Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n",
      "/anaconda/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.arange(0,20).reshape(5,4), index=['Row1','Row2','Row3','Row4','Row5'], columns=['Col1','Col2','Col3','Col4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Row1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row5</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Col1  Col2  Col3  Col4\n",
       "Row1     0     1     2     3\n",
       "Row2     4     5     6     7\n",
       "Row3     8     9    10    11\n",
       "Row4    12    13    14    15\n",
       "Row5    16    17    18    19"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataframe elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "      <td>6.324555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Col1       Col2       Col3       Col4\n",
       "count   5.000000   5.000000   5.000000   5.000000\n",
       "mean    8.000000   9.000000  10.000000  11.000000\n",
       "std     6.324555   6.324555   6.324555   6.324555\n",
       "min     0.000000   1.000000   2.000000   3.000000\n",
       "25%     4.000000   5.000000   6.000000   7.000000\n",
       "50%     8.000000   9.000000  10.000000  11.000000\n",
       "75%    12.000000  13.000000  14.000000  15.000000\n",
       "max    16.000000  17.000000  18.000000  19.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 5 entries, Row1 to Row5\n",
      "Data columns (total 4 columns):\n",
      "Col1    5 non-null int64\n",
      "Col2    5 non-null int64\n",
      "Col3    5 non-null int64\n",
      "Col4    5 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 3 columns):\n",
      "A    5 non-null object\n",
      "B    5 non-null int64\n",
      "C    5 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 200.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({\"A\":[\"sofia\", 5, 8, 11, 100], \n",
    "                   \"B\":[2, 8, 77, 4, 11], \n",
    "                   \"C\":[\"amy\", 11, 4, 6, 9]}) \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>0</th>\n",
       "      <td>sofia</td>\n",
       "      <td>2</td>\n",
       "      <td>amy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A   B    C\n",
       "0  sofia   2  amy\n",
       "1      5   8   11\n",
       "2      8  77    4\n",
       "3     11   4    6\n",
       "4    100  11    9"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df[1:] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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",
      "A    4 non-null object\n",
      "B    4 non-null int64\n",
      "C    4 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 180.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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",
      "A    4 non-null int64\n",
      "B    4 non-null int64\n",
      "C    4 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 180.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df_new.infer_objects().info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Test1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1    0\n",
       "Col2    0\n",
       "Col3    0\n",
       "Col4    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0         True          True        False\n",
       "1         True          True         True\n",
       "2        False          True         True\n",
       "3         True         False         True"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'First Score':[100, 90, np.nan, 95], \n",
    "        'Second Score': [30, 45, 56, np.nan], \n",
    "        'Third Score':[np.nan, 40, 80, 98]} \n",
    "df = pd.DataFrame(dict) \n",
    "df.notnull() "
   ]
  },
  {
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        100.0          30.0          NaN\n",
       "1         90.0          45.0         40.0\n",
       "3         95.0           NaN         98.0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Access not nulls\n",
    "bool_series = pd.notnull(df[\"First Score\"])\n",
    "df[bool_series]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        100.0          30.0          NaN\n",
       "1         90.0          45.0         40.0\n",
       "2         90.0          56.0         80.0\n",
       "3         95.0          56.0         98.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling null value with previous ones   \n",
    "df.fillna(method ='pad') \n",
    "#add attribute inplace=True if want to change the dataframe,like -\n",
    "# df.fillna(method ='pad', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>95.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        100.0          30.0         40.0\n",
       "1         90.0          45.0         40.0\n",
       "2         95.0          56.0         80.0\n",
       "3         95.0           NaN         98.0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filling null value with the next ones   \n",
    "df.fillna(method ='bfill') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>99.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        100.0          30.0         99.0\n",
       "1         90.0          45.0         40.0\n",
       "2         99.0          56.0         80.0\n",
       "3         95.0          99.0         98.0"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replcae null values\n",
    "df.replace(to_replace = np.nan, value = 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0        False         False         True\n",
       "1        False         False        False\n",
       "2         True         False        False\n",
       "3        False          True        False"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'First Score':[100, 90, np.nan, 95], \n",
    "        'Second Score': [30, 45, 56, np.nan], \n",
    "        'Third Score':[np.nan, 40, 80, 98]} \n",
    "df = pd.DataFrame(dict) \n",
    "df.isna() "
   ]
  },
  {
   "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",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Second Score  Third Score\n",
       "0         True          True        False\n",
       "1         True          True         True\n",
       "2        False          True         True\n",
       "3         True         False         True"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.notna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drop nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Fourth Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Fourth Score  Second Score  Third Score\n",
       "0        100.0           NaN          30.0         52.0\n",
       "1          NaN           NaN           NaN          NaN\n",
       "2          NaN           NaN          45.0         80.0\n",
       "3         95.0          65.0          56.0         98.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'First Score':[100, np.nan, np.nan, 95], \n",
    "        'Second Score': [30, np.nan, 45, 56], \n",
    "        'Third Score':[52, np.nan, 80, 98], \n",
    "        'Fourth Score':[np.nan, np.nan, np.nan, 65]} \n",
    "df = pd.DataFrame(dict) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Fourth Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Fourth Score  Second Score  Third Score\n",
       "0        100.0           NaN          30.0         52.0\n",
       "2          NaN           NaN          45.0         80.0\n",
       "3         95.0          65.0          56.0         98.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop row with all nulls\n",
    "df.dropna(how = 'all') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Fourth Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Fourth Score  Second Score  Third Score\n",
       "0          100           NaN          30.0         52.0\n",
       "1           10           NaN           NaN          NaN\n",
       "2           40           NaN          45.0         80.0\n",
       "3           95          65.0          56.0         98.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'First Score':[100, 10, 40, 95], \n",
    "        'Second Score': [30, np.nan, 45, 56], \n",
    "        'Third Score':[52, np.nan, 80, 98], \n",
    "        'Fourth Score':[np.nan, np.nan, np.nan, 65]} \n",
    "df = pd.DataFrame(dict)    \n",
    "df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
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       "      <th>2</th>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score\n",
       "0          100\n",
       "1           10\n",
       "2           40\n",
       "3           95"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop all null columns\n",
    "df.dropna(axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Score</th>\n",
       "      <th>Fourth Score</th>\n",
       "      <th>Second Score</th>\n",
       "      <th>Third Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   First Score  Fourth Score  Second Score  Third Score\n",
       "3           95          65.0          56.0         98.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop rows with any nulls\n",
    "df.dropna(axis = 0, how ='any') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>12.0</td>\n",
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       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "0  12.0   NaN  20.0  14.0\n",
       "1   4.0   2.0  16.0   3.0\n",
       "2   5.0  54.0   NaN   NaN\n",
       "3   NaN   3.0   3.0   NaN\n",
       "4   1.0   NaN   8.0   6.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Interpolate\n",
    "\n",
    "df = pd.DataFrame({\"A\":[12, 4, 5, None, 1],  \n",
    "                   \"B\":[None, 2, 54, 3, None],  \n",
    "                   \"C\":[20, 16, None, 3, 8],  \n",
    "                   \"D\":[14, 3, None, None, 6]})  \n",
    "df  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A     B     C     D\n",
       "0  12.0   NaN  20.0  14.0\n",
       "1   4.0   2.0  16.0   3.0\n",
       "2   5.0  54.0   9.5   4.0\n",
       "3   3.0   3.0   3.0   5.0\n",
       "4   1.0   3.0   8.0   6.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.interpolate(method ='linear', limit_direction ='forward') \n",
    "# As we can see the output, values in the first row could not get filled as the direction of filling of values is forward and there is no previous value which could have been used in interpolation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deal with rows columns - Series, DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Unnamed: 0</th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Row1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Row2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row4</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row5</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0  Col1  Col2  Col3  Col4\n",
       "0       Row1     0     1     2     3\n",
       "1       Row2     4     5     6     7\n",
       "2       Row3     8     9    10    11\n",
       "3       Row4    12    13    14    15\n",
       "4       Row5    16    17    18    19"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Test1.csv') # sep is attrubute to provide separator if there is anything else than comma(,) like sep=';'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Col1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Col1\n",
       "0     0\n",
       "1     4\n",
       "2     8\n",
       "3    12\n",
       "4    16"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns Dataframe\n",
    "df[['Col1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Col1  Col2\n",
       "0     0     1\n",
       "1     4     5\n",
       "2     8     9\n",
       "3    12    13\n",
       "4    16    17"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns Dataframe\n",
    "df[['Col1','Col2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     4\n",
       "2     8\n",
       "3    12\n",
       "4    16\n",
       "Name: Col1, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns Series\n",
    "df['Col1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df['Col1']))\n",
    "print(type(df[['Col1']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  8, 12, 16])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Col1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>Unnamed: 0</th>\n",
       "      <th>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Row3</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Row4</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Row5</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0  Col1  Col2  Col3  Col4\n",
       "2       Row3     8     9    10    11\n",
       "3       Row4    12    13    14    15\n",
       "4       Row5    16    17    18    19"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Col1']>4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height    Name Qualification\n",
       "0     5.1     Jai           Msc\n",
       "1     6.2  Princi            MA\n",
       "2     5.1  Gaurav           Msc\n",
