03_02_Data_Wrangling_avec_Pandas.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "cDcDgB854vLR"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kcrb6GQU5qUD"
   },
   "source": [
    "# Concatener des dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ZtBRL5tN5LfO"
   },
   "outputs": [],
   "source": [
    "df1 =  pd.DataFrame({ 'StudentID': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29],\n",
    "                     'Score' : [89, 39, 50, 97, 22, 66, 31, 51, 71, 91, 56, 32, 52, 73, 92]})\n",
    "df2 =  pd.DataFrame({'StudentID': [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30],\n",
    "                     'Score': [98, 93, 44, 77, 69, 56, 31, 53, 78, 93, 56, 77, 33, 56, 27]})"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudentID</th>\n",
       "      <th>Score</th>\n",
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       "   StudentID  Score\n",
       "0          1     89\n",
       "1          3     39\n",
       "2          5     50\n",
       "3          7     97\n",
       "4          9     22"
      ]
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     "metadata": {
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   "source": [
    "df1.head()"
   ]
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       "      <th></th>\n",
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       "   StudentID  Score\n",
       "0          2     98\n",
       "1          4     93\n",
       "2          6     44\n",
       "3          8     77\n",
       "4         10     69"
      ]
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     "execution_count": 6,
     "metadata": {
      "tags": []
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   ],
   "source": [
    "df2.head()"
   ]
  },
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      "text/plain": [
       "    StudentID  Score\n",
       "0           1     89\n",
       "1           3     39\n",
       "2           5     50\n",
       "3           7     97\n",
       "4           9     22\n",
       "5          11     66\n",
       "6          13     31\n",
       "7          15     51\n",
       "8          17     71\n",
       "9          19     91\n",
       "10         21     56\n",
       "11         23     32\n",
       "12         25     52\n",
       "13         27     73\n",
       "14         29     92\n",
       "15          2     98\n",
       "16          4     93\n",
       "17          6     44\n",
       "18          8     77\n",
       "19         10     69\n",
       "20         12     56\n",
       "21         14     31\n",
       "22         16     53\n",
       "23         18     78\n",
       "24         20     93\n",
       "25         22     56\n",
       "26         24     77\n",
       "27         26     33\n",
       "28         28     56\n",
       "29         30     27"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
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      "text/plain": [
       "    StudentID  Score\n",
       "0           1     89\n",
       "1           3     39\n",
       "2           5     50\n",
       "3           7     97\n",
       "4           9     22\n",
       "5          11     66\n",
       "6          13     31\n",
       "7          15     51\n",
       "8          17     71\n",
       "9          19     91\n",
       "10         21     56\n",
       "11         23     32\n",
       "12         25     52\n",
       "13         27     73\n",
       "14         29     92\n",
       "15          2     98\n",
       "16          4     93\n",
       "17          6     44\n",
       "18          8     77\n",
       "19         10     69\n",
       "20         12     56\n",
       "21         14     31\n",
       "22         16     53\n",
       "23         18     78\n",
       "24         20     93\n",
       "25         22     56\n",
       "26         24     77\n",
       "27         26     33\n",
       "28         28     56\n",
       "29         30     27"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MetiBo2KB4yZ"
   },
   "source": [
    "# Merger des dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =  pd.DataFrame({ 'StudentID': [1, 2, 3],\n",
    "                       'ScoreMath' : [22, 66, 31]})\n",
    "\n",
    "df2 =  pd.DataFrame({ 'StudentID': [21, 8, 3],\n",
    "                       'ScoreML' : [11, 85, 46]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "    }\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>StudentID</th>\n",
       "      <th>ScoreMath</th>\n",
       "      <th>ScoreML</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StudentID  ScoreMath  ScoreML\n",
       "0          3         31       46"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>StudentID</th>\n",
       "      <th>ScoreMath</th>\n",
       "      <th>ScoreML</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StudentID  ScoreMath  ScoreML\n",
       "0          1       22.0      NaN\n",
       "1          2       66.0      NaN\n",
       "2          3       31.0     46.0\n",
       "3         21        NaN     11.0\n",
       "4          8        NaN     85.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudentID</th>\n",
       "      <th>ScoreMath</th>\n",
       "      <th>ScoreML</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31.0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StudentID  ScoreMath  ScoreML\n",
       "0         21        NaN       11\n",
       "1          8        NaN       85\n",
       "2          3       31.0       46"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StudentID</th>\n",
       "      <th>ScoreMath</th>\n",
       "      <th>ScoreML</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StudentID  ScoreMath  ScoreML\n",
       "0          1         22      NaN\n",
       "1          2         66      NaN\n",
       "2          3         31     46.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "39esjBQ2EAxM"
   },
   "source": [
    "# Merging sur l'index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "id": "zbfcV-v5DyhJ",
    "outputId": "cb2a2f72-c66b-447c-c8da-227a7768c255"
   },
   "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",
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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>key</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ball</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apple</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apple</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ball</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  value\n",
       "0  apple      0\n",
       "1   ball      1\n",
       "2  apple      2\n",
       "3  apple      3\n",
       "4   ball      4\n",
       "5    cat      5"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left1 = pd.DataFrame({'key': ['apple','ball','apple', 'apple',\n",
    "'ball', 'cat'], 'value': range(6)})\n",
