16-GroupBy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is Groupby in Pandas?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to show the groupby method in Pandas.\n",
    "</br>\n",
    "Happy Learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>0.128979</td>\n",
       "      <td>0.903436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.334460</td>\n",
       "      <td>-1.431566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.506455</td>\n",
       "      <td>-0.854207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "      <td>2.135132</td>\n",
       "      <td>-0.996191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.979153</td>\n",
       "      <td>1.918519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.165257</td>\n",
       "      <td>0.204901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1   key2     data1     data2\n",
       "0    a    one  0.128979  0.903436\n",
       "1    a    two -0.334460 -1.431566\n",
       "2    b  three -0.506455 -0.854207\n",
       "3    b    one  2.135132 -0.996191\n",
       "4    a    two -0.979153  1.918519\n",
       "5    b  three -0.165257  0.204901"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(\n",
    "    {\"key1\":list(\"aabbab\"),\n",
    "     \"key2\":[\"one\",\"two\",\"three\"]*2,\n",
    "     \"data1\":np.random.randn(6),\n",
    "     \"data2\":np.random.randn(6)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "group=df[\"data1\"].groupby(df[\"key1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.SeriesGroupBy object at 0x000001EE83397370>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a   -0.394878\n",
       "b    0.487807\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1  key2 \n",
       "a     one      0.128979\n",
       "      two     -0.656807\n",
       "b     one      2.135132\n",
       "      three   -0.335856\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave=df[\"data1\"].groupby([df[\"key1\"],\n",
    "                         df[\"key2\"]]).mean()\n",
    "ave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>key2</th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.128979</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.656807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.135132</td>\n",
       "      <td>-0.335856</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "key2       one     three       two\n",
       "key1                              \n",
       "a     0.128979       NaN -0.656807\n",
       "b     2.135132 -0.335856       NaN"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-0.394878</td>\n",
       "      <td>0.463463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.487807</td>\n",
       "      <td>-0.548499</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a    -0.394878  0.463463\n",
       "b     0.487807 -0.548499"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"key1\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>one</th>\n",
       "      <td>0.128979</td>\n",
       "      <td>0.903436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.656807</td>\n",
       "      <td>0.243476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>2.135132</td>\n",
       "      <td>-0.996191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.335856</td>\n",
       "      <td>-0.324653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               data1     data2\n",
       "key1 key2                     \n",
       "a    one    0.128979  0.903436\n",
       "     two   -0.656807  0.243476\n",
       "b    one    2.135132 -0.996191\n",
       "     three -0.335856 -0.324653"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"key1\",\"key2\"]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating over Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "  key1 key2     data1     data2\n",
      "0    a  one  0.128979  0.903436\n",
      "1    a  two -0.334460 -1.431566\n",
      "4    a  two -0.979153  1.918519\n",
      "b\n",
      "  key1   key2     data1     data2\n",
      "2    b  three -0.506455 -0.854207\n",
      "3    b    one  2.135132 -0.996191\n",
      "5    b  three -0.165257  0.204901\n"
     ]
    }
   ],
   "source": [
    "for name, group in df.groupby(\"key1\"):\n",
    "    print(name)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a one\n",
      "  key1 key2     data1     data2\n",
      "0    a  one  0.128979  0.903436\n",
      "a two\n",
      "  key1 key2     data1     data2\n",
      "1    a  two -0.334460 -1.431566\n",
      "4    a  two -0.979153  1.918519\n",
      "b one\n",
      "  key1 key2     data1     data2\n",
      "3    b  one  2.135132 -0.996191\n",
      "b three\n",
      "  key1   key2     data1     data2\n",
      "2    b  three -0.506455 -0.854207\n",
      "5    b  three -0.165257  0.204901\n"
     ]
    }
   ],
   "source": [
    "for (x1,x2),group in df.groupby([\"key1\",\n",
    "                                 \"key2\"]):\n",
    "    print(x1,x2)\n",
    "    print(group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "piece=dict(list(df.groupby(\"key1\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "      <td>0.128979</td>\n",