       "3     5.2    Anuj           Msc"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Height': [5.1, 6.2, 5.1, 5.2], \n",
    "        'Qualification': ['Msc', 'MA', 'Msc', 'Msc']} \n",
    "df = pd.DataFrame(data) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Msc</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Msc</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height    Name Qualification    Address\n",
       "0     5.1     Jai           Msc      Delhi\n",
       "1     6.2  Princi            MA  Bangalore\n",
       "2     5.1  Gaurav           Msc    Chennai\n",
       "3     5.2    Anuj           Msc      Patna"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "address = ['Delhi', 'Bangalore', 'Chennai', 'Patna'] \n",
    "df['Address'] = address \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"Qualification\"], axis = 1, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height    Name    Address\n",
       "0     5.1     Jai      Delhi\n",
       "1     6.2  Princi  Bangalore\n",
       "2     5.1  Gaurav    Chennai\n",
       "3     5.2    Anuj      Patna"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete Rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Height    Name    Address\n",
       "1     6.2  Princi  Bangalore\n",
       "2     5.1  Gaurav    Chennai\n",
       "3     5.2    Anuj      Patna"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop([0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing elements\n",
    "#### 1. df.[]\n",
    "#### 2. df.loc[]\n",
    "#### 3. df.iloc[]\n",
    "#### 4. df.ix[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patna</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Height    Name Qualification\n",
       "0      Delhi     5.1     Jai           Msc\n",
       "1  Bangalore     6.2  Princi            MA\n",
       "2    Chennai     5.1  Gaurav           Msc\n",
       "3      Patna     5.2    Anuj           Msc"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name': ['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Height': [5.1, 6.2, 5.1, 5.2], \n",
    "        'Qualification': ['Msc', 'MA', 'Msc', 'Msc'],\n",
    "        'Address': ['Delhi', 'Bangalore', 'Chennai', 'Patna']} \n",
    "df = pd.DataFrame(data) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Jai\n",
       "1    Princi\n",
       "2    Gaurav\n",
       "3      Anuj\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first = df[\"Name\"] \n",
    "first #Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Address\n",
       "0     Jai      Delhi\n",
       "1  Princi  Bangalore\n",
       "2  Gaurav    Chennai\n",
       "3    Anuj      Patna"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first = df[[\"Name\",\"Address\"]]\n",
    "first #Dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### loc : This function selects data by the label of the rows and columns. The df.loc indexer selects data in a different way than just the indexing operator. It can select subsets of rows or columns. It can also simultaneously select subsets of rows and columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Address          Delhi\n",
       "Height             5.1\n",
       "Name               Jai\n",
       "Qualification      Msc\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second = df.loc[0]\n",
    "second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Height    Name Qualification\n",
       "0      Delhi     5.1     Jai           Msc\n",
       "1  Bangalore     6.2  Princi            MA"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second = df.loc[[0,1]]\n",
    "second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Address\n",
       "0     Jai      Delhi\n",
       "1  Princi  Bangalore"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second = df.loc[[0,1],['Name','Address']]\n",
    "second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Address\n",
       "0     Jai      Delhi\n",
       "1  Princi  Bangalore\n",
       "2  Gaurav    Chennai\n",
       "3    Anuj      Patna"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second = df.loc[:,['Name','Address']]\n",
    "second"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iloc: This function allows us to retrieve rows and columns by position. In order to do that, we’ll need to specify the positions of the rows that we want, and the positions of the columns that we want as well. The df.iloc indexer is very similar to df.loc but only uses integer locations to make its selections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Address          Patna\n",
       "Height             5.2\n",
       "Name              Anuj\n",
       "Qualification      Msc\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third = df.iloc[3]  \n",
    "third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patna</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Address  Height    Name Qualification\n",
       "2  Chennai     5.1  Gaurav           Msc\n",
       "3    Patna     5.2    Anuj           Msc"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third = df.iloc [[2, 3]]\n",
    "third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Address\n",
       "2  Gaurav  Chennai\n",
       "3    Anuj    Patna"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third = df.iloc[[2,3],[2,0]]\n",
    "third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jai</td>\n",
       "      <td>Delhi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Princi</td>\n",
       "      <td>Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Chennai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anuj</td>\n",
       "      <td>Patna</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name    Address\n",
       "0     Jai      Delhi\n",
       "1  Princi  Bangalore\n",
       "2  Gaurav    Chennai\n",
       "3    Anuj      Patna"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third = df.iloc[:,[2,0]]\n",
    "third"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ix: Early in the development of pandas, there existed another indexer, ix. This indexer was capable of selecting both by label and by integer location. While it was versatile, it caused lots of confusion because it’s not explicit. Sometimes integers can also be labels for rows or columns. Thus there were instances where it was ambiguous. Generally, ix is label based and acts just as the .loc indexer. However, .ix also supports integer type selections (as in .iloc) where passed an integer. This only works where the index of the DataFrame is not integer based .ix will accept any of the inputs of .loc and .iloc.\n",
    "### Note: The .ix indexer has been deprecated in recent versions of Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Address          Delhi\n",
       "Height             5.1\n",
       "Name               Jai\n",
       "Qualification      Msc\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forth = df.ix[0] \n",
    "forth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Patna</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Height    Name Qualification\n",
       "0      Delhi     5.1     Jai           Msc\n",
       "1  Bangalore     6.2  Princi            MA\n",
       "2    Chennai     5.1  Gaurav           Msc\n",
       "3      Patna     5.2    Anuj           Msc"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Address</th>\n",
       "      <th>Height</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>6.2</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Height    Name Qualification\n",
       "0      Delhi     5.1     Jai           Msc\n",
       "1  Bangalore     6.2  Princi            MA"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[True,True,False,False]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      degree    name  score\n",
      "True     MBA  aparna     90\n",
      "True  M.Tech  sudhir     80\n"
     ]
    }
   ],
   "source": [
    "dict = {'name':[\"aparna\", \"pankaj\", \"sudhir\", \"Geeku\"], \n",
    "        'degree': [\"MBA\", \"BCA\", \"M.Tech\", \"MBA\"], \n",
    "        'score':[90, 40, 80, 98]} \n",
    "df = pd.DataFrame(dict, index = [True, False, True, False]) \n",
    "print(df.loc[True]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(df.iloc[True])\n",
    "# Won't work, In order to access a dataframe using .iloc[], we have to pass a boolean value (True or False) in a iloc[] function but iloc[] function accept only integer as argument so it will throw an error so we can only access a dataframe when we pass a integer in iloc[] function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "degree       BCA\n",
       "name      pankaj\n",
       "score         40\n",
       "dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "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>degree</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>MBA</td>\n",
       "      <td>aparna</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>M.Tech</td>\n",
       "      <td>sudhir</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      degree    name  score\n",
       "True     MBA  aparna     90\n",
       "True  M.Tech  sudhir     80"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:1: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "degree       BCA\n",
       "name      pankaj\n",
       "score         40\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ix[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>degree</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>MBA</td>\n",
       "      <td>aparna</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>MBA</td>\n",
       "      <td>Geeku</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      degree    name  score\n",
       "True     MBA  aparna     90\n",
       "False    MBA   Geeku     98"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['degree'] == 'MBA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>index</th>\n",
       "      <th>degree</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>MBA</td>\n",
       "      <td>aparna</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>BCA</td>\n",
       "      <td>pankaj</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>M.Tech</td>\n",
       "      <td>sudhir</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Geeku</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  degree    name  score\n",
       "0   True     MBA  aparna     90\n",
       "1  False     BCA  pankaj     40\n",
       "2   True  M.Tech  sudhir     80\n",
       "3  False     MBA   Geeku     98"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "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>index</th>\n",
       "      <th>degree</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>M.Tech</td>\n",
       "      <td>sudhir</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>MBA</td>\n",
       "      <td>Geeku</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  degree    name  score\n",
       "2   True  M.Tech  sudhir     80\n",
       "3  False     MBA   Geeku     98"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index >1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_excel = pd.read_excel(\"Excel_Sample.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_excel[df_excel['a'] >0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <th>c</th>\n",
       "      <th>d</th>\n",
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       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "    a   b   c   d\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
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     "execution_count": 263,
     "metadata": {},
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   "source": [
    "df_excel[(df_excel['a'] > 0) & (df_excel['c'] >= 10)]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    a   b   c   d\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
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     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_excel[(df_excel.a > 0) & (df_excel.c >= 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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      "text/plain": [
       "    c   d\n",
       "2  10  11\n",
       "3  14  15"
      ]
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     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_excel[(df_excel['a'] > 0) & (df_excel['c'] >= 10)][['c','d']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([['A',10,3,5],['B',1,4,6],['A',9,4,1],['C',1,6,9],['C',4,2,1]], index=['Row1','Row2','Row3','Row4','Row5'], columns=['Col1','Col2','Col3','Col4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Col1</th>\n",
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       "      <th>Col4</th>\n",
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       "      <td>A</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row2</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>Row3</th>\n",