    "left1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 110
    },
    "id": "ZIFD16TGEHdo",
    "outputId": "6e769840-986d-4bf3-a031-2af92ec954c0"
   },
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       group_val\n",
       "apple       33.4\n",
       "ball         5.0"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right1 = pd.DataFrame({'group_val': [33.4, 5]}, index=['apple',\n",
    "'ball'])\n",
    "right1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 203
    },
    "id": "gGnqXPI3EQpK",
    "outputId": "b534a96c-3086-4d67-f7d4-bbd26ae181c5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "      <th>group_val</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "      <td>0</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apple</td>\n",
       "      <td>2</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apple</td>\n",
       "      <td>3</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ball</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ball</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  value  group_val\n",
       "0  apple      0       33.4\n",
       "2  apple      2       33.4\n",
       "3  apple      3       33.4\n",
       "1   ball      1        5.0\n",
       "4   ball      4        5.0"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 234
    },
    "id": "LV3MytSJEmaB",
    "outputId": "b59a5854-c39c-4d49-8d43-0658def19d89"
   },
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>value</th>\n",
       "      <th>group_val</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>apple</td>\n",
       "      <td>2</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>apple</td>\n",
       "      <td>3</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ball</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ball</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cat</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     key  value  group_val\n",
       "0  apple      0       33.4\n",
       "2  apple      2       33.4\n",
       "3  apple      3       33.4\n",
       "1   ball      1        5.0\n",
       "4   ball      4        5.0\n",
       "5    cat      5        NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left1, right1, left_on='key', right_index=True, how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jp4VXkIDE4sq"
   },
   "source": [
    "# Reshaping and pivoting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "id": "eqJSM-XOE0-2",
    "outputId": "224f9b34-d711-4f30-ce0f-2f24131ec44a"
   },
   "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>Bergen</th>\n",
       "      <th>Oslo</th>\n",
       "      <th>Trondheim</th>\n",
       "      <th>Stavanger</th>\n",
       "      <th>Kristiansand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rainfall</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Bergen  Oslo  Trondheim  Stavanger  Kristiansand\n",
       "Rainfall       0     1          2          3             4\n",
       "Humidity       5     6          7          8             9\n",
       "Wind          10    11         12         13            14"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.arange(15).reshape((3,5))\n",
    "indexers = ['Rainfall', 'Humidity', 'Wind']\n",
    "dframe1 = pd.DataFrame(data, index=indexers, columns=['Bergen', 'Oslo', 'Trondheim', 'Stavanger', 'Kristiansand'])\n",
    "dframe1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Z_wr4V_3FWtl",
    "outputId": "690d20ed-cbb3-4bcb-f58e-4b02b7157ba7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rainfall  Bergen           0\n",
       "          Oslo             1\n",
       "          Trondheim        2\n",
       "          Stavanger        3\n",
       "          Kristiansand     4\n",
       "Humidity  Bergen           5\n",
       "          Oslo             6\n",
       "          Trondheim        7\n",
       "          Stavanger        8\n",
       "          Kristiansand     9\n",
       "Wind      Bergen          10\n",
       "          Oslo            11\n",
       "          Trondheim       12\n",
       "          Stavanger       13\n",
       "          Kristiansand    14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack = dframe1.stack()\n",
    "stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 141
    },
    "id": "eLtybx4lF0jb",
    "outputId": "a8524d87-a7fb-4db0-94b1-ae5c4c01bc39"
   },
   "outputs": [
    {
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       "      <th>Kristiansand</th>\n",
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       "      <th>Humidity</th>\n",
       "      <td>5</td>\n",
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       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>Wind</th>\n",
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      "text/plain": [
       "          Bergen  Oslo  Trondheim  Stavanger  Kristiansand\n",
       "Rainfall       0     1          2          3             4\n",
       "Humidity       5     6          7          8             9\n",
       "Wind          10    11         12         13            14"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   ],
   "source": [
    "stack.unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yA8FUAtLGD3G"
   },
   "source": [
    "# Chercher et Supprimer les doublons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "OKuIug-bFp0o",
    "outputId": "63311f43-5432-461c-b2c1-3250593b2489"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "    column 1  column 2\n",
       "0    Looping        10\n",
       "1    Looping        10\n",
       "2    Looping        22\n",
       "3  Functions        23\n",
       "4  Functions        23\n",
       "5  Functions        24\n",
       "6  Functions        24"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3 = pd.DataFrame({'column 1': ['Looping'] * 3 + ['Functions']\n",
    "* 4, 'column 2': [10, 10, 22, 23, 23, 24, 24]})\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "38C5axoXGNCl",
    "outputId": "0c3e7218-d3ad-46dc-a045-d8e3d1fa8d37"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4     True\n",
       "5    False\n",
       "6     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "KVaqxo-2GX7V",
    "outputId": "0b13714b-a218-4d27-be2f-a01517aab34f"
   },
   "outputs": [
    {
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       "    column 1  column 2\n",
       "0    Looping        10\n",
       "2    Looping        22\n",
       "3  Functions        23\n",
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      ]
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   "source": [
    "frame3.drop_duplicates()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "8oO_goQmGe7t",
    "outputId": "e95a2d14-478c-47ed-9a2b-5911864ab981"
   },
   "outputs": [
    {
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       "    column 1  column 2\n",
       "0    Looping        10\n",
       "1    Looping        22\n",
