       "F    39\n",
       "dtype: int64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.idxmax(axis=0)\n",
    "df.apply(lambda x: x.idxmax(),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subtract_and_divide(x, sub, divide=1):\n",
    "    return (x - sub) / divide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.666667</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>36.333333</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>-1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>-1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.333333</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>-1.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A          B          C          D          E         F\n",
       "0  19.666667   7.666667  17.333333  36.333333  14.333333 -1.666667\n",
       "1  35.000000  11.333333   4.666667   4.333333  22.666667 -1.333333\n",
       "2   3.000000  34.000000   8.666667   4.333333  16.333333 -1.000000\n",
       "3  21.333333  29.000000  24.666667  -1.333333   3.000000 -0.666667\n",
       "4  34.000000  42.333333  22.000000  42.333333  44.666667 -0.333333"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(subtract_and_divide,args=(5,3)).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.666667</td>\n",
       "      <td>7.666667</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>36.333333</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>-1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>11.333333</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>-1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>16.333333</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.333333</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>-1.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>44.666667</td>\n",
       "      <td>-0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           A          B          C          D          E         F\n",
       "0  19.666667   7.666667  17.333333  36.333333  14.333333 -1.666667\n",
       "1  35.000000  11.333333   4.666667   4.333333  22.666667 -1.333333\n",
       "2   3.000000  34.000000   8.666667   4.333333  16.333333 -1.000000\n",
       "3  21.333333  29.000000  24.666667  -1.333333   3.000000 -0.666667\n",
       "4  34.000000  42.333333  22.000000  42.333333  44.666667 -0.333333"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(subtract_and_divide,args=(5,),divide=3).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2      True\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31    False\n",
       "32    False\n",
       "33    False\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38    False\n",
       "39    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]=np.nan\n",
    "df.isnull().any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "21    False\n",
       "22    False\n",
       "23    False\n",
       "24    False\n",
       "25    False\n",
       "26    False\n",
       "27    False\n",
       "28    False\n",
       "29    False\n",
       "30    False\n",
       "31    False\n",
       "32    False\n",
       "33    False\n",
       "34    False\n",
       "35    False\n",
       "36    False\n",
       "37    False\n",
       "38    False\n",
       "39    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.apply(pd.Series.interpolate)\n",
    "df.isnull().any(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### agg()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.762888\n",
       "b    2.931231\n",
       "c    1.418909\n",
       "d    2.210151\n",
       "e    2.513546\n",
       "dtype: float64"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.rand(4,5),columns=list('abcde'))\n",
    "df.agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.762888\n",
       "b    2.931231\n",
       "c    1.418909\n",
       "d    2.210151\n",
       "e    2.513546\n",
       "dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg('sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.084692</td>\n",
       "      <td>0.616938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.852308</td>\n",
       "      <td>0.570462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.319953</td>\n",
       "      <td>0.463991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.579772</td>\n",
       "      <td>0.715954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sum      mean\n",
       "0  3.084692  0.616938\n",
       "1  2.852308  0.570462\n",
       "2  2.319953  0.463991\n",
       "3  3.579772  0.715954"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg(['sum','mean'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>2.762888</td>\n",
       "      <td>2.931231</td>\n",
       "      <td>1.418909</td>\n",
       "      <td>2.210151</td>\n",
       "      <td>2.513546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;lambda&gt;</th>\n",
       "      <td>0.690722</td>\n",
       "      <td>0.732808</td>\n",
       "      <td>0.354727</td>\n",
       "      <td>0.552538</td>\n",
       "      <td>0.628387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 a         b         c         d         e\n",
       "sum       2.762888  2.931231  1.418909  2.210151  2.513546\n",
       "<lambda>  0.690722  0.732808  0.354727  0.552538  0.628387"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg(['sum',lambda x: x.mean()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>2.762888</td>\n",
       "      <td>2.931231</td>\n",
       "      <td>1.418909</td>\n",
       "      <td>2.210151</td>\n",
       "      <td>2.513546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>0.690722</td>\n",
       "      <td>0.732808</td>\n",
       "      <td>0.354727</td>\n",
       "      <td>0.552538</td>\n",
       "      <td>0.628387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a         b         c         d         e\n",
       "sum   2.762888  2.931231  1.418909  2.210151  2.513546\n",
       "test  0.690722  0.732808  0.354727  0.552538  0.628387"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def test(x):\n",
    "    return x.mean()\n",
    "\n",
    "df.agg(['sum',test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    2.762888\n",
       "b    0.732808\n",
       "dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg({'a':'sum','b':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.732808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>2.762888</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a         b\n",
       "max   0.954748       NaN\n",
       "mean       NaN  0.732808\n",
       "sum   2.762888       NaN"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.agg({'a':['sum','max'],'b':'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952377</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592398</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.554021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.542281</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.796405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "0  0.952377  0.810574       NaN  0.596234       NaN\n",
       "1  0.592398  0.893752  0.554021       NaN       NaN\n",
       "2       NaN  0.542281       NaN  0.635637  0.796405\n",
       "3  0.954748  0.684624       NaN       NaN  0.966693"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mask(df<0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reindexing and Altering labels"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "    df.columns.levels\n",
    "    col/index.get_level_values()\n",
    "    remove_unused_levels()\n",
    "    reindex()\n",
    "    reindex_like()\n",
    "    align()\n",
    "    drop()\n",
    "    rename()\n",
    "    set_axis()\n",
    "    df.columns.set_levels()\n",
    "    df.set_index()\n",
    "    rename_axis()\n",
    "    reset_index()\n",
    "    df.swaplevel()\n",
    "    df.reorder_levels()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### reindex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.415816\n",