       "      <td>A</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Row4</th>\n",
       "      <td>C</td>\n",
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       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <th>Row5</th>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Col1  Col2  Col3  Col4\n",
       "Row1    A    10     3     5\n",
       "Row2    B     1     4     6\n",
       "Row3    A     9     4     1\n",
       "Row4    C     1     6     9\n",
       "Row5    C     4     2     1"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "      <th>A</th>\n",
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       "      <th>B</th>\n",
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      "text/plain": [
       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A       19     7     6\n",
       "B        1     4     6\n",
       "C        5     8    10"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).agg(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <td>9.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>B</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
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       "      <th>C</th>\n",
       "      <td>2.5</td>\n",
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      "text/plain": [
       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A      9.5   3.5   3.0\n",
       "B      1.0   4.0   6.0\n",
       "C      2.5   4.0   5.0"
      ]
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     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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      "text/plain": [
       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A      9.5   3.5   3.0\n",
       "B      1.0   4.0   6.0\n",
       "C      2.5   4.0   5.0"
      ]
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     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <td>6</td>\n",
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       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A        9     3     1\n",
       "B        1     4     6\n",
       "C        1     2     1"
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     "metadata": {},
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   "source": [
    "df.groupby(['Col1']).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>6</td>\n",
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      "text/plain": [
       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A       10     4     5\n",
       "B        1     4     6\n",
       "C        4     6     9"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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>Col2</th>\n",
       "      <th>Col3</th>\n",
       "      <th>Col4</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col1</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</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Col2  Col3  Col4\n",
       "Col1                  \n",
       "A        2     2     2\n",
       "B        1     1     1\n",
       "C        2     2     2"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).count() # returns DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Col1\n",
       "A    2\n",
       "B    1\n",
       "C    2\n",
       "Name: Col2, dtype: int64"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).count()['Col2'] # returns Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "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>Col2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Col2\n",
       "Col1      \n",
       "A        2\n",
       "B        1\n",
       "C        2"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Col1']).count()[['Col2']] # returns DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "ser = pd.Series() \n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create series from array\n",
    "data = np.array(['g','e','e','k','s'])\n",
    "ser = pd.Series(data)\n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10    g\n",
      "11    e\n",
      "12    e\n",
      "13    k\n",
      "14    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "data = np.array(['g', 'e', 'e', 'k', 's']) \n",
    "\n",
    "# providing an index \n",
    "ser = pd.Series(data, index =[10, 11, 12, 13, 14]) \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create series from list\n",
    "list = ['g', 'e', 'e', 'k', 's']\n",
    "ser = pd.Series(list)\n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geeks    10\n",
      "for      20\n",
      "geeks    30\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create series from dict\n",
    "dict = {'Geeks' : 10, \n",
    "        'for' : 20, \n",
    "        'geeks' : 30}\n",
    "ser = pd.Series(dict) \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    10\n",
      "1    10\n",
      "2    10\n",
      "3    10\n",
      "4    10\n",
      "5    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create series from scalar value\n",
    "ser = pd.Series(10, index =[0, 1, 2, 3, 4, 5]) \n",
    "  \n",
    "print(ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     3.0\n",
      "1    18.0\n",
      "2    33.0\n",
      "dtype: float64\n",
      "\n",
      " 0      1.0\n",
      "1     12.0\n",
      "2     23.0\n",
      "3     34.0\n",
      "4     45.0\n",
      "5     56.0\n",
      "6     67.0\n",
      "7     78.0\n",
      "8     89.0\n",
      "9    100.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Create series using numpy function\n",
    "ser1 = pd.Series(np.linspace(3, 33, 3))  \n",
    "print(ser1)  \n",
    "\n",
    "ser2 = pd.Series(np.linspace(1, 100, 10))  \n",
    "print(\"\\n\", ser2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating dataframe using series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>first</th>\n",
       "      <th>second</th>\n",
       "      <th>third</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>4.7</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first  second third\n",
       "0      1     1.1     a\n",
       "1      3     3.5     b\n",
       "2      4     4.7     c\n",
       "3      5     5.8     d\n",
       "4      6     2.9     e\n",
       "5      2     9.3   NaN\n",
       "6      9     NaN   NaN"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1, 3, 4, 5, 6, 2, 9])           # Define series 1 \n",
    "s2 = pd.Series([1.1, 3.5, 4.7, 5.8, 2.9, 9.3]) # Define series 2 \n",
    "s3 = pd.Series(['a', 'b', 'c', 'd', 'e'])     # Define series 3 \n",
    "  \n",
    "Data ={'first':s1, 'second':s2, 'third':s3} # Define Data \n",
    "dfseries = pd.DataFrame(Data) \n",
    "dfseries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Access elements of series\n",
    "### There are two ways through which we can access element of series, they are :\n",
    "\n",
    "##### Accessing Element from Series with Position\n",
    "##### Accessing Element Using Label (index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    g\n",
      "1    e\n",
      "2    e\n",
      "3    k\n",
      "4    s\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "data = np.array(['g','e','e','k','s','f', 'o','r','g','e','e','k','s'])\n",
    "ser = pd.Series(data)  \n",
    "#retrieve the first element\n",
    "print(ser[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o\n"
     ]
    }
   ],
   "source": [
    "data = np.array(['g','e','e','k','s','f', 'o','r','g','e','e','k','s'])\n",
    "ser = pd.Series(data,index=[10,11,12,13,14,15,16,17,18,19,20,21,22])\n",
    "# accessing a element using index element\n",
    "print(ser[16])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing and Selecting Data in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>degree</th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MBA</td>\n",
       "      <td>aparna</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BCA</td>\n",
       "      <td>pankaj</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M.Tech</td>\n",
       "      <td>sudhir</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MBA</td>\n",
       "      <td>Geeku</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   degree    name  score\n",
       "0     MBA  aparna     90\n",
       "1     BCA  pankaj     40\n",
       "2  M.Tech  sudhir     80\n",
       "3     MBA   Geeku     98"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {'name':[\"aparna\", \"pankaj\", \"sudhir\", \"Geeku\"], \n",
    "        'degree': [\"MBA\", \"BCA\", \"M.Tech\", \"MBA\"], \n",
    "        'score':[90, 40, 80, 98]} \n",
    "df = pd.DataFrame(dict)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    aparna\n",
       "1    pankaj\n",
       "2    sudhir\n",
       "3     Geeku\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series(df['name']) \n",
    "data = ser.head(4)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    pankaj\n",
       "2    sudhir\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using indexing operator\n",
    "data[1:3] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    pankaj\n",
       "2    sudhir\n",
       "3     Geeku\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using .loc[] function\n",
    "data.loc[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    pankaj\n",
       "2    sudhir\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using .iloc[] function\n",
    "data.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Operation on Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    5\n",
      "b    2\n",
      "c    3\n",
      "d    7\n",
      "dtype: int64 \n",
      "\n",
      " a    1\n",
      "b    6\n",
      "d    4\n",
      "e    9\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a     6.0\n",
       "b     8.0\n",
       "c     3.0\n",
       "d    11.0\n",
       "e     9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([5, 2, 3,7], index=['a', 'b', 'c', 'd'])\n",
    "data1 = pd.Series([1, 6, 4, 9], index=['a', 'b', 'd', 'e'])\n",
    "print(data, \"\\n\\n\", data1)\n",
    "\n",
    "# adding two series using\n",
    "# .add\n",
    "data.add(data1, fill_value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    5\n",
      "b    2\n",
      "c    3\n",
      "d    7\n",
      "dtype: int64 \n",
      "\n",
      " a    1\n",
      "b    6\n",
      "d    4\n",
      "e    9\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "a    4.0\n",
       "b   -4.0\n",
       "c    3.0\n",
       "d    3.0\n",
       "e   -9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([5, 2, 3,7], index=['a', 'b', 'c', 'd'])\n",
    "data1 = pd.Series([1, 6, 4, 9], index=['a', 'b', 'd', 'e']) \n",
    "print(data, \"\\n\\n\", data1)\n",
    "\n",
    "# subtracting two series using\n",
    "# .sub\n",
    "data.sub(data1, fill_value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conversion Operation on Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE CONVERSION\n",
      " degree    object\n",
      "name      object\n",
      "score      int64\n",
      "dtype: object \n",
      "\n",
      "AFTER CONVERSION\n",
      " degree     object\n",
      "name       object\n",
      "score     float64\n",
      "dtype: object \n",
      "\n"
     ]
    }
   ],
   "source": [
    "dict = {'name':[\"aparna\", \"pankaj\", \"sudhir\", \"Geeku\"], \n",
    "        'degree': [\"MBA\", \"BCA\", \"M.Tech\", \"MBA\"], \n",
    "        'score':[90, 40, 80, 98]} \n",
    "data = pd.DataFrame(dict)\n",
    "data\n",
    "\n",
    "before = data.dtypes \n",
    "\n",
    "# converting dtypes using astype \n",
    "data[\"score\"]= data[\"score\"].astype(float) \n",
    "# storing dtype after converting \n",
    "after = data.dtypes\n",
    "# printing to compare \n",
    "print(\"BEFORE CONVERSION\\n\", before, \"\\n\") \n",
    "print(\"AFTER CONVERSION\\n\", after, \"\\n\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data type before converting = <class 'pandas.core.series.Series'>\n",
      "Data type after converting = <class 'list'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['MBA', 'BCA', 'M.Tech', 'MBA']"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# storing dtype before operation \n",
    "dtype_before = type(data[\"degree\"]) \n",
    "\n",
    "# converting to list \n",
    "salary_list = data[\"degree\"].tolist() \n",
    "   \n",
    "# storing dtype after operation \n",
    "dtype_after = type(salary_list) \n",
    "   \n",
    "# printing dtype \n",
    "print(\"Data type before converting = {}\\nData type after converting = {}\"\n",
    "      .format(dtype_before, dtype_after)) \n",
    "# displaying list \n",
    "salary_list "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concat\n",
    "\n",
    "#### In order to concat dataframe, we use concat() function which helps in concatenating a dataframe. We can concat a dataframe in many different ways, they are:\n",
    "\n",
    "###### Concatenating DataFrame using .concat()\n",
    "###### Concatenating DataFrame by setting logic on axes\n",
    "###### Concatenating DataFrame using .append()\n",