       "2  Functions        23\n",
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    "frame3.drop_duplicates().reset_index(drop=True)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
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    "id": "EoA-CJ6rGn1M",
    "outputId": "af496c51-c9c5-471d-f99d-a9dd8ed0f032"
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   "outputs": [
    {
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Functions</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "    column 1  column 2  col3\n",
       "0    Looping        10     0\n",
       "1    Looping        10     1\n",
       "2    Looping        22     2\n",
       "3  Functions        23     3\n",
       "4  Functions        23     4\n",
       "5  Functions        24     5\n",
       "6  Functions        24     6"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3['col3'] = range(7)\n",
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 172
    },
    "id": "00rniK3YG6rV",
    "outputId": "bdf37d4c-266c-448c-cdf4-f63d3a707943"
   },
   "outputs": [
    {
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       "    column 1  column 2  col3\n",
       "0    Looping        10     0\n",
       "1    Looping        22     2\n",
       "2  Functions        23     3\n",
       "3  Functions        24     5"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3.drop_duplicates(['column 2']).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lSpKUJfBHHSl"
   },
   "source": [
    "# Replacing des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "RsKvyLImG_Yb",
    "outputId": "460920e3-5d4e-4e30-968f-2b993e35db81"
   },
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>Functions</td>\n",
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       "    column 1  column 2  col3\n",
       "0    Looping        10     0\n",
       "1    Looping        10     1\n",
       "2    Looping        22     2\n",
       "3  Functions        23     3\n",
       "4  Functions        23     4\n",
       "5  Functions        24     5\n",
       "6  Functions        24     6"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
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    "frame3.iat[3, 2] = 12000"
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   "cell_type": "code",
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    "frame3.at[5, \"column 2\"] = 12"
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    "# Les valeurs manquantes"
   ]
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    "id": "CdeaoRLDICUL",
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    {
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       "        store1  store2  store3\n",
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       "banana      18      19      20\n",
       "kiwi        21      22      23\n",
       "grapes      24      25      26\n",
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    "data = np.arange(15, 30).reshape(5, 3)\n",
    "dfx = pd.DataFrame(data, index=['apple', 'banana', 'kiwi',\n",
    "'grapes', 'mango'], columns=['store1', 'store2', 'store3'])\n",
    "dfx"
   ]
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    "id": "hwIqH6TqISNj",
    "outputId": "319b01e6-7ce2-460f-8c4a-9e4dd48e1f16"
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   "outputs": [
    {
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       "        store1  store2  store3  store4\n",
       "apple       15      16      17     NaN\n",
       "banana      18      19      20     NaN\n",
       "kiwi        21      22      23     NaN\n",
       "grapes      24      25      26     NaN\n",
       "mango       27      28      29     NaN"
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    "dfx['store4'] = np.nan\n",
    "dfx"
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  {
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   "execution_count": null,
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    },
    "id": "R7DO9PpWIeaM",
    "outputId": "87ddcb5f-d850-4d8c-c624-7c54e15275aa"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "store1    15.0\n",
       "store2    16.0\n",
       "store3    17.0\n",
       "store4     NaN\n",
       "Name: apple, dtype: float64"
      ]
     },
     "execution_count": 50,
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   ]
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    "id": "lkeeCpIrIist",
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       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>21</td>\n",
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       "      <th>grapes</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>17</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        store1  store2  store3  store4\n",
       "apple       15      16      17     NaN\n",
       "banana      18      19      20     NaN\n",
       "kiwi        21      22      23     NaN\n",
       "grapes      24      25      26     NaN\n",
       "mango       27      28      29     NaN\n",
       "orange      15      16      17    18.0"
      ]
     },
     "execution_count": 51,
     "metadata": {
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    "dfx.loc['orange'] = np.arange(15, 19)\n",
    "dfx"
   ]
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       "            store1  store2  store3  store4\n",
       "apple         15.0    16.0    17.0     NaN\n",
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       "kiwi          21.0    22.0    23.0     NaN\n",
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       "mango         27.0    28.0    29.0     NaN\n",
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       "            store1  store2  store3  store4  store5\n",
       "apple         15.0    16.0    17.0     NaN     NaN\n",
       "banana        18.0    19.0    20.0     NaN     NaN\n",
       "kiwi          21.0    22.0    23.0     NaN     NaN\n",
       "grapes        24.0    25.0    26.0     NaN     NaN\n",
       "mango         27.0    28.0    29.0     NaN     NaN\n",
       "orange        15.0    16.0    17.0    18.0     NaN\n",
       "strawberry     NaN     NaN     NaN     NaN     NaN"
      ]
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     "execution_count": 56,
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   "source": [
    "dfx['store5'] = np.nan\n",
    "dfx"
   ]
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       "            store1  store2  store3  store4  store5\n",
       "apple         15.0    16.0    17.0     NaN     NaN\n",
       "banana        18.0    -1.0    20.0     NaN     NaN\n",
       "kiwi          21.0    22.0    23.0     NaN     NaN\n",
       "grapes        24.0    25.0    26.0     NaN     NaN\n",
       "mango         27.0    28.0    29.0     NaN     NaN\n",
       "orange        15.0    16.0    17.0    18.0     NaN\n",
       "strawberry     NaN     NaN     NaN     NaN     NaN"