       "      <td>0.903436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.334460</td>\n",
       "      <td>-1.431566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.979153</td>\n",
       "      <td>1.918519</td>\n",
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      "text/plain": [
       "  key1 key2     data1     data2\n",
       "0    a  one  0.128979  0.903436\n",
       "1    a  two -0.334460 -1.431566\n",
       "4    a  two -0.979153  1.918519"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piece[\"a\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a Column or Subset of Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
       "      <th>one</th>\n",
       "      <td>0.128979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.656807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>one</th>\n",
       "      <td>2.135132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.335856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               data1\n",
       "key1 key2           \n",
       "a    one    0.128979\n",
       "     two   -0.656807\n",
       "b    one    2.135132\n",
       "     three -0.335856"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['key1', \n",
    "            'key2'])[['data1']].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping with Dicts and Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>apple</th>\n",
       "      <td>0.803066</td>\n",
       "      <td>0.165556</td>\n",
       "      <td>0.040465</td>\n",
       "      <td>-0.376024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cherry</th>\n",
       "      <td>-0.265198</td>\n",
       "      <td>0.778739</td>\n",
       "      <td>0.574622</td>\n",
       "      <td>-1.292316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>-0.977442</td>\n",
       "      <td>-0.458472</td>\n",
       "      <td>-1.271370</td>\n",
       "      <td>0.614398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>0.580412</td>\n",
       "      <td>0.061148</td>\n",
       "      <td>1.257117</td>\n",
       "      <td>-1.351419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               a         b         c         d\n",
       "apple   0.803066  0.165556  0.040465 -0.376024\n",
       "cherry -0.265198  0.778739  0.574622 -1.292316\n",
       "banana -0.977442 -0.458472 -1.271370  0.614398\n",
       "kiwi    0.580412  0.061148  1.257117 -1.351419"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit=pd.DataFrame(np.random.randn(4,4),\n",
    "                   columns=list(\"abcd\"),\n",
    "                   index=[\"apple\",\"cherry\",\n",
    "                          \"banana\",\"kiwi\"])\n",
    "fruit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "label={\"a\": \"green\",\"b\":\"yellow\",\n",
    "       \"c\":\"green\",\"d\":\"yellow\",\n",
    "       \"e\":\"purple\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "group=fruit.groupby(label,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",
       "        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>green</th>\n",
       "      <th>yellow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>0.843531</td>\n",
       "      <td>-0.210469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cherry</th>\n",
       "      <td>0.309424</td>\n",
       "      <td>-0.513577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>-2.248812</td>\n",
       "      <td>0.155926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>1.837529</td>\n",
       "      <td>-1.290271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           green    yellow\n",
       "apple   0.843531 -0.210469\n",
       "cherry  0.309424 -0.513577\n",
       "banana -2.248812  0.155926\n",
       "kiwi    1.837529 -1.290271"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     green\n",
       "b    yellow\n",
       "c     green\n",
       "d    yellow\n",
       "e    purple\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(label)\n",
    "s"
   ]
  },
  {
   "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>green</th>\n",
       "      <th>yellow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cherry</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        green  yellow\n",
       "apple       2       2\n",
       "cherry      2       2\n",
       "banana      2       2\n",
       "kiwi        2       2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit.groupby(s,axis=1).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping with Functions"
   ]
  },
  {
   "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",
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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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.580412</td>\n",
       "      <td>0.061148</td>\n",
       "      <td>1.257117</td>\n",
       "      <td>-1.351419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.803066</td>\n",
       "      <td>0.165556</td>\n",
       "      <td>0.040465</td>\n",
       "      <td>-0.376024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.242640</td>\n",