    "###### Concatenating DataFrame by ignoring indexes\n",
    "###### Concatenating DataFrame with group keys\n",
    "###### Concatenating with mixed ndims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age    Name Qualification\n",
      "0     Nagpur   27     Jai           Msc\n",
      "1     Kanpur   24  Princi            MA\n",
      "2  Allahabad   22  Gaurav           MCA\n",
      "3    Kannuaj   32    Anuj           Phd \n",
      "\n",
      "      Address  Age    Name Qualification\n",
      "4     Nagpur   17    Abhi         Btech\n",
      "5     Kanpur   14  Ayushi           B.A\n",
      "6  Allahabad   12  Dhiraj          Bcom\n",
      "7    Kannuaj   52  Hitesh        B.hons\n"
     ]
    },
    {
     "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>17</td>\n",
       "      <td>Abhi</td>\n",
       "      <td>Btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>14</td>\n",
       "      <td>Ayushi</td>\n",
       "      <td>B.A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>12</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>52</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0     Nagpur   27     Jai           Msc\n",
       "1     Kanpur   24  Princi            MA\n",
       "2  Allahabad   22  Gaurav           MCA\n",
       "3    Kannuaj   32    Anuj           Phd\n",
       "4     Nagpur   17    Abhi         Btech\n",
       "5     Kanpur   14  Ayushi           B.A\n",
       "6  Allahabad   12  Dhiraj          Bcom\n",
       "7    Kannuaj   52  Hitesh        B.hons"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary containing employee data \n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "   \n",
    "# Define a dictionary containing employee data \n",
    "data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[17, 14, 12, 52], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']} \n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])\n",
    " \n",
    "print(df, \"\\n\\n\", df1) \n",
    "\n",
    "# using a .concat() method\n",
    "frames = [df, df1]\n",
    " \n",
    "res1 = pd.concat(frames)\n",
    "res1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age  Mobile No    Name Qualification\n",
      "0     Nagpur   27         97     Jai           Msc\n",
      "1     Kanpur   24         91  Princi            MA\n",
      "2  Allahabad   22         58  Gaurav           MCA\n",
      "3    Kannuaj   32         76    Anuj           Phd \n",
      "\n",
      "      Address  Age    Name Qualification  Salary\n",
      "2  Allahabad   22  Gaurav           MCA    1000\n",
      "3    Kannuaj   32    Anuj           Phd    2000\n",
      "6  Allahabad   12  Dhiraj          Bcom    3000\n",
      "7    Kannuaj   52  Hitesh        B.hons    4000\n"
     ]
    },
    {
     "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>76</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age  Mobile No    Name Qualification    Address  Age    Name  \\\n",
       "2  Allahabad   22         58  Gaurav           MCA  Allahabad   22  Gaurav   \n",
       "3    Kannuaj   32         76    Anuj           Phd    Kannuaj   32    Anuj   \n",
       "\n",
       "  Qualification  Salary  \n",
       "2           MCA    1000  \n",
       "3           Phd    2000  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary containing employee data \n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd'],\n",
    "        'Mobile No': [97, 91, 58, 76]} \n",
    "   \n",
    "# Define a dictionary containing employee data \n",
    "data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[22, 32, 12, 52], \n",
    "        'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],\n",
    "        'Salary':[1000, 2000, 3000, 4000]} \n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df1 = pd.DataFrame(data2, index=[2, 3, 6, 7]) \n",
    " \n",
    "print(df, \"\\n\\n\", df1) \n",
    "\n",
    "res2 = pd.concat([df, df1], axis=1, join='inner')\n",
    "res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address   Age  Mobile No    Name Qualification    Address   Age    Name  \\\n",
       "0     Nagpur  27.0       97.0     Jai           Msc        NaN   NaN     NaN   \n",
       "1     Kanpur  24.0       91.0  Princi            MA        NaN   NaN     NaN   \n",
       "2  Allahabad  22.0       58.0  Gaurav           MCA  Allahabad  22.0  Gaurav   \n",
       "3    Kannuaj  32.0       76.0    Anuj           Phd    Kannuaj  32.0    Anuj   \n",
       "6        NaN   NaN        NaN     NaN           NaN  Allahabad  12.0  Dhiraj   \n",
       "7        NaN   NaN        NaN     NaN           NaN    Kannuaj  52.0  Hitesh   \n",
       "\n",
       "  Qualification  Salary  \n",
       "0           NaN     NaN  \n",
       "1           NaN     NaN  \n",
       "2           MCA  1000.0  \n",
       "3           Phd  2000.0  \n",
       "6          Bcom  3000.0  \n",
       "7        B.hons  4000.0  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res2 = pd.concat([df, df1], axis=1, sort=False)\n",
    "res2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Mobile No</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>97</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>91</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>76</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age  Mobile No    Name Qualification    Address   Age    Name  \\\n",
       "0     Nagpur   27         97     Jai           Msc        NaN   NaN     NaN   \n",
       "1     Kanpur   24         91  Princi            MA        NaN   NaN     NaN   \n",
       "2  Allahabad   22         58  Gaurav           MCA  Allahabad  22.0  Gaurav   \n",
       "3    Kannuaj   32         76    Anuj           Phd    Kannuaj  32.0    Anuj   \n",
       "\n",
       "  Qualification  Salary  \n",
       "0           NaN     NaN  \n",
       "1           NaN     NaN  \n",
       "2           MCA  1000.0  \n",
       "3           Phd  2000.0  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res3 = pd.concat([df, df1], axis=1, join_axes=[df.index])\n",
    "res3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age    Name Qualification\n",
      "0     Nagpur   27     Jai           Msc\n",
      "1     Kanpur   24  Princi            MA\n",
      "2  Allahabad   22  Gaurav           MCA\n",
      "3    Kannuaj   32    Anuj           Phd \n",
      "\n",
      "      Address  Age    Name Qualification\n",
      "4     Nagpur   17    Abhi         Btech\n",
      "5     Kanpur   14  Ayushi           B.A\n",
      "6  Allahabad   12  Dhiraj          Bcom\n",
      "7    Kannuaj   52  Hitesh        B.hons\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
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       "      <th>Qualification</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>17</td>\n",
       "      <td>Abhi</td>\n",
       "      <td>Btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>14</td>\n",
       "      <td>Ayushi</td>\n",
       "      <td>B.A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>12</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>52</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0     Nagpur   27     Jai           Msc\n",
       "1     Kanpur   24  Princi            MA\n",
       "2  Allahabad   22  Gaurav           MCA\n",
       "3    Kannuaj   32    Anuj           Phd\n",
       "4     Nagpur   17    Abhi         Btech\n",
       "5     Kanpur   14  Ayushi           B.A\n",
       "6  Allahabad   12  Dhiraj          Bcom\n",
       "7    Kannuaj   52  Hitesh        B.hons"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary containing employee data \n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "   \n",
    "# Define a dictionary containing employee data \n",
    "data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[17, 14, 12, 52], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']} \n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])\n",
    " \n",
    "print(df, \"\\n\\n\", df1) \n",
    "\n",
    "# using append function \n",
    "res = df.append(df1)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age  Mobile No    Name Qualification\n",
      "0     Nagpur   27         97     Jai           Msc\n",
      "1     Kanpur   24         91  Princi            MA\n",
      "2  Allahabad   22         58  Gaurav           MCA\n",
      "3    Kannuaj   32         76    Anuj           Phd \n",
      "\n",
      "      Address  Age    Name Qualification  Salary\n",
      "2  Allahabad   22  Gaurav           MCA    1000\n",
      "3    Kannuaj   32    Anuj           Phd    2000\n",
      "6  Allahabad   12  Dhiraj          Bcom    3000\n",
      "7    Kannuaj   52  Hitesh        B.hons    4000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/py35/lib/python3.5/site-packages/ipykernel/__main__.py:23: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=True'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass sort=False\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Mobile No</th>\n",
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       "      <td>Jai</td>\n",
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       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
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       "      <td>91.0</td>\n",
       "      <td>Princi</td>\n",
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       "      <th>2</th>\n",
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       "      <td>58.0</td>\n",
       "      <td>Gaurav</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>Bcom</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>B.hons</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age  Mobile No    Name Qualification  Salary\n",
       "0     Nagpur   27       97.0     Jai           Msc     NaN\n",
       "1     Kanpur   24       91.0  Princi            MA     NaN\n",
       "2  Allahabad   22       58.0  Gaurav           MCA     NaN\n",
       "3    Kannuaj   32       76.0    Anuj           Phd     NaN\n",
       "4  Allahabad   22        NaN  Gaurav           MCA  1000.0\n",
       "5    Kannuaj   32        NaN    Anuj           Phd  2000.0\n",
       "6  Allahabad   12        NaN  Dhiraj          Bcom  3000.0\n",
       "7    Kannuaj   52        NaN  Hitesh        B.hons  4000.0"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd'],\n",
    "        'Mobile No': [97, 91, 58, 76]} \n",
    "    \n",
    "# Define a dictionary containing employee data \n",
    "data2 = {'Name':['Gaurav', 'Anuj', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[22, 32, 12, 52], \n",
    "        'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons'],\n",
    "        'Salary':[1000, 2000, 3000, 4000]} \n",
    "  \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    "  \n",
    "# Convert the dictionary into DataFrame  \n",
    "df1 = pd.DataFrame(data2, index=[2, 3, 6, 7]) \n",
    "\n",
    "print(df, \"\\n\\n\", df1)\n",
    "\n",
    "# using ignore_index\n",
    "res = pd.concat([df, df1], ignore_index=True)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age    Name Qualification\n",
      "0     Nagpur   27     Jai           Msc\n",
      "1     Kanpur   24  Princi            MA\n",
      "2  Allahabad   22  Gaurav           MCA\n",
      "3    Kannuaj   32    Anuj           Phd \n",
      "\n",
      "      Address  Age    Name Qualification\n",
      "4     Nagpur   17    Abhi         Btech\n",
      "5     Kanpur   14  Ayushi           B.A\n",
      "6  Allahabad   12  Dhiraj          Bcom\n",
      "7    Kannuaj   52  Hitesh        B.hons\n"
     ]
    },
    {
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       "    <tr>\n",
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       "      <td>Kanpur</td>\n",
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       "      <td>Ayushi</td>\n",
       "      <td>B.A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>12</td>\n",
       "      <td>Dhiraj</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>52</td>\n",
       "      <td>Hitesh</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Address  Age    Name Qualification\n",
       "x 0     Nagpur   27     Jai           Msc\n",
       "  1     Kanpur   24  Princi            MA\n",
       "  2  Allahabad   22  Gaurav           MCA\n",
       "  3    Kannuaj   32    Anuj           Phd\n",
       "y 4     Nagpur   17    Abhi         Btech\n",
       "  5     Kanpur   14  Ayushi           B.A\n",
       "  6  Allahabad   12  Dhiraj          Bcom\n",
       "  7    Kannuaj   52  Hitesh        B.hons"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define a dictionary containing employee data \n",
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "   \n",
    "# Define a dictionary containing employee data \n",
    "data2 = {'Name':['Abhi', 'Ayushi', 'Dhiraj', 'Hitesh'], \n",
    "        'Age':[17, 14, 12, 52], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']} \n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    " \n",