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    "dfx.loc[\"banana\", \"store2\"] = -1\n",
    "dfx"
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    "outputId": "fd64b921-eb95-4280-ad60-5b8d553f00d3"
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   "outputs": [
    {
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      "text/plain": [
       "store1    1\n",
       "store2    1\n",
       "store3    1\n",
       "store4    6\n",
       "store5    7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
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       "store1    0.142857\n",
       "store2    0.142857\n",
       "store3    0.142857\n",
       "store4    0.857143\n",
       "store5    1.000000\n",
       "dtype: float64"
      ]
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       "            store1  store2  store3  store4  store5\n",
       "apple         True    True    True   False   False\n",
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       "grapes        True    True    True   False   False\n",
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       "orange        True    True    True    True   False\n",
       "strawberry   False   False   False   False   False"
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   "source": [
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       "Empty DataFrame\n",
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       "        store1  store2  store3  store4  store5\n",
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       "            store1  store2  store3  store4  store5\n",
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       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strawberry</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store1  store2  store3  store4  store5\n",
       "apple         15.0    16.0    17.0     0.0     0.0\n",
       "banana        18.0    -1.0    20.0     0.0     0.0\n",
       "kiwi          21.0    22.0    23.0     0.0     0.0\n",
       "grapes        24.0    25.0    26.0     0.0     0.0\n",
       "mango         27.0    28.0    29.0     0.0     0.0\n",
       "orange        15.0    16.0    17.0    18.0     0.0\n",
       "strawberry     0.0     0.0     0.0     0.0     0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "9Q8ZZ6vZK4XV",
    "outputId": "374c07d2-9783-456a-90c0-02b1442b8c8a"
   },
   "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>store1</th>\n",
       "      <th>store2</th>\n",
       "      <th>store3</th>\n",
       "      <th>store4</th>\n",
       "      <th>store5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strawberry</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store1  store2  store3  store4  store5\n",
       "apple         15.0    16.0    17.0     NaN     NaN\n",
       "banana        18.0    -1.0    20.0     NaN     NaN\n",
       "kiwi          21.0    22.0    23.0     NaN     NaN\n",
       "grapes        24.0    25.0    26.0     NaN     NaN\n",
       "mango         27.0    28.0    29.0     NaN     NaN\n",
       "orange        15.0    16.0    17.0    18.0     NaN\n",
       "strawberry     NaN     NaN     NaN     NaN     NaN"
      ]
     },
     "execution_count": 73,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6vtZjgC9K_By",
    "outputId": "b3f5d9c4-f0e8-435d-fa74-3d790705bd3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "store1    20.000000\n",
       "store2    17.666667\n",
       "store3    22.000000\n",
       "store4    18.000000\n",
       "store5          NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "TFujxAT2LA_y",
    "outputId": "af5aa277-b7b7-4a2d-8d2d-1fb1c2958cb2"
   },
   "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>store1</th>\n",
       "      <th>store2</th>\n",
       "      <th>store3</th>\n",
       "      <th>store4</th>\n",
       "      <th>store5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>21.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>24.0</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>27.0</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strawberry</th>\n",
       "      <td>20.0</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>22.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store1     store2  store3  store4  store5\n",
       "apple         15.0  16.000000    17.0    18.0     NaN\n",
       "banana        18.0  -1.000000    20.0    18.0     NaN\n",
       "kiwi          21.0  22.000000    23.0    18.0     NaN\n",
       "grapes        24.0  25.000000    26.0    18.0     NaN\n",
       "mango         27.0  28.000000    29.0    18.0     NaN\n",
       "orange        15.0  16.000000    17.0    18.0     NaN\n",
       "strawberry    20.0  17.666667    22.0    18.0     NaN"
      ]
     },
     "execution_count": 75,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.fillna(dfx.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 264
    },
    "id": "MDHyt5AILFdb",
    "outputId": "19cff2a7-7856-4998-94d0-a24b93f5d98b"
   },
   "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>store1</th>\n",
       "      <th>store2</th>\n",
       "      <th>store3</th>\n",
       "      <th>store4</th>\n",
       "      <th>store5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>banana</th>\n",
       "      <td>18.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kiwi</th>\n",
       "      <td>21.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grapes</th>\n",
       "      <td>24.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mango</th>\n",
       "      <td>27.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>orange</th>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>strawberry</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            store1  store2  store3  store4  store5\n",
       "apple         15.0    16.0    17.0     NaN     NaN\n",
       "banana        18.0    -1.0    20.0     NaN     NaN\n",
       "kiwi          21.0    22.0    23.0     NaN     NaN\n",
       "grapes        24.0    25.0    26.0     NaN     NaN\n",
       "mango         27.0    28.0    29.0     NaN     NaN\n",
       "orange        15.0    16.0    17.0    18.0     NaN\n",
       "strawberry     NaN     NaN     NaN     NaN     NaN"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# backward forward filling\n",
    "dfx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wm0HaurMLOIL",
    "outputId": "35e92d2d-5610-4ee0-fd7c-603f12f2c057"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple          NaN\n",
       "banana         NaN\n",
       "kiwi           NaN\n",
       "grapes         NaN\n",
       "mango          NaN\n",
       "orange        18.0\n",
       "strawberry    18.0\n",
       "Name: store4, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.store4.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IaujGX2-LY--",
    "outputId": "51db5483-80a4-4c92-e13a-07c41db1241e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple         18.0\n",
