GroupBy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GroupBy (Split Apply Combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Day</th>\n",
       "      <th>City</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>NewYork</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>NewYork</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>NewYork</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>NewYork</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4//2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>88</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Day     City  Temperature  Windspeed   Event\n",
       "0    1/1/2017  NewYork           32          6    Rain\n",
       "1    1/2/2017  NewYork           36          7   Sunny\n",
       "2    1/3/2017  NewYork           28         12    Snow\n",
       "3    1/4/2017  NewYork           33          7   Sunny\n",
       "4    1/1/2017   Mumbai           90          5   Sunny\n",
       "5    1/2/2017   Mumbai           85         12     Fog\n",
       "6    1/3/2017   Mumbai           87         15     Fog\n",
       "7   1/4//2017   Mumbai           88          5    Rain\n",
       "8    1/1/2017    Paris           45         20   Sunny\n",
       "9    1/2/2017    Paris           50          8  Cloudy\n",
       "10   1/3/2017    Paris           44         10  Cloudy\n",
       "11   1/4/2017    Paris           42         11  Cloudy"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import *\n",
    "\n",
    "df = read_csv(r\"GroupBy_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000017FA3026D88>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp= df.groupby(\"City\")   # group the dataframe by city \n",
    "gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mumbai\n",
      "         Day    City  Temperature  Windspeed  Event\n",
      "4   1/1/2017  Mumbai           90          5  Sunny\n",
      "5   1/2/2017  Mumbai           85         12    Fog\n",
      "6   1/3/2017  Mumbai           87         15    Fog\n",
      "7  1/4//2017  Mumbai           88          5   Rain\n",
      "NewYork\n",
      "        Day     City  Temperature  Windspeed  Event\n",
      "0  1/1/2017  NewYork           32          6   Rain\n",
      "1  1/2/2017  NewYork           36          7  Sunny\n",
      "2  1/3/2017  NewYork           28         12   Snow\n",
      "3  1/4/2017  NewYork           33          7  Sunny\n",
      "Paris\n",
      "         Day   City  Temperature  Windspeed   Event\n",
      "8   1/1/2017  Paris           45         20   Sunny\n",
      "9   1/2/2017  Paris           50          8  Cloudy\n",
      "10  1/3/2017  Paris           44         10  Cloudy\n",
      "11  1/4/2017  Paris           42         11  Cloudy\n"
     ]
    }
   ],
   "source": [
    "# To access the data splitted by using groupby method\n",
    "\n",
    "for city, city_df in gp:\n",
    "    print(city)\n",
    "    print(city_df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>City</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>44</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>Paris</td>\n",
       "      <td>42</td>\n",
       "      <td>11</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day   City  Temperature  Windspeed   Event\n",
       "8   1/1/2017  Paris           45         20   Sunny\n",
       "9   1/2/2017  Paris           50          8  Cloudy\n",
       "10  1/3/2017  Paris           44         10  Cloudy\n",
       "11  1/4/2017  Paris           42         11  Cloudy"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.get_group(\"Paris\")   # Access the splitted data of city Paris only\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>Event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>1/4//2017</td>\n",
       "      <td>90</td>\n",
       "      <td>15</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>36</td>\n",
       "      <td>12</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>50</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
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      ],
      "text/plain": [
       "               Day  Temperature  Windspeed  Event\n",
       "City                                             \n",
       "Mumbai   1/4//2017           90         15  Sunny\n",
       "NewYork   1/4/2017           36         12  Sunny\n",
       "Paris     1/4/2017           50         20  Sunny"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.max()   # get the maximum data of each city \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Temperature</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mumbai</th>\n",
       "      <td>4.0</td>\n",
       "      <td>87.50</td>\n",
       "      <td>2.081666</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>87.5</td>\n",
       "      <td>88.50</td>\n",
       "      <td>90.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.25</td>\n",
       "      <td>5.057997</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8.5</td>\n",
       "      <td>12.75</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewYork</th>\n",
       "      <td>4.0</td>\n",
       "      <td>32.25</td>\n",
       "      <td>3.304038</td>\n",
       "      <td>28.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>33.75</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>2.708013</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.75</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.25</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paris</th>\n",
       "      <td>4.0</td>\n",
       "      <td>45.25</td>\n",
       "      <td>3.403430</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>46.25</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.25</td>\n",
       "      <td>5.315073</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.50</td>\n",
       "      <td>10.5</td>\n",
       "      <td>13.25</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Temperature                                                 Windspeed  \\\n",
       "              count   mean       std   min   25%   50%    75%   max     count   \n",
       "City                                                                            \n",
       "Mumbai          4.0  87.50  2.081666  85.0  86.5  87.5  88.50  90.0       4.0   \n",
       "NewYork         4.0  32.25  3.304038  28.0  31.0  32.5  33.75  36.0       4.0   \n",
       "Paris           4.0  45.25  3.403430  42.0  43.5  44.5  46.25  50.0       4.0   \n",
       "\n",
       "                                                        \n",
       "          mean       std  min   25%   50%    75%   max  \n",
       "City                                                    \n",
       "Mumbai    9.25  5.057997  5.0  5.00   8.5  12.75  15.0  \n",
       "NewYork   8.00  2.708013  6.0  6.75   7.0   8.25  12.0  \n",
       "Paris    12.25  5.315073  8.0  9.50  10.5  13.25  20.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.describe()   # Get all the statistics result of each city"
   ]
  }
 ],
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