    "# Convert the dictionary into DataFrame  \n",
    "df1 = pd.DataFrame(data2, index=[4, 5, 6, 7])\n",
    " \n",
    "print(df, \"\\n\\n\", df1)  \n",
    "\n",
    "# using keys \n",
    "frames = [df, df1 ]\n",
    " \n",
    "res = pd.concat(frames, keys=['x', 'y'])\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Address  Age    Name Qualification\n",
      "0     Nagpur   27     Jai           Msc\n",
      "1     Kanpur   24  Princi            MA\n",
      "2  Allahabad   22  Gaurav           MCA\n",
      "3    Kannuaj   32    Anuj           Phd \n",
      "\n",
      " 0    1000\n",
      "1    2000\n",
      "2    3000\n",
      "3    4000\n",
      "Name: Salary, dtype: int64\n"
     ]
    },
    {
     "data": {
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       "      <td>22</td>\n",
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       "      <th>3</th>\n",
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       "      <td>32</td>\n",
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       "     Address  Age    Name Qualification  Salary\n",
       "0     Nagpur   27     Jai           Msc    1000\n",
       "1     Kanpur   24  Princi            MA    2000\n",
       "2  Allahabad   22  Gaurav           MCA    3000\n",
       "3    Kannuaj   32    Anuj           Phd    4000"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "   \n",
    "# Convert the dictionary into DataFrame  \n",
    "df = pd.DataFrame(data1,index=[0, 1, 2, 3])\n",
    " \n",
    "# creating a series\n",
    "s1 = pd.Series([1000, 2000, 3000, 4000], name='Salary')\n",
    " \n",
    "print(df, \"\\n\\n\", s1) \n",
    "\n",
    "# combining series and dataframe\n",
    "res = pd.concat([df, s1], axis=1)\n",
    " \n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Merge/Join Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>animal</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tiger</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>zebra</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     animal        food\n",
       "0  elephant  vegetables\n",
       "1     tiger        meat\n",
       "2  kangaroo  vegetables\n",
       "3     zebra  vegetables\n",
       "4   giraffe  vegetables"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo_eats = pd.DataFrame([['elephant','vegetables'], ['tiger','meat'], ['kangaroo','vegetables'], ['zebra','vegetables'], ['giraffe','vegetables']], columns=['animal', 'food'])\n",
    "zoo_eats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "0   elephant     1001         500\n",
       "1   elephant     1002         600\n",
       "2   elephant     1003         550\n",
       "3      tiger     1004         300\n",
       "4      tiger     1005         350\n",
       "5      tiger     1006         400\n",
       "6      tiger     1007         450\n",
       "7   kangaroo     1008         200\n",
       "8   kangaroo     1009         250\n",
       "9      zebra     1010         300\n",
       "10     zebra     1011         350\n",
       "11   giraffe     1012         550\n",
       "12   giraffe     1013         600\n",
       "13   giraffe     1014         700\n",
       "14   giraffe     1015         810\n",
       "15   giraffe     1016         870"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo = pd.DataFrame([['elephant',1001,500], ['elephant',1002,600], ['elephant',1003,550], \n",
    "                    ['tiger',1004,300], ['tiger',1005,350], ['tiger',1006,400], ['tiger',1007,450],\n",
    "                    ['kangaroo',1008, 200], ['kangaroo',1009,250], \n",
    "                    ['zebra',1010,300], ['zebra',1011,350],\n",
    "                    ['giraffe',1012,550], ['giraffe',1013,600], ['giraffe',1014,700], ['giraffe',1015,810], ['giraffe',1016,870],\n",
    "                   ], columns=['animal', 'uniq_id', 'water_need'])\n",
    "zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need        food\n",
       "0   elephant     1001         500  vegetables\n",
       "1   elephant     1002         600  vegetables\n",
       "2   elephant     1003         550  vegetables\n",
       "3      tiger     1004         300        meat\n",
       "4      tiger     1005         350        meat\n",
       "5      tiger     1006         400        meat\n",
       "6      tiger     1007         450        meat\n",
       "7   kangaroo     1008         200  vegetables\n",
       "8   kangaroo     1009         250  vegetables\n",
       "9      zebra     1010         300  vegetables\n",
       "10     zebra     1011         350  vegetables\n",
       "11   giraffe     1012         550  vegetables\n",
       "12   giraffe     1013         600  vegetables\n",
       "13   giraffe     1014         700  vegetables\n",
       "14   giraffe     1015         810  vegetables\n",
       "15   giraffe     1016         870  vegetables"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.merge(zoo_eats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need        food\n",
       "0   elephant     1001         500  vegetables\n",
       "1   elephant     1002         600  vegetables\n",
       "2   elephant     1003         550  vegetables\n",
       "3      tiger     1004         300        meat\n",
       "4      tiger     1005         350        meat\n",
       "5      tiger     1006         400        meat\n",
       "6      tiger     1007         450        meat\n",
       "7   kangaroo     1008         200  vegetables\n",
       "8   kangaroo     1009         250  vegetables\n",
       "9      zebra     1010         300  vegetables\n",
       "10     zebra     1011         350  vegetables\n",
       "11   giraffe     1012         550  vegetables\n",
       "12   giraffe     1013         600  vegetables\n",
       "13   giraffe     1014         700  vegetables\n",
       "14   giraffe     1015         810  vegetables\n",
       "15   giraffe     1016         870  vegetables"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.merge(zoo_eats, how=\"outer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "      <th>food</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "      <td>meat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "      <td>vegetables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need        food\n",
       "0   elephant     1001         500  vegetables\n",
       "1   elephant     1002         600  vegetables\n",
       "2   elephant     1003         550  vegetables\n",
       "3      tiger     1004         300        meat\n",
       "4      tiger     1005         350        meat\n",
       "5      tiger     1006         400        meat\n",
       "6      tiger     1007         450        meat\n",
       "7   kangaroo     1008         200  vegetables\n",
       "8   kangaroo     1009         250  vegetables\n",
       "9      zebra     1010         300  vegetables\n",
       "10     zebra     1011         350  vegetables\n",
       "11   giraffe     1012         550  vegetables\n",
       "12   giraffe     1013         600  vegetables\n",
       "13   giraffe     1014         700  vegetables\n",
       "14   giraffe     1015         810  vegetables\n",
       "15   giraffe     1016         870  vegetables"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.merge(zoo_eats, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x\n",
      "0  0\n",
      "1  1\n",
      "2  2\n",
      "3  3\n",
      "4  4\n",
      "   x\n",
      "0  3\n",
      "1  4\n",
      "2  5\n",
      "3  6\n",
      "4  7\n"
     ]
    }
   ],
   "source": [
    "A = pd.DataFrame({'x':np.arange(5)})\n",
    "B = pd.DataFrame({'x':np.arange(3,8)})\n",
    "print(A)\n",
    "print(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>x</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x      _merge\n",
       "0  0   left_only\n",
       "1  1   left_only\n",
       "2  2   left_only\n",
       "3  3        both\n",
       "4  4        both\n",
       "5  5  right_only\n",
       "6  6  right_only\n",
       "7  7  right_only"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(A,B, how='outer', indicator=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Age    Name key key1\n",
      "0   27     Jai  K0   K0\n",
      "1   24  Princi  K1   K1\n",
      "2   22  Gaurav  K2   K0\n",
      "3   32    Anuj  K3   K1 \n",
      "\n",
      "      Address Qualification key key1\n",
      "0     Nagpur         Btech  K0   K0\n",
      "1     Kanpur           B.A  K1   K0\n",
      "2  Allahabad          Bcom  K2   K0\n",
      "3    Kannuaj        B.hons  K3   K0\n"
     ]
    },
    {
     "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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>key</th>\n",
       "      <th>key1</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>K0</td>\n",
       "      <td>K0</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>Btech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>K2</td>\n",
       "      <td>K0</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age    Name key key1    Address Qualification\n",
       "0   27     Jai  K0   K0     Nagpur         Btech\n",
       "1   22  Gaurav  K2   K0  Allahabad          Bcom"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "         'key1': ['K0', 'K1', 'K0', 'K1'],\n",
    "         'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32],} \n",
    "data2 = {'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "         'key1': ['K0', 'K0', 'K0', 'K0'],\n",
    "         'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Btech', 'B.A', 'Bcom', 'B.hons']} \n",
    "df = pd.DataFrame(data1)\n",
    "df1 = pd.DataFrame(data2) \n",
    "print(df, \"\\n\\n\", df1) \n",
    "\n",
    "# merging dataframe using multiple keys\n",
    "res1 = pd.merge(df, df1, on=['key', 'key1'])\n",
    "res1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Left Anti and Right Ani Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Anti\n"
     ]
    },
    {
     "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>x</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x     _merge\n",
       "0  0  left_only\n",
       "1  1  left_only\n",
       "2  2  left_only"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(A,B, how='outer', indicator=True)\n",
    "print(\"Left Anti\")\n",
    "merged[merged['_merge'] =='left_only']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right Anti\n"
     ]
    },
    {
     "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>x</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x      _merge\n",
       "5  5  right_only\n",
       "6  6  right_only\n",
       "7  7  right_only"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Right Anti\")\n",
    "merged[merged['_merge'] =='right_only']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x\n",
       "0  0\n",
       "1  1\n",
       "2  2"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also like this\n",
    "A[~A['x'].isin(B['x'])] # left Anti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x\n",
       "2  5\n",
       "3  6\n",
       "4  7"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Also like this\n",
    "B[~B['x'].isin(A['x'])] # RIght Anti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Age    Name\n",
      "K0   27     Jai\n",
      "K1   24  Princi\n",
      "K2   22  Gaurav\n",
      "K3   32    Anuj \n",
      "\n",
      "       Address Qualification\n",
      "K0  Allahabad           MCA\n",
      "K2    Kannuaj           Phd\n",
      "K3  Allahabad          Bcom\n",
      "K4    Kannuaj        B.hons\n"
     ]
    },
    {
     "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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age    Name    Address Qualification\n",
       "K0   27     Jai  Allahabad           MCA\n",
       "K1   24  Princi        NaN           NaN\n",
       "K2   22  Gaurav    Kannuaj           Phd\n",
       "K3   32    Anuj  Allahabad          Bcom"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32]} \n",
    "data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']} \n",
    "df = pd.DataFrame(data1,index=['K0', 'K1', 'K2', 'K3'])\n",
    "df1 = pd.DataFrame(data2, index=['K0', 'K2', 'K3', 'K4'])\n",
    "print(df, \"\\n\\n\", df1)  \n",
    "res = df.join(df1) \n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>24.0</td>\n",
       "      <td>Princi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age    Name    Address Qualification\n",
       "K0  27.0     Jai  Allahabad           MCA\n",
       "K1  24.0  Princi        NaN           NaN\n",
       "K2  22.0  Gaurav    Kannuaj           Phd\n",
       "K3  32.0    Anuj  Allahabad          Bcom\n",
       "K4   NaN     NaN    Kannuaj        B.hons"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res1 = df.join(df1, how='outer')\n",
    "res1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Age    Name    Address Qualification\n",
       "K0   27     Jai  Allahabad           MCA\n",
       "K2   22  Gaurav    Kannuaj           Phd\n",