       "banana        18.0\n",
       "kiwi          18.0\n",
       "grapes        18.0\n",
       "mango         18.0\n",
       "orange        18.0\n",
       "strawberry     NaN\n",
       "Name: store4, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfx.store4.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "63VH28PCLhrS",
    "outputId": "c9703943-baa2-4597-a2d4-9448cdd9c079"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100.000000\n",
       "1    133.333333\n",
       "2    166.666667\n",
       "3    200.000000\n",
       "4    175.000000\n",
       "5    150.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interpoler les valeurs manquantes\n",
    "n_ventes = pd.Series([100, np.nan, np.nan, 200, np.nan, 150]\n",
    ")\n",
    "n_ventes.interpolate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H2cU_qTOMAcs"
   },
   "source": [
    "# Transformer les variables numériques en catégories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "wu7_ExsqMaSf"
   },
   "outputs": [],
   "source": [
    "notes = np.random.randint(20, size=(15,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UijFUKwSMzdP",
    "outputId": "056d687c-0547-4fda-991a-a5bdbdb4790d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 12, 15,  8, 11,  6,  2, 13,  0,  2, 12, 11, 15, 13,  1])"
      ]
     },
     "execution_count": 94,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "p20RgiCkMkhn"
   },
   "outputs": [],
   "source": [
    "bins = [-1,10, 12, 15, 18, 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iIUjUwkXNX_4"
   },
   "outputs": [],
   "source": [
    "bins_names = ['echoué', 'passable', 'assez-bien', 'bien', 'très bien']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "76waSX53Mw4b"
   },
   "outputs": [],
   "source": [
    "category = pd.cut(notes, bins, right=False, labels=bins_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YmHc1fwUM4r8",
    "outputId": "9c586ad1-3317-427a-bde1-4dfa39b87f84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['assez-bien', 'assez-bien', 'bien', 'echoué', 'passable', ..., 'assez-bien', 'passable', 'bien', 'assez-bien', 'echoué']\n",
       "Length: 15\n",
       "Categories (5, object): ['echoué' < 'passable' < 'assez-bien' < 'bien' < 'très bien']"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "y-OFw6xSM5vb"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dLizi4IIOCky"
   },
   "outputs": [],
   "source": [
    "Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LGJmRnTmOBiM"
   },
   "source": [
    "# Aggregations avec groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oECYa6srOHaq"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UWo4i-p3OREO",
    "outputId": "7d1ea69f-a426-44ef-decc-ff274498cfbf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yqSwgWI6OZJy"
   },
   "outputs": [],
   "source": [
    "data = '/gdrive/My Drive/DataKev/data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "48wuqeasOREP"
   },
   "outputs": [],
   "source": [
    "auto = pd.read_csv(data + 'Automobile_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 308
    },
    "id": "lIUXUQGwOkY2",
    "outputId": "32b7977d-be1b-4c0e-97d6-d9be5051b9c5"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
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       "      <th>drive-wheels</th>\n",
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       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
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       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
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       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
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       "      <td>gas</td>\n",
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       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
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      "text/plain": [
       "   symboling normalized-losses         make  ... city-mpg highway-mpg  price\n",
       "0          3                 ?  alfa-romero  ...       21          27  13495\n",
       "1          3                 ?  alfa-romero  ...       21          27  16500\n",
       "2          1                 ?  alfa-romero  ...       19          26  16500\n",
       "3          2               164         audi  ...       24          30  13950\n",
       "4          2               164         audi  ...       18          22  17450\n",
       "\n",
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  {
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    "id": "vUUFyCYROl67",
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       "      <td>2414.0</td>\n",
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       "      <td>41.642693</td>\n",
       "      <td>61.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>326.0</td>\n",
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       "    <tr>\n",
       "      <th>compression-ratio</th>\n",
       "      <td>205.0</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.6</td>\n",
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       "      <td>13.0</td>\n",
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       "                   count         mean         std  ...     50%     75%     max\n",
       "symboling          205.0     0.834146    1.245307  ...     1.0     2.0     3.0\n",
       "wheel-base         205.0    98.756585    6.021776  ...    97.0   102.4   120.9\n",
       "length             205.0   174.049268   12.337289  ...   173.2   183.1   208.1\n",
       "width              205.0    65.907805    2.145204  ...    65.5    66.9    72.3\n",
       "height             205.0    53.724878    2.443522  ...    54.1    55.5    59.8\n",
       "curb-weight        205.0  2555.565854  520.680204  ...  2414.0  2935.0  4066.0\n",
       "engine-size        205.0   126.907317   41.642693  ...   120.0   141.0   326.0\n",
       "compression-ratio  205.0    10.142537    3.972040  ...     9.0     9.4    23.0\n",
       "city-mpg           205.0    25.219512    6.542142  ...    24.0    30.0    49.0\n",
       "highway-mpg        205.0    30.751220    6.886443  ...    30.0    34.0    54.0\n",
       "\n",
       "[10 rows x 8 columns]"
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  {
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   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
    },
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    {
     "data": {
      "text/plain": [
       "sedan          96\n",
       "hatchback      70\n",
       "wagon          25\n",
       "hardtop         8\n",
       "convertible     6\n",
       "Name: body-style, dtype: int64"
      ]
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     "execution_count": 109,
     "metadata": {
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    }
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "outputId": "5c6e9a02-9d49-428b-b482-62650a09ccbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fcdf4b6bf28>"