       "      <td>0.320267</td>\n",
       "      <td>-0.696747</td>\n",
       "      <td>-0.677917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "4  0.580412  0.061148  1.257117 -1.351419\n",
       "5  0.803066  0.165556  0.040465 -0.376024\n",
       "6 -1.242640  0.320267 -0.696747 -0.677917"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit.groupby(len).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping by Index Levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame(np.random.randn(4,5),\n",
    "                  columns=[list(\"AAABB\"),\n",
    "                           [1,2,3,1,2]])"
   ]
  },
  {
   "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",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>letter</th>\n",
       "      <th colspan=\"3\" halign=\"left\">A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>number</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.741181</td>\n",
       "      <td>-0.399735</td>\n",
       "      <td>0.562333</td>\n",
       "      <td>-1.035530</td>\n",
       "      <td>0.678250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.394531</td>\n",
       "      <td>0.745952</td>\n",
       "      <td>-0.661248</td>\n",
       "      <td>0.811781</td>\n",
       "      <td>-0.804934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.028793</td>\n",
       "      <td>-0.914979</td>\n",
       "      <td>0.857640</td>\n",
       "      <td>-0.780221</td>\n",
       "      <td>1.898880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.029662</td>\n",
       "      <td>0.092263</td>\n",
       "      <td>1.424289</td>\n",
       "      <td>-0.143006</td>\n",
       "      <td>1.484412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "letter         A                             B          \n",
       "number         1         2         3         1         2\n",
       "0       0.741181 -0.399735  0.562333 -1.035530  0.678250\n",
       "1       0.394531  0.745952 -0.661248  0.811781 -0.804934\n",
       "2       0.028793 -0.914979  0.857640 -0.780221  1.898880\n",
       "3      -0.029662  0.092263  1.424289 -0.143006  1.484412"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns.names=[\"letter\",\"number\"]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>letter</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.903779</td>\n",
       "      <td>-0.357280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.479235</td>\n",
       "      <td>0.006847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.028546</td>\n",
       "      <td>1.118659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.486890</td>\n",
       "      <td>1.341406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "letter         A         B\n",
       "0       0.903779 -0.357280\n",
       "1       0.479235  0.006847\n",
       "2      -0.028546  1.118659\n",
       "3       1.486890  1.341406"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby(level=\"letter\",axis=1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application with Real Data Set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "game=pd.read_csv(\"Data/vgsalesGlobale.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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",
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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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Platform</th>\n",
       "      <th>Year</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Wii Sports</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>41.49</td>\n",
       "      <td>29.02</td>\n",
       "      <td>3.77</td>\n",
       "      <td>8.46</td>\n",
       "      <td>82.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Super Mario Bros.</td>\n",
       "      <td>NES</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Platform</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>29.08</td>\n",
       "      <td>3.58</td>\n",
       "      <td>6.81</td>\n",
       "      <td>0.77</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Mario Kart Wii</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>Racing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.85</td>\n",
       "      <td>12.88</td>\n",
       "      <td>3.79</td>\n",
       "      <td>3.31</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Wii Sports Resort</td>\n",
       "      <td>Wii</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>15.75</td>\n",
       "      <td>11.01</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.96</td>\n",
       "      <td>33.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Pokemon Red/Pokemon Blue</td>\n",
       "      <td>GB</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>Role-Playing</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>11.27</td>\n",
       "      <td>8.89</td>\n",
       "      <td>10.22</td>\n",
       "      <td>1.00</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                      Name Platform    Year         Genre Publisher  \\\n",
       "0     1                Wii Sports      Wii  2006.0        Sports  Nintendo   \n",