       "K3   32    Anuj  Allahabad          Bcom"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res1 = df.join(df1, how='inner')\n",
    "res1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join multi-indexed dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Age    Name\n",
      "key             \n",
      "K0    27     Jai\n",
      "K1    24  Princi\n",
      "K2    22  Gaurav \n",
      "\n",
      "           Address Qualification\n",
      "key Y                          \n",
      "K0  Y0  Allahabad           MCA\n",
      "K1  Y1    Kannuaj           Phd\n",
      "K2  Y2  Allahabad          Bcom\n",
      "    Y3     Kanpur        B.hons\n"
     ]
    },
    {
     "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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th>Y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <th>Y0</th>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <th>Y1</th>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">K2</th>\n",
       "      <th>Y2</th>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Allahabad</td>\n",
       "      <td>Bcom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y3</th>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>B.hons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age    Name    Address Qualification\n",
       "key Y                                       \n",
       "K0  Y0   27     Jai  Allahabad           MCA\n",
       "K1  Y1   24  Princi    Kannuaj           Phd\n",
       "K2  Y2   22  Gaurav  Allahabad          Bcom\n",
       "    Y3   22  Gaurav     Kanpur        B.hons"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = {'Name':['Jai', 'Princi', 'Gaurav'], \n",
    "        'Age':[27, 24, 22]} \n",
    "data2 = {'Address':['Allahabad', 'Kannuaj', 'Allahabad', 'Kanpur'], \n",
    "        'Qualification':['MCA', 'Phd', 'Bcom', 'B.hons']} \n",
    "df = pd.DataFrame(data1, index=pd.Index(['K0', 'K1', 'K2'], name='key')) \n",
    "index = pd.MultiIndex.from_tuples([('K0', 'Y0'), ('K1', 'Y1'),\n",
    "                                   ('K2', 'Y2'), ('K2', 'Y3')],\n",
    "                                   names=['key', 'Y'])\n",
    "df1 = pd.DataFrame(data2, index= index)\n",
    "print(df, \"\\n\\n\", df1)\n",
    "# joining singly indexed with multi indexed\n",
    "result = df.join(df1, how='inner')\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "7   kangaroo     1008         200\n",
       "8   kangaroo     1009         250\n",
       "3      tiger     1004         300\n",
       "9      zebra     1010         300\n",
       "4      tiger     1005         350\n",
       "10     zebra     1011         350\n",
       "5      tiger     1006         400\n",
       "6      tiger     1007         450\n",
       "0   elephant     1001         500\n",
       "2   elephant     1003         550\n",
       "11   giraffe     1012         550\n",
       "1   elephant     1002         600\n",
       "12   giraffe     1013         600\n",
       "13   giraffe     1014         700\n",
       "14   giraffe     1015         810\n",
       "15   giraffe     1016         870"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.sort_values('water_need')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "0   elephant     1001         500\n",
       "2   elephant     1003         550\n",
       "1   elephant     1002         600\n",
       "11   giraffe     1012         550\n",
       "12   giraffe     1013         600\n",
       "13   giraffe     1014         700\n",
       "14   giraffe     1015         810\n",
       "15   giraffe     1016         870\n",
       "7   kangaroo     1008         200\n",
       "8   kangaroo     1009         250\n",
       "3      tiger     1004         300\n",
       "4      tiger     1005         350\n",
       "5      tiger     1006         400\n",
       "6      tiger     1007         450\n",
       "9      zebra     1010         300\n",
       "10     zebra     1011         350"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.sort_values(by=['animal','water_need'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
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       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "10     zebra     1011         350\n",
       "9      zebra     1010         300\n",
       "6      tiger     1007         450\n",
       "5      tiger     1006         400\n",
       "4      tiger     1005         350\n",
       "3      tiger     1004         300\n",
       "8   kangaroo     1009         250\n",
       "7   kangaroo     1008         200\n",
       "15   giraffe     1016         870\n",
       "14   giraffe     1015         810\n",
       "13   giraffe     1014         700\n",
       "12   giraffe     1013         600\n",
       "11   giraffe     1012         550\n",
       "1   elephant     1002         600\n",
       "2   elephant     1003         550\n",
       "0   elephant     1001         500"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.sort_values(by=['animal','water_need'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reset Index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
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       "      <td>450</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11</td>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2</td>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index    animal  uniq_id  water_need\n",
       "0      10     zebra     1011         350\n",
       "1       9     zebra     1010         300\n",
       "2       6     tiger     1007         450\n",
       "3       5     tiger     1006         400\n",
       "4       4     tiger     1005         350\n",
       "5       3     tiger     1004         300\n",
       "6       8  kangaroo     1009         250\n",
       "7       7  kangaroo     1008         200\n",
       "8      15   giraffe     1016         870\n",
       "9      14   giraffe     1015         810\n",
       "10     13   giraffe     1014         700\n",
       "11     12   giraffe     1013         600\n",
       "12     11   giraffe     1012         550\n",
       "13      1  elephant     1002         600\n",
       "14      2  elephant     1003         550\n",
       "15      0  elephant     1001         500"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.sort_values(by=['animal','water_need'], ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zebra</td>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
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       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
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       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
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       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
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       "      <td>200</td>\n",
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       "      <th>8</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "0      zebra     1011         350\n",
       "1      zebra     1010         300\n",
       "2      tiger     1007         450\n",
       "3      tiger     1006         400\n",
       "4      tiger     1005         350\n",
       "5      tiger     1004         300\n",
       "6   kangaroo     1009         250\n",
       "7   kangaroo     1008         200\n",
       "8    giraffe     1016         870\n",
       "9    giraffe     1015         810\n",
       "10   giraffe     1014         700\n",
       "11   giraffe     1013         600\n",
       "12   giraffe     1012         550\n",
       "13  elephant     1002         600\n",
       "14  elephant     1003         550\n",
       "15  elephant     1001         500"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.sort_values(by=['animal','water_need'], ascending=False).reset_index(drop=True) # drop previous indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>kangaroo</td>\n",
       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>250</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>300</td>\n",
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       "    <tr>\n",
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       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>550</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  water_need\n",
       "0   elephant         500\n",
       "1   elephant         600\n",
       "2   elephant         550\n",
       "3      tiger         300\n",
       "4      tiger         350\n",
       "5      tiger         400\n",
       "6      tiger         450\n",
       "7   kangaroo         200\n",
       "8   kangaroo         250\n",
       "9      zebra         300\n",
       "10     zebra         350\n",
       "11   giraffe         550\n",
       "12   giraffe         600\n",
       "13   giraffe         700\n",
       "14   giraffe         810\n",
       "15   giraffe         870"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.drop(['uniq_id'], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>uniq_id</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1001</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1002</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>1003</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1004</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1005</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1006</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>1007</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1008</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>1009</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1010</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>1011</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1012</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1013</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1014</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1015</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>1016</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  uniq_id  water_need\n",
       "0   elephant     1001         500\n",
       "1   elephant     1002         600\n",
       "2   elephant     1003         550\n",
       "3      tiger     1004         300\n",
       "4      tiger     1005         350\n",
       "5      tiger     1006         400\n",
       "6      tiger     1007         450\n",
       "7   kangaroo     1008         200\n",
       "8   kangaroo     1009         250\n",
       "9      zebra     1010         300\n",
       "10     zebra     1011         350\n",
       "11   giraffe     1012         550\n",
       "12   giraffe     1013         600\n",
       "13   giraffe     1014         700\n",
       "14   giraffe     1015         810\n",
       "15   giraffe     1016         870"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo.drop(['uniq_id'], axis =1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>500</td>\n",
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       "      <td>elephant</td>\n",
       "      <td>600</td>\n",
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       "      <td>elephant</td>\n",
       "      <td>550</td>\n",
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       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>300</td>\n",
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       "      <td>tiger</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
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       "      <td>kangaroo</td>\n",
       "      <td>200</td>\n",
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       "      <td>kangaroo</td>\n",
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       "      <td>giraffe</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  water_need\n",
       "0   elephant         500\n",
       "1   elephant         600\n",
       "2   elephant         550\n",
       "3      tiger         300\n",
       "4      tiger         350\n",
       "5      tiger         400\n",
       "6      tiger         450\n",
       "7   kangaroo         200\n",
       "8   kangaroo         250\n",
       "9      zebra         300\n",
       "10     zebra         350\n",
       "11   giraffe         550\n",
       "12   giraffe         600\n",
       "13   giraffe         700\n",
       "14   giraffe         810\n",
       "15   giraffe         870"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo.drop(7, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>water_need</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>5</th>\n",
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       "      <td>zebra</td>\n",
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       "      <td>550</td>\n",
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       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  water_need\n",
       "0   elephant         500\n",
       "1   elephant         600\n",
       "2   elephant         550\n",