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     "execution_count": 110,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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   "source": [
    "auto.groupby('body-style')"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2iimI-YJPSIB",
    "outputId": "cf9541dd-9738-4eed-89ba-e84890ba4e81"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'convertible': [0, 1, 72, 128, 172, 189], 'hardtop': [69, 74, 98, 126, 127, 167, 168, 170], 'hatchback': [2, 9, 18, 19, 21, 22, 23, 24, 29, 30, 31, 32, 33, 34, 37, 38, 46, 50, 51, 52, 55, 56, 57, 58, 59, 61, 64, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 95, 99, 104, 105, 106, 118, 119, 120, 124, 125, 129, 131, 132, 134, 136, 138, 139, 140, 150, 151, 152, 157, 159, 161, 164, 166, 169, 171, 175, 177, 178, 179, 190], 'sedan': [3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 25, 26, 27, 35, 39, 40, 41, 42, 43, 44, 45, 47, 48, 49, 53, 54, 60, 62, 63, 65, 66, 67, 70, 71, 73, 85, 86, 87, 88, 89, 90, 91, 92, 94, 96, 100, 101, 103, 107, 108, 111, 112, 115, 116, 117, 121, 122, 133, 135, 137, 141, 142, 143, 144, 145, 156, 158, 160, 162, 163, 165, 173, 174, 176, 180, 182, 183, 184, 185, 186, 187, 188, 191, 192, 194, 196, 198, 200, 201, 202, 203, 204], 'wagon': [7, 28, 36, 68, 93, 97, 102, 109, 110, 113, 114, 123, 130, 146, 147, 148, 149, 153, 154, 155, 181, 193, 195, 197, 199]}"
      ]
     },
     "execution_count": 113,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
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   "source": [
    "auto.groupby('body-style').groups"
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   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "De-xciyiPVRk",
    "outputId": "d7f7101d-246a-40f1-e670-5a95c8bbb9b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['convertible', 'hardtop', 'hatchback', 'sedan', 'wagon'])"
      ]
     },
     "execution_count": 114,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
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     "height": 384
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    "id": "QFHDqNd9PjYE",
    "outputId": "8ac9902b-56ce-4c3e-9e92-822d44d7c1eb"
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       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>106.7</td>\n",
       "      <td>187.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>54.9</td>\n",
       "      <td>3495</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>112.0</td>\n",
       "      <td>199.2</td>\n",
       "      <td>72.0</td>\n",
       "      <td>55.4</td>\n",
       "      <td>3715</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>304</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.35</td>\n",
       "      <td>8.0</td>\n",
       "      <td>184</td>\n",
       "      <td>4500</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>45400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>nissan</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>95.1</td>\n",
       "      <td>162.4</td>\n",
       "      <td>63.8</td>\n",
       "      <td>53.3</td>\n",
       "      <td>2008</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.29</td>\n",
       "      <td>9.4</td>\n",
       "      <td>69</td>\n",
       "      <td>5200</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>8249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>porsche</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>rear</td>\n",
       "      <td>89.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>2756</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>six</td>\n",
       "      <td>194</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.74</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>207</td>\n",
       "      <td>5900</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>32528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>porsche</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>rear</td>\n",
       "      <td>89.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>2756</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>six</td>\n",
       "      <td>194</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.74</td>\n",
       "      <td>2.9</td>\n",
       "      <td>9.5</td>\n",
       "      <td>207</td>\n",
       "      <td>5900</td>\n",
       "      <td>17</td>\n",
       "      <td>25</td>\n",
       "      <td>34028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2540</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>146</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>116</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>8449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2536</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>146</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>116</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>9639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>2</td>\n",
       "      <td>134</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>98.4</td>\n",
       "      <td>176.2</td>\n",
       "      <td>65.6</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2679</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>146</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>116</td>\n",
       "      <td>4800</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>11199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     symboling normalized-losses           make  ... city-mpg highway-mpg  price\n",
       "69           0                93  mercedes-benz  ...       22          25  28176\n",
       "74           1                 ?  mercedes-benz  ...       14          16  45400\n",
       "98           2               168         nissan  ...       31          37   8249\n",
       "126          3                 ?        porsche  ...       17          25  32528\n",
       "127          3                 ?        porsche  ...       17          25  34028\n",
       "167          2               134         toyota  ...       24          30   8449\n",
       "168          2               134         toyota  ...       24          30   9639\n",
       "170          2               134         toyota  ...       24          30  11199\n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style = auto.groupby('body-style')\n",
    "style.get_group('hardtop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EGc-8gcKP-NI",
    "outputId": "9b80f4d4-75d5-4ffd-e513-7a0762e4c691"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    120\n",
       "rwd     76\n",
       "4wd      9\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auto['drive-wheels'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dx7hd6evQJgd"
   },
   "outputs": [],
   "source": [
    "double_groupin = auto.groupby([\"body-style\", 'drive-wheels'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 586
    },