       "1     2         Super Mario Bros.      NES  1985.0      Platform  Nintendo   \n",
       "2     3            Mario Kart Wii      Wii  2008.0        Racing  Nintendo   \n",
       "3     4         Wii Sports Resort      Wii  2009.0        Sports  Nintendo   \n",
       "4     5  Pokemon Red/Pokemon Blue       GB  1996.0  Role-Playing  Nintendo   \n",
       "\n",
       "   NA_Sales  EU_Sales  JP_Sales  Other_Sales  Global_Sales  \n",
       "0     41.49     29.02      3.77         8.46         82.74  \n",
       "1     29.08      3.58      6.81         0.77         40.24  \n",
       "2     15.85     12.88      3.79         3.31         35.82  \n",
       "3     15.75     11.01      3.28         2.96         33.00  \n",
       "4     11.27      8.89     10.22         1.00         31.37  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank              int64\n",
       "Name             object\n",
       "Platform         object\n",
       "Year            float64\n",
       "Genre            object\n",
       "Publisher        object\n",
       "NA_Sales        float64\n",
       "EU_Sales        float64\n",
       "JP_Sales        float64\n",
       "Other_Sales     float64\n",
       "Global_Sales    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Rank</th>\n",
       "      <th>Year</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "      <td>16291.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8290.190228</td>\n",
       "      <td>2006.405561</td>\n",
       "      <td>0.265647</td>\n",
       "      <td>0.147731</td>\n",
       "      <td>0.078833</td>\n",
       "      <td>0.048426</td>\n",
       "      <td>0.540910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4792.654450</td>\n",
       "      <td>5.832412</td>\n",
       "      <td>0.822432</td>\n",
       "      <td>0.509303</td>\n",
       "      <td>0.311879</td>\n",
       "      <td>0.190083</td>\n",
       "      <td>1.567345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4132.500000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8292.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12439.500000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>0.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16600.000000</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>41.490000</td>\n",
       "      <td>29.020000</td>\n",
       "      <td>10.220000</td>\n",
       "      <td>10.570000</td>\n",
       "      <td>82.740000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Rank          Year      NA_Sales      EU_Sales      JP_Sales  \\\n",
       "count  16291.000000  16291.000000  16291.000000  16291.000000  16291.000000   \n",
       "mean    8290.190228   2006.405561      0.265647      0.147731      0.078833   \n",
       "std     4792.654450      5.832412      0.822432      0.509303      0.311879   \n",
       "min        1.000000   1980.000000      0.000000      0.000000      0.000000   \n",
       "25%     4132.500000   2003.000000      0.000000      0.000000      0.000000   \n",
       "50%     8292.000000   2007.000000      0.080000      0.020000      0.000000   \n",
       "75%    12439.500000   2010.000000      0.240000      0.110000      0.040000   \n",
       "max    16600.000000   2020.000000     41.490000     29.020000     10.220000   \n",
       "\n",
       "        Other_Sales  Global_Sales  \n",
       "count  16291.000000  16291.000000  \n",
       "mean       0.048426      0.540910  \n",
       "std        0.190083      1.567345  \n",
       "min        0.000000      0.010000  \n",
       "25%        0.000000      0.060000  \n",
       "50%        0.010000      0.170000  \n",
       "75%        0.040000      0.480000  \n",
       "max       10.570000     82.740000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game.dropna().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5374406555006628"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game.Global_Sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "group=game.groupby(\"Genre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Genre\n",
       "Action          3316\n",
       "Adventure       1286\n",
       "Fighting         848\n",
       "Misc            1739\n",
       "Platform         886\n",
       "Puzzle           582\n",
       "Racing          1249\n",
       "Role-Playing    1488\n",
       "Shooter         1310\n",
       "Simulation       867\n",
       "Sports          2346\n",
       "Strategy         681\n",
       "Name: Global_Sales, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group[\"Global_Sales\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Genre</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>Action</th>\n",
       "      <td>3316.0</td>\n",
       "      <td>0.528100</td>\n",
       "      <td>1.156427</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>21.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>1286.0</td>\n",
       "      <td>0.185879</td>\n",
       "      <td>0.513280</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.1600</td>\n",
       "      <td>11.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fighting</th>\n",