       "3      tiger         300\n",
       "4      tiger         350\n",
       "5      tiger         400\n",
       "6      tiger         450\n",
       "8   kangaroo         250\n",
       "9      zebra         300\n",
       "10     zebra         350\n",
       "11   giraffe         550\n",
       "12   giraffe         600\n",
       "13   giraffe         700\n",
       "14   giraffe         810\n",
       "15   giraffe         870"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>water_need</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>500</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>550</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>350</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>450</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>300</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>550</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  water_need    type\n",
       "0   elephant         500  animal\n",
       "2   elephant         550  animal\n",
       "4      tiger         350  animal\n",
       "6      tiger         450  animal\n",
       "9      zebra         300  animal\n",
       "11   giraffe         550  animal\n",
       "13   giraffe         700  animal\n",
       "15   giraffe         870  animal"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo[[True,False,True,False,True,False,True,False,True,False,True,False,True,False,True]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     animal  water_need\n",
       "0  elephant         500\n",
       "1  elephant         600\n",
       "2  elephant         550"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.query('animal == \"elephant\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Where"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>animal</th>\n",
       "      <th>water_need</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     animal  water_need\n",
       "0       NaN         NaN\n",
       "1       NaN         NaN\n",
       "2       NaN         NaN\n",
       "3       NaN         NaN\n",
       "4       NaN         NaN\n",
       "5       NaN         NaN\n",
       "6       NaN         NaN\n",
       "8       NaN         NaN\n",
       "9       NaN         NaN\n",
       "10      NaN         NaN\n",
       "11      NaN         NaN\n",
       "12  giraffe       600.0\n",
       "13  giraffe       700.0\n",
       "14  giraffe       810.0\n",
       "15  giraffe       870.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.where((zoo[\"animal\"] == 'giraffe') & (zoo[\"water_need\"] >=600))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>animal</th>\n",
       "      <th>water_need</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>elephant</td>\n",
       "      <td>500</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elephant</td>\n",
       "      <td>600</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>elephant</td>\n",
       "      <td>550</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tiger</td>\n",
       "      <td>300</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tiger</td>\n",
       "      <td>350</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tiger</td>\n",
       "      <td>400</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tiger</td>\n",
       "      <td>450</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>kangaroo</td>\n",
       "      <td>250</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>zebra</td>\n",
       "      <td>300</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>zebra</td>\n",
       "      <td>350</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>550</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>600</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>700</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>810</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>giraffe</td>\n",
       "      <td>870</td>\n",
       "      <td>animal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      animal  water_need    type\n",
       "0   elephant         500  animal\n",
       "1   elephant         600  animal\n",
       "2   elephant         550  animal\n",
       "3      tiger         300  animal\n",
       "4      tiger         350  animal\n",
       "5      tiger         400  animal\n",
       "6      tiger         450  animal\n",
       "8   kangaroo         250  animal\n",
       "9      zebra         300  animal\n",
       "10     zebra         350  animal\n",
       "11   giraffe         550  animal\n",
       "12   giraffe         600  animal\n",
       "13   giraffe         700  animal\n",
       "14   giraffe         810  animal\n",
       "15   giraffe         870  animal"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.insert(2, \"type\", \"animal\")\n",
    "zoo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change Datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zoo[\"water_need\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zoo.water_need[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(zoo.water_need.astype('float64')[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterate through Dataframe rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     elephant\n",
      "1     elephant\n",
      "2     elephant\n",
      "3        tiger\n",
      "4        tiger\n",
      "5        tiger\n",
      "6        tiger\n",
      "8     kangaroo\n",
      "9        zebra\n",
      "10       zebra\n",
      "11     giraffe\n",
      "12     giraffe\n",
      "13     giraffe\n",
      "14     giraffe\n",
      "15     giraffe\n",
      "Name: animal, dtype: object\n",
      "0     500\n",
      "1     600\n",
      "2     550\n",
      "3     300\n",
      "4     350\n",
      "5     400\n",
      "6     450\n",
      "8     250\n",
      "9     300\n",
      "10    350\n",
      "11    550\n",
      "12    600\n",
      "13    700\n",
      "14    810\n",
      "15    870\n",
      "Name: water_need, dtype: int64\n",
      "0     animal\n",
      "1     animal\n",
      "2     animal\n",
      "3     animal\n",
      "4     animal\n",
      "5     animal\n",
      "6     animal\n",
      "8     animal\n",
      "9     animal\n",
      "10    animal\n",
      "11    animal\n",
      "12    animal\n",
      "13    animal\n",
      "14    animal\n",
      "15    animal\n",
      "Name: type, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i, j in zoo.iteritems():\n",
    "    print(j) # Returns Series Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas(Index=0, animal='elephant', water_need=500, type='animal')\n",
      "Pandas(Index=1, animal='elephant', water_need=600, type='animal')\n",
      "Pandas(Index=2, animal='elephant', water_need=550, type='animal')\n",
      "Pandas(Index=3, animal='tiger', water_need=300, type='animal')\n",
      "Pandas(Index=4, animal='tiger', water_need=350, type='animal')\n",
      "Pandas(Index=5, animal='tiger', water_need=400, type='animal')\n",
      "Pandas(Index=6, animal='tiger', water_need=450, type='animal')\n",
      "Pandas(Index=8, animal='kangaroo', water_need=250, type='animal')\n",
      "Pandas(Index=9, animal='zebra', water_need=300, type='animal')\n",
      "Pandas(Index=10, animal='zebra', water_need=350, type='animal')\n",
      "Pandas(Index=11, animal='giraffe', water_need=550, type='animal')\n",
      "Pandas(Index=12, animal='giraffe', water_need=600, type='animal')\n",
      "Pandas(Index=13, animal='giraffe', water_need=700, type='animal')\n",
      "Pandas(Index=14, animal='giraffe', water_need=810, type='animal')\n",
      "Pandas(Index=15, animal='giraffe', water_need=870, type='animal')\n"
     ]
    }
   ],
   "source": [
    "for i in zoo.itertuples():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "animal\n",
      "water_need\n",
      "type\n"
     ]
    }
   ],
   "source": [
    "for i in zoo.columns:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     elephant\n",
      "1     elephant\n",
      "2     elephant\n",
      "3        tiger\n",
      "4        tiger\n",
      "5        tiger\n",
      "6        tiger\n",
      "8     kangaroo\n",
      "9        zebra\n",
      "10       zebra\n",
      "11     giraffe\n",
      "12     giraffe\n",
      "13     giraffe\n",
      "14     giraffe\n",
      "15     giraffe\n",
      "Name: animal, dtype: object\n",
      "0     500\n",
      "1     600\n",
      "2     550\n",
      "3     300\n",
      "4     350\n",
      "5     400\n",
      "6     450\n",
      "8     250\n",
      "9     300\n",
      "10    350\n",
      "11    550\n",
      "12    600\n",
      "13    700\n",
      "14    810\n",
      "15    870\n",
      "Name: water_need, dtype: int64\n",
      "0     animal\n",
      "1     animal\n",
      "2     animal\n",
      "3     animal\n",
      "4     animal\n",
      "5     animal\n",
      "6     animal\n",
      "8     animal\n",
      "9     animal\n",
      "10    animal\n",
      "11    animal\n",
      "12    animal\n",
      "13    animal\n",
      "14    animal\n",
      "15    animal\n",
      "Name: type, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i in list(zoo):\n",
    "    print(zoo[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elephant\n",
      "550\n",
      "animal\n"
     ]
    }
   ],
   "source": [
    "for i in list(zoo):\n",
    "    print(zoo[i][2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with text data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>27</td>\n",
       "      <td>jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannauj</td>\n",
       "      <td>32</td>\n",
       "      <td>anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0      Delhi   27     jai           Msc\n",
       "1     Kanpur   24  princi            MA\n",
       "2  Allahabad   22  gaurav           MCA\n",
       "3    Kannauj   32    anuj           Phd"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "df = pd.DataFrame(data) \n",
    "df[\"Name\"]= df[\"Name\"].str.lower() \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>27</td>\n",
       "      <td>JAI</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>PRINCI</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>GAURAV</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannauj</td>\n",
       "      <td>32</td>\n",
       "      <td>ANUJ</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0      Delhi   27     JAI           Msc\n",
       "1     Kanpur   24  PRINCI            MA\n",
       "2  Allahabad   22  GAURAV           MCA\n",
       "3    Kannauj   32    ANUJ           Phd"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Name\"]= df[\"Name\"].str.upper() \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split and replace data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Knnuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0     Nagpur   27     Jai           Msc\n",
       "1     Kanpur   24  Princi            MA\n",
       "2  Allahabad   22  Gaurav           MCA\n",
       "3     Knnuaj   32    Anuj           Phd"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Knnuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "df = pd.DataFrame(data) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>K</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Knnu</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Address  Age    Name Qualification\n",
       "0       N   27     Jai           Msc\n",
       "1       K   24  Princi            MA\n",
       "2     All   22  Gaurav           MCA\n",
       "3    Knnu   32    Anuj           Phd"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace = True) \n",
    "df[\"Address\"]= df[\"Address\"].str.split(\"a\", n = 1, expand = True) \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age    Name Qualification\n",
       "0     Nagpur   27     Jai           Msc\n",
       "1     Kanpur   24  Princi            MA\n",
       "2  Allahabad   22  Gaurav           MCA\n",
       "3    Kannuaj   32    Anuj           Phd"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur', 'Kanpur', 'Allahabad', 'Kannuaj'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "df = pd.DataFrame(data) \n",
    "df"
   ]
  },
  {
   "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai, Nagpur</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi, Kanpur</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Allahabad</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav, Allahabad</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj, Kannuaj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Address  Age               Name Qualification\n",
       "0     Nagpur   27        Jai, Nagpur           Msc\n",
       "1     Kanpur   24     Princi, Kanpur            MA\n",
       "2  Allahabad   22  Gaurav, Allahabad           MCA\n",
       "3    Kannuaj   32      Anuj, Kannuaj           Phd"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making copy of address column \n",
    "new = df[\"Address\"].copy()   \n",
    "# concatenating address with name column \n",