    "id": "kL0wD-bMQaNN",
    "outputId": "f16cda91-76ab-4296-df65-fa070f22e8f9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</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",
       "      <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 rowspan=\"2\" valign=\"top\">convertible</th>\n",
       "      <th>fwd</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>159.3</td>\n",
       "      <td>64.2</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2254</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>90</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>29</td>\n",
       "      <td>11595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hardtop</th>\n",
       "      <th>fwd</th>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>nissan</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>95.1</td>\n",
       "      <td>162.4</td>\n",
       "      <td>63.8</td>\n",
       "      <td>53.3</td>\n",
       "      <td>2008</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>97</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.29</td>\n",
       "      <td>9.4</td>\n",
       "      <td>69</td>\n",
       "      <td>5200</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>8249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>106.7</td>\n",
       "      <td>187.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>54.9</td>\n",
       "      <td>3495</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">hatchback</th>\n",
       "      <th>4wd</th>\n",
       "      <td>0</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>99.5</td>\n",
       "      <td>178.2</td>\n",
       "      <td>67.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3053</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>131</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>160</td>\n",
       "      <td>5500</td>\n",
       "      <td>16</td>\n",
       "      <td>22</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>88.4</td>\n",
       "      <td>141.1</td>\n",
       "      <td>60.3</td>\n",
       "      <td>53.2</td>\n",
       "      <td>1488</td>\n",
       "      <td>l</td>\n",
       "      <td>three</td>\n",
       "      <td>61</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.03</td>\n",
       "      <td>9.5</td>\n",
       "      <td>48</td>\n",
       "      <td>5100</td>\n",
       "      <td>47</td>\n",
       "      <td>53</td>\n",
       "      <td>5151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">sedan</th>\n",
       "      <th>4wd</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2824</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
       "      <td>bmw</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>front</td>\n",
       "      <td>101.2</td>\n",
       "      <td>176.8</td>\n",
       "      <td>64.8</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2395</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>8.8</td>\n",
       "      <td>101</td>\n",
       "      <td>5800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>16430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">wagon</th>\n",
       "      <th>4wd</th>\n",
       "      <td>0</td>\n",
       "      <td>85</td>\n",
       "      <td>subaru</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>front</td>\n",
       "      <td>96.9</td>\n",
       "      <td>173.6</td>\n",
       "      <td>65.4</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2420</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>four</td>\n",
       "      <td>108</td>\n",
       "      <td>2bbl</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.64</td>\n",
       "      <td>9.0</td>\n",
       "      <td>82</td>\n",
       "      <td>4800</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2954</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>-1</td>\n",
       "      <td>93</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>front</td>\n",
       "      <td>110.0</td>\n",
       "      <td>190.9</td>\n",
       "      <td>70.3</td>\n",
       "      <td>58.7</td>\n",
       "      <td>3750</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          symboling normalized-losses  ... highway-mpg  price\n",
       "body-style  drive-wheels                               ...                   \n",
       "convertible fwd                   3                 ?  ...          29  11595\n",
       "            rwd                   3                 ?  ...          27  13495\n",
       "hardtop     fwd                   2               168  ...          37   8249\n",
       "            rwd                   0                93  ...          25  28176\n",
       "hatchback   4wd                   0                 ?  ...          22      ?\n",
       "            fwd                   2               121  ...          53   5151\n",
       "            rwd                   1                 ?  ...          26  16500\n",
       "sedan       4wd                   2               164  ...          22  17450\n",
       "            fwd                   2               164  ...          30  13950\n",
       "            rwd                   2               192  ...          29  16430\n",
       "wagon       4wd                   0                85  ...          29   8013\n",
       "            fwd                   1                 ?  ...          25  18920\n",
       "            rwd                  -1                93  ...          25  28248\n",
       "\n",
       "[13 rows x 24 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "double_groupin.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 356
    },
    "id": "_wgcYnhGQdXc",
    "outputId": "96c242f4-c86e-4521-c935-4cadd82a5069"
   },
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</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",
       "      <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>convertible</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "      <td>2548</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardtop</th>\n",
       "      <td>0</td>\n",
       "      <td>93</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>two</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>106.7</td>\n",
       "      <td>187.5</td>\n",
       "      <td>70.3</td>\n",
       "      <td>54.9</td>\n",
       "      <td>3495</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>183</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.64</td>\n",
       "      <td>21.5</td>\n",
       "      <td>123</td>\n",
       "      <td>4350</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>28176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "      <td>2823</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "      <td>2337</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>105.8</td>\n",
       "      <td>192.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>55.7</td>\n",