       "      <td>848.0</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>0.955965</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.210</td>\n",
       "      <td>0.5500</td>\n",
       "      <td>13.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc</th>\n",
       "      <td>1739.0</td>\n",
       "      <td>0.465762</td>\n",
       "      <td>1.314886</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.4100</td>\n",
       "      <td>29.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <td>886.0</td>\n",
       "      <td>0.938341</td>\n",
       "      <td>2.585254</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.7900</td>\n",
       "      <td>40.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puzzle</th>\n",
       "      <td>582.0</td>\n",
       "      <td>0.420876</td>\n",
       "      <td>1.561716</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.3075</td>\n",
       "      <td>30.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Racing</th>\n",
       "      <td>1249.0</td>\n",
       "      <td>0.586101</td>\n",
       "      <td>1.662437</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.5300</td>\n",
       "      <td>35.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role-Playing</th>\n",
       "      <td>1488.0</td>\n",
       "      <td>0.623233</td>\n",
       "      <td>1.707909</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.5225</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shooter</th>\n",
       "      <td>1310.0</td>\n",
       "      <td>0.791885</td>\n",
       "      <td>1.817263</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.7275</td>\n",
       "      <td>28.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simulation</th>\n",
       "      <td>867.0</td>\n",
       "      <td>0.452364</td>\n",
       "      <td>1.195255</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.160</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>24.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sports</th>\n",
       "      <td>2346.0</td>\n",
       "      <td>0.567319</td>\n",
       "      <td>2.089716</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.5600</td>\n",
       "      <td>82.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <td>681.0</td>\n",
       "      <td>0.257151</td>\n",
       "      <td>0.520908</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>5.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count      mean       std   min   25%    50%     75%    max\n",
       "Genre                                                                     \n",
       "Action        3316.0  0.528100  1.156427  0.01  0.07  0.190  0.5000  21.40\n",
       "Adventure     1286.0  0.185879  0.513280  0.01  0.02  0.060  0.1600  11.18\n",
       "Fighting       848.0  0.529375  0.955965  0.01  0.08  0.210  0.5500  13.04\n",
       "Misc          1739.0  0.465762  1.314886  0.01  0.06  0.160  0.4100  29.02\n",
       "Platform       886.0  0.938341  2.585254  0.01  0.09  0.280  0.7900  40.24\n",
       "Puzzle         582.0  0.420876  1.561716  0.01  0.04  0.110  0.3075  30.26\n",
       "Racing        1249.0  0.586101  1.662437  0.01  0.07  0.190  0.5300  35.82\n",
       "Role-Playing  1488.0  0.623233  1.707909  0.01  0.07  0.185  0.5225  31.37\n",
       "Shooter       1310.0  0.791885  1.817263  0.01  0.08  0.230  0.7275  28.31\n",
       "Simulation     867.0  0.452364  1.195255  0.01  0.05  0.160  0.4200  24.76\n",
       "Sports        2346.0  0.567319  2.089716  0.01  0.09  0.220  0.5600  82.74\n",
       "Strategy       681.0  0.257151  0.520908  0.01  0.04  0.090  0.2700   5.45"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group[\"Global_Sales\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5281001206272617"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "game[game.Genre==\"Action\"].Global_Sales.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year</th>\n",
       "      <th>NA_Sales</th>\n",
       "      <th>EU_Sales</th>\n",
       "      <th>JP_Sales</th>\n",
       "      <th>Other_Sales</th>\n",
       "      <th>Global_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genre</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>Action</th>\n",
       "      <td>7973.879071</td>\n",
       "      <td>2007.909929</td>\n",
       "      <td>0.264726</td>\n",
       "      <td>0.158323</td>\n",
       "      <td>0.048236</td>\n",
       "      <td>0.056508</td>\n",
       "      <td>0.528100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adventure</th>\n",
       "      <td>11532.787714</td>\n",
       "      <td>2008.130878</td>\n",
       "      <td>0.082271</td>\n",
       "      <td>0.049868</td>\n",
       "      <td>0.040490</td>\n",
       "      <td>0.013072</td>\n",
       "      <td>0.185879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fighting</th>\n",
       "      <td>7646.511792</td>\n",
       "      <td>2004.630383</td>\n",
       "      <td>0.263667</td>\n",
       "      <td>0.119481</td>\n",
       "      <td>0.103007</td>\n",
       "      <td>0.043255</td>\n",