    "# overwriting name column \n",
    "df[\"Name\"]= df[\"Name\"].str.cat(new, sep =\", \") \n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remove Whitespaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>Address</th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>Qualification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nagpur junction</td>\n",
       "      <td>27</td>\n",
       "      <td>Jai</td>\n",
       "      <td>Msc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kanpur junction</td>\n",
       "      <td>24</td>\n",
       "      <td>Princi</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nagpur junction</td>\n",
       "      <td>22</td>\n",
       "      <td>Gaurav</td>\n",
       "      <td>MCA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kannuaj junction</td>\n",
       "      <td>32</td>\n",
       "      <td>Anuj</td>\n",
       "      <td>Phd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Address  Age    Name Qualification\n",
       "0   Nagpur junction   27     Jai           Msc\n",
       "1   Kanpur junction   24  Princi            MA\n",
       "2   Nagpur junction   22  Gaurav           MCA\n",
       "3  Kannuaj junction   32    Anuj           Phd"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'], \n",
    "        'Age':[27, 24, 22, 32], \n",
    "        'Address':['Nagpur junction', 'Kanpur junction', \n",
    "                   'Nagpur junction', 'Kannuaj junction'], \n",
    "        'Qualification':['Msc', 'MA', 'MCA', 'Phd']} \n",
    "df = pd.DataFrame(data)\n",
    "df   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "Name: Address, dtype: bool\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "Name: Address, dtype: bool\n",
      "0    False\n",
      "1    False\n",
      "2    False\n",
      "3    False\n",
      "Name: Address, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# replacing address name and adding spaces in start and end \n",
    "new = df[\"Address\"].replace(\"Nagpur junction\", \"  Nagpur junction  \").copy() \n",
    "# checking with custom string \n",
    "print(new.str.strip()==\" Nagpur junction\")\n",
    "print(new.str.strip()==\"Nagpur junction \")\n",
    "print(new.str.strip()==\" Nagpur junction \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Data"
   ]
  },
  {
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1\n",
       "0    a    1\n",
       "1    b    2\n",
       "2  NaN  NaN"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['a1', 'b2', 'c3'])\n",
    "# Extracting a data\n",
    "n= s.str.extract(r'([ab])(\\d)')\n",
    " \n",
    "n"
   ]
  },
  {
   "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>Geeks</th>\n",
       "      <th>For</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Geeks  For\n",
       "0     a    1\n",
       "1     b    2\n",
       "2   NaN  NaN"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['a1', 'b2', 'c3'])\n",
    " \n",
    "# Extracting a data\n",
    "n = s.str.extract(r'(?P<Geeks>[ab])(?P<For>\\d)')\n",
    "n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Date and Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2011-01-01 00:00:00', '2011-01-01 01:00:00',\n",
       "               '2011-01-01 02:00:00', '2011-01-01 03:00:00',\n",
       "               '2011-01-01 04:00:00', '2011-01-01 05:00:00',\n",
       "               '2011-01-01 06:00:00', '2011-01-01 07:00:00',\n",
       "               '2011-01-01 08:00:00', '2011-01-01 09:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.date_range('1/1/2011', periods = 10, freq ='H')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2020)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "x = datetime.now()\n",
    "x.month, x.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date  year  month  day  hour  minute\n",
       "0 2011-01-01 00:00:00  2011      1    1     0       0\n",
       "1 2011-01-01 01:00:00  2011      1    1     1       0\n",
       "2 2011-01-01 02:00:00  2011      1    1     2       0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.DataFrame()\n",
    "rng['date'] = pd.date_range('1/1/2011', periods = 72, freq ='H')\n",
    " \n",
    "# Print the dates in dd-mm-yy format\n",
    "rng[:5]\n",
    " \n",
    "# Create features for year, month, day, hour, and minute\n",
    "rng['year'] = rng['date'].dt.year\n",
    "rng['month'] = rng['date'].dt.month\n",
    "rng['day'] = rng['date'].dt.day\n",
    "rng['hour'] = rng['date'].dt.hour\n",
    "rng['minute'] = rng['date'].dt.minute\n",
    " \n",
    "# Print the dates divided into features\n",
    "rng.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO, BytesIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('col1,col2,col3\\n'\n",
    "     'x,y.,1\\n'\n",
    "     'a,b,2\\n'\n",
    "     'c,d,3\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.StringIO at 0x7f7a9f79a048>"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StringIO()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>y.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1 col2  col3\n",
       "0    x   y.     1\n",
       "1    a    b     2\n",
       "2    c    d     3"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(StringIO(data), usecols=['col1','col3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "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>col1</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  col1  col3\n",
       "0    x     1\n",
       "1    a     2\n",
       "2    c     3"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('a,b,c,d\\n'\n",
    "     '1,2,3,4\\n'\n",
    "     '5,6,7,8\\n'\n",
    "     '9,10,11\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a,b,c,d\n",
      "1,2,3,4\n",
      "5,6,7,8\n",
      "9,10,11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data), dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    5\n",
       "2    9\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5'"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('a,b,c,d\\n'\n",
    "     '1,2,3,4\\n'\n",
    "     '5,6,7,8\\n'\n",
    "     '9,10,11,12\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a     b     c     d\n",
       "0  1.0   2.0   3.0   4.0\n",
       "1  5.0   6.0   7.0   8.0\n",
       "2  9.0  10.0  11.0  12.0"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    5.0\n",
       "2    9.0\n",
       "Name: a, dtype: float64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(StringIO(data), dtype={'a': object, 'b':float, 'c':int})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "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",
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       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a     b   c   d\n",
       "0  1   2.0   3   4\n",
       "1  5   6.0   7   8\n",
       "2  9  10.0  11  12"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    5\n",
       "2    9\n",
       "Name: a, dtype: object"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1     6.0\n",
       "2    10.0\n",
       "Name: b, dtype: float64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3\n",
       "1     7\n",
       "2    11\n",
       "Name: c, dtype: int64"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['c']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     object\n",
       "b    float64\n",
       "c      int64\n",
       "d      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('index,a,b,c\\n'\n",
    "     '4,apple,bat,5.7\\n'\n",
    "     '8,orange,cat,10\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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",
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       "</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",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>orange</td>\n",
       "      <td>cat</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            a    b     c\n",
       "index                   \n",
       "4       apple  bat   5.7\n",
       "8      orange  cat  10.0"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data), index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('a,b,c\\n'\n",
    "     '4,apple,bat,\\n'\n",
    "     '8,orange,cat,\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "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>4</th>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>orange</td>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a    b   c\n",
       "4   apple  bat NaN\n",
       "8  orange  cat NaN"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('a,b,c\\n'\n",
    "     '4,apple,bat,\\n'\n",
    "     '8,orange,cat,\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>orange</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a       b    c\n",
       "0  4   apple  bat\n",
       "1  8  orange  cat"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data), index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=('a,b,c\\n'\n",
    "     '4,apple,bat,\\n'\n",
    "     '8,orange,cat,\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>bat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>orange</td>\n",
       "      <td>cat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        b    c\n",
       "0   apple  bat\n",
       "1  orange  cat"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data), usecols=['b','c'], index_col=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "data='a,b\\n\"hello , \\\\\"Bob\\\\\", nice to see you\",5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "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>hello , \"Bob\", nice to see you</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                a  b\n",
       "0  hello , \"Bob\", nice to see you  5"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(StringIO(data), escapechar=\"\\\\\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('https://download.bls.gov/pub/time.series/cu/cu.item', \n",
    "#                  sep='\\t')\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read JSON to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "Data = '{\"employee_name\": \"James\", \"email\": \"james@gmail.com\", \"job_profile\": [{\"title1\": \"Team Lead\", \"title2\": \"Sr. Developer\"}]}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_json(Data) # header=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>email</th>\n",
       "      <th>employee_name</th>\n",
       "      <th>job_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>james@gmail.com</td>\n",
       "      <td>James</td>\n",
       "      <td>{'title1': 'Team Lead', 'title2': 'Sr. Develop...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             email employee_name  \\\n",
       "0  james@gmail.com         James   \n",
       "\n",
       "                                         job_profile  \n",
       "0  {'title1': 'Team Lead', 'title2': 'Sr. Develop...  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1      2     3     4     5    6     7     8     9     10     11  \\\n",
       "0  NaN   0   1.00  2.00  3.00   4.0    5  6.00  7.00  8.00  9.00  10.00   \n",
       "1  0.0   1  14.23  1.71  2.43  15.6  127  2.80  3.06  0.28  2.29   5.64   \n",
       "2  1.0   1  13.20  1.78  2.14  11.2  100  2.65  2.76  0.26  1.28   4.38   \n",
       "3  2.0   1  13.16  2.36  2.67  18.6  101  2.80  3.24  0.30  2.81   5.68   \n",
       "4  3.0   1  14.37  1.95  2.50  16.8  113  3.85  3.49  0.24  2.18   7.80   \n",
       "\n",
       "      12     13    14  \n",
       "0  11.00  12.00    13  \n",
       "1   1.04   3.92  1065  \n",
       "2   1.05   3.40  1050  \n",
       "3   1.03   3.17  1185  \n",
       "4   0.86   3.45  1480  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('wine.csv', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"email\":{\"0\":\"james@gmail.com\"},\"employee_name\":{\"0\":\"James\"},\"job_profile\":{\"0\":{\"title1\":\"Team Lead\",\"title2\":\"Sr. Developer\"}}}'"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.to_json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"email\":\"james@gmail.com\",\"employee_name\":\"James\",\"job_profile\":{\"title1\":\"Team Lead\",\"title2\":\"Sr. Developer\"}}]'"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_json(orient=\"records\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading HTML Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# url = 'https://www.fdic.gov/bank/individual/failed/banklist.html'\n",
    "\n",
    "# dfs = pd.read_html(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading Excel Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_excel = pd.read_excel(\"Excel_Sample.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_excel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pickling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_excel.to_pickle('df_excel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('df_excel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
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