       "b   -1.358503\n",
       "c   -0.514430\n",
       "d   -0.216060\n",
       "e    0.422380\n",
       "dtype: float64"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.randn(5),index=list('abcde'))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    0.422380\n",
       "b   -1.358503\n",
       "f         NaN\n",
       "d   -0.216060\n",
       "dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.reindex(['e', 'b', 'f', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952377</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.294330</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.431178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592398</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.554021</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.319270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263366</td>\n",
       "      <td>0.542281</td>\n",
       "      <td>0.082265</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.796405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>0.488293</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "0  0.952377  0.810574  0.294330  0.596234  0.431178\n",
       "1  0.592398  0.893752  0.554021  0.492867  0.319270\n",
       "2  0.263366  0.542281  0.082265  0.635637  0.796405\n",
       "3  0.954748  0.684624  0.488293  0.485414  0.966693"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>e</th>\n",
       "      <th>c</th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.796405</td>\n",
       "      <td>0.082265</td>\n",
       "      <td>0.542281</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.263366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.319270</td>\n",
       "      <td>0.554021</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.592398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.966693</td>\n",
       "      <td>0.488293</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.954748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.431178</td>\n",
       "      <td>0.294330</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.952377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          e         c         b         d         a\n",
       "2  0.796405  0.082265  0.542281  0.635637  0.263366\n",
       "1  0.319270  0.554021  0.893752  0.492867  0.592398\n",
       "3  0.966693  0.488293  0.684624  0.485414  0.954748\n",
       "0  0.431178  0.294330  0.810574  0.596234  0.952377"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(index=[2,1,3,0], columns=list('ecbda'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### align()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -1.094043\n",
       "b    1.236908\n",
       "c   -0.230285\n",
       "d   -0.704418\n",
       "dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])\n",
    "s1 = s[:4]\n",
    "s2=s[1:]\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.236908\n",
       "c   -0.230285\n",
       "d   -0.704418\n",
       "e   -0.591375\n",
       "dtype: float64"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(a   -1.094043\n",
       " b    1.236908\n",
       " c   -0.230285\n",
       " d   -0.704418\n",
       " e         NaN\n",
       " dtype: float64,\n",
       " a         NaN\n",
       " b    1.236908\n",
       " c   -0.230285\n",
       " d   -0.704418\n",
       " e   -0.591375\n",
       " dtype: float64)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.align(s2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### drop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "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>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.294330</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.431178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.554021</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.319270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.082265</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.796405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.488293</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c         d         e\n",
       "0  0.294330  0.596234  0.431178\n",
       "1  0.554021  0.492867  0.319270\n",
       "2  0.082265  0.635637  0.796405\n",
       "3  0.488293  0.485414  0.966693"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['a','b'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>i</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952377</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.294330</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.431178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592398</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.554021</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.319270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263366</td>\n",
       "      <td>0.542281</td>\n",
       "      <td>0.082265</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.796405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>0.488293</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         i         d         e\n",
       "0  0.952377  0.810574  0.294330  0.596234  0.431178\n",
       "1  0.592398  0.893752  0.554021  0.492867  0.319270\n",
       "2  0.263366  0.542281  0.082265  0.635637  0.796405\n",
       "3  0.954748  0.684624  0.488293  0.485414  0.966693"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.rename({'c':'i'}, axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Sorting"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "sort_index()\n",
    "sort_values()\n",
    "searchsorted() - searches where to insert element to maintain index order\n",
    "nsmallest()\n",
    "nlargest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952377</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.431178</td>\n",
       "      <td>0.294330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592398</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.319270</td>\n",
       "      <td>0.554021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.263366</td>\n",
       "      <td>0.542281</td>\n",
       "      <td>0.635637</td>\n",
       "      <td>0.796405</td>\n",
       "      <td>0.082265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.966693</td>\n",
       "      <td>0.488293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         d         e         i\n",
       "0  0.952377  0.810574  0.596234  0.431178  0.294330\n",
       "1  0.592398  0.893752  0.492867  0.319270  0.554021\n",
       "2  0.263366  0.542281  0.635637  0.796405  0.082265\n",
       "3  0.954748  0.684624  0.485414  0.966693  0.488293"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.a.searchsorted(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    0.263366\n",
       "1    0.592398\n",
       "0    0.952377\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.a.nsmallest(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>i</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.954748</td>\n",
       "      <td>0.684624</td>\n",
       "      <td>0.488293</td>\n",
       "      <td>0.485414</td>\n",
       "      <td>0.966693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.952377</td>\n",
       "      <td>0.810574</td>\n",
       "      <td>0.294330</td>\n",
       "      <td>0.596234</td>\n",
       "      <td>0.431178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592398</td>\n",
       "      <td>0.893752</td>\n",
       "      <td>0.554021</td>\n",
       "      <td>0.492867</td>\n",
       "      <td>0.319270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         i         d         e\n",
       "3  0.954748  0.684624  0.488293  0.485414  0.966693\n",
       "0  0.952377  0.810574  0.294330  0.596234  0.431178\n",
       "1  0.592398  0.893752  0.554021  0.492867  0.319270"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nlargest(3,['a','b'])"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}