       "      <td>0.529375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misc</th>\n",
       "      <td>8561.847039</td>\n",
       "      <td>2007.258480</td>\n",
       "      <td>0.235906</td>\n",
       "      <td>0.124198</td>\n",
       "      <td>0.061967</td>\n",
       "      <td>0.043312</td>\n",
       "      <td>0.465762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Platform</th>\n",
       "      <td>6927.251693</td>\n",
       "      <td>2003.820776</td>\n",
       "      <td>0.504571</td>\n",
       "      <td>0.227573</td>\n",
       "      <td>0.147596</td>\n",
       "      <td>0.058228</td>\n",
       "      <td>0.938341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Puzzle</th>\n",
       "      <td>9627.381443</td>\n",
       "      <td>2005.243433</td>\n",
       "      <td>0.212680</td>\n",
       "      <td>0.087251</td>\n",
       "      <td>0.098471</td>\n",
       "      <td>0.021564</td>\n",
       "      <td>0.420876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Racing</th>\n",
       "      <td>7961.515612</td>\n",
       "      <td>2004.840131</td>\n",
       "      <td>0.287766</td>\n",
       "      <td>0.190865</td>\n",
       "      <td>0.045388</td>\n",
       "      <td>0.061865</td>\n",
       "      <td>0.586101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Role-Playing</th>\n",
       "      <td>8086.174731</td>\n",
       "      <td>2007.055744</td>\n",
       "      <td>0.219946</td>\n",
       "      <td>0.126384</td>\n",
       "      <td>0.236767</td>\n",
       "      <td>0.040060</td>\n",
       "      <td>0.623233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shooter</th>\n",
       "      <td>7369.367939</td>\n",
       "      <td>2005.918877</td>\n",
       "      <td>0.444733</td>\n",
       "      <td>0.239137</td>\n",
       "      <td>0.029221</td>\n",
       "      <td>0.078389</td>\n",
       "      <td>0.791885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Simulation</th>\n",
       "      <td>8626.085352</td>\n",
       "      <td>2006.567568</td>\n",
       "      <td>0.211430</td>\n",
       "      <td>0.130773</td>\n",
       "      <td>0.073472</td>\n",
       "      <td>0.036355</td>\n",
       "      <td>0.452364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sports</th>\n",
       "      <td>7425.026428</td>\n",
       "      <td>2005.477865</td>\n",
       "      <td>0.291283</td>\n",
       "      <td>0.160635</td>\n",
       "      <td>0.057702</td>\n",
       "      <td>0.057532</td>\n",
       "      <td>0.567319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strategy</th>\n",
       "      <td>10071.897210</td>\n",
       "      <td>2005.599106</td>\n",
       "      <td>0.100881</td>\n",
       "      <td>0.066579</td>\n",
       "      <td>0.072628</td>\n",
       "      <td>0.016681</td>\n",
       "      <td>0.257151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Rank         Year  NA_Sales  EU_Sales  JP_Sales  \\\n",
       "Genre                                                                   \n",
       "Action         7973.879071  2007.909929  0.264726  0.158323  0.048236   \n",
       "Adventure     11532.787714  2008.130878  0.082271  0.049868  0.040490   \n",
       "Fighting       7646.511792  2004.630383  0.263667  0.119481  0.103007   \n",
       "Misc           8561.847039  2007.258480  0.235906  0.124198  0.061967   \n",
       "Platform       6927.251693  2003.820776  0.504571  0.227573  0.147596   \n",
       "Puzzle         9627.381443  2005.243433  0.212680  0.087251  0.098471   \n",
       "Racing         7961.515612  2004.840131  0.287766  0.190865  0.045388   \n",
       "Role-Playing   8086.174731  2007.055744  0.219946  0.126384  0.236767   \n",
       "Shooter        7369.367939  2005.918877  0.444733  0.239137  0.029221   \n",
       "Simulation     8626.085352  2006.567568  0.211430  0.130773  0.073472   \n",
       "Sports         7425.026428  2005.477865  0.291283  0.160635  0.057702   \n",
       "Strategy      10071.897210  2005.599106  0.100881  0.066579  0.072628   \n",
       "\n",
       "              Other_Sales  Global_Sales  \n",
       "Genre                                    \n",
       "Action           0.056508      0.528100  \n",
       "Adventure        0.013072      0.185879  \n",
       "Fighting         0.043255      0.529375  \n",
       "Misc             0.043312      0.465762  \n",
       "Platform         0.058228      0.938341  \n",
       "Puzzle           0.021564      0.420876  \n",
       "Racing           0.061865      0.586101  \n",
       "Role-Playing     0.040060      0.623233  \n",
       "Shooter          0.078389      0.791885  \n",
       "Simulation       0.036355      0.452364  \n",
       "Sports           0.057532      0.567319  \n",
       "Strategy         0.016681      0.257151  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group[\"Global_Sales\"].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Genre'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group[[\"NA_Sales\",\n",
    "       \"EU_Sales\",\n",
    "       \"JP_Sales\"]].mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}