       "      <td>2954</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>18920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             symboling normalized-losses  ... highway-mpg  price\n",
       "body-style                                ...                   \n",
       "convertible          3                 ?  ...          27  13495\n",
       "hardtop              0                93  ...          25  28176\n",
       "hatchback            1                 ?  ...          26  16500\n",
       "sedan                2               164  ...          30  13950\n",
       "wagon                1                 ?  ...          25  18920\n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style.first()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cnMBLecfQ8cv",
    "outputId": "966fc4cb-7947-452e-a4c4-35aa374abade"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   symboling          205 non-null    int64  \n",
      " 1   normalized-losses  205 non-null    object \n",
      " 2   make               205 non-null    object \n",
      " 3   fuel-type          205 non-null    object \n",
      " 4   aspiration         205 non-null    object \n",
      " 5   num-of-doors       205 non-null    object \n",
      " 6   body-style         205 non-null    object \n",
      " 7   drive-wheels       205 non-null    object \n",
      " 8   engine-location    205 non-null    object \n",
      " 9   wheel-base         205 non-null    float64\n",
      " 10  length             205 non-null    float64\n",
      " 11  width              205 non-null    float64\n",
      " 12  height             205 non-null    float64\n",
      " 13  curb-weight        205 non-null    int64  \n",
      " 14  engine-type        205 non-null    object \n",
      " 15  num-of-cylinders   205 non-null    object \n",
      " 16  engine-size        205 non-null    int64  \n",
      " 17  fuel-system        205 non-null    object \n",
      " 18  bore               205 non-null    object \n",
      " 19  stroke             205 non-null    object \n",
      " 20  compression-ratio  205 non-null    float64\n",
      " 21  horsepower         205 non-null    object \n",
      " 22  peak-rpm           205 non-null    object \n",
      " 23  city-mpg           205 non-null    int64  \n",
      " 24  highway-mpg        205 non-null    int64  \n",
      " 25  price              205 non-null    object \n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.8+ KB\n"
     ]
    }
   ],
   "source": [
    "auto.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "z2Q9OgOWQvCq",
    "outputId": "14bffe7f-1686-4c2a-9315-cbebf91be4df"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "body-style\n",
       "convertible    20.500000\n",
       "hardtop        21.625000\n",
       "hatchback      26.314286\n",
       "sedan          25.322917\n",
       "wagon          24.040000\n",
       "Name: city-mpg, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style['city-mpg'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "H30UPVrfQ31p",
    "outputId": "3afdc529-e651-4ebd-a10c-1e41e5599654"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcdf421ccc0>"
      ]
     },
     "execution_count": 126,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "style['city-mpg'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 342
    },
    "id": "x3qLWvywRUVd",
    "outputId": "e85fb9ab-ca0a-42e5-fe2b-fe20b381b1ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcdf41604e0>"
      ]
     },
     "execution_count": 127,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "style['city-mpg'].min().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yBV2WrdKR2pj",
    "outputId": "270a15de-9127-4bbf-dac5-5cb31d8bb212"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            2.833333e+00\n",
       "wheel-base           9.270000e+01\n",
       "length               1.703833e+02\n",
       "width                6.558333e+01\n",
       "height               5.143333e+01\n",
       "curb-weight          2.801667e+03\n",
       "engine-size          1.571667e+02\n",
       "compression-ratio    8.933333e+00\n",
       "horsepower           1.851853e+15\n",
       "peak-rpm             8.334167e+22\n",
       "city-mpg             2.050000e+01\n",
       "highway-mpg          2.600000e+01\n",
       "price                2.249194e+28\n",
       "dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style.get_group('convertible').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "gxZ_PSjzR9gS",
    "outputId": "ed843b7b-1888-40eb-83c2-c09efd0c82e2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">symboling</th>\n",
       "      <th colspan=\"2\" halign=\"left\">wheel-base</th>\n",
       "      <th colspan=\"2\" halign=\"left\">length</th>\n",
       "      <th colspan=\"2\" halign=\"left\">width</th>\n",
       "      <th colspan=\"2\" halign=\"left\">height</th>\n",
       "      <th colspan=\"2\" halign=\"left\">curb-weight</th>\n",
       "      <th colspan=\"2\" halign=\"left\">engine-size</th>\n",
       "      <th colspan=\"2\" halign=\"left\">compression-ratio</th>\n",
       "      <th colspan=\"2\" halign=\"left\">city-mpg</th>\n",
       "      <th colspan=\"2\" halign=\"left\">highway-mpg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>convertible</th>\n",
       "      <td>2.833333</td>\n",
       "      <td>3</td>\n",
       "      <td>92.700000</td>\n",
       "      <td>98.4</td>\n",
       "      <td>170.383333</td>\n",
       "      <td>180.3</td>\n",
       "      <td>65.583333</td>\n",
       "      <td>70.5</td>\n",
       "      <td>51.433333</td>\n",
       "      <td>55.6</td>\n",
       "      <td>2801.666667</td>\n",
       "      <td>3685</td>\n",
       "      <td>157.166667</td>\n",
       "      <td>234</td>\n",
       "      <td>8.933333</td>\n",
       "      <td>9.5</td>\n",
       "      <td>20.500000</td>\n",
       "      <td>24</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardtop</th>\n",
       "      <td>1.875000</td>\n",
       "      <td>3</td>\n",
       "      <td>98.500000</td>\n",
       "      <td>112.0</td>\n",
       "      <td>176.937500</td>\n",
       "      <td>199.2</td>\n",
       "      <td>66.612500</td>\n",
       "      <td>72.0</td>\n",
       "      <td>52.850000</td>\n",
       "      <td>55.4</td>\n",
       "      <td>2810.625000</td>\n",
       "      <td>3715</td>\n",
       "      <td>176.250000</td>\n",
       "      <td>304</td>\n",
       "      <td>10.725000</td>\n",
       "      <td>21.5</td>\n",
       "      <td>21.625000</td>\n",
       "      <td>31</td>\n",
       "      <td>27.250000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hatchback</th>\n",
       "      <td>1.585714</td>\n",
       "      <td>3</td>\n",
       "      <td>95.535714</td>\n",
       "      <td>102.9</td>\n",
       "      <td>166.592857</td>\n",
       "      <td>186.6</td>\n",
       "      <td>65.247143</td>\n",
       "      <td>72.3</td>\n",
       "      <td>52.108571</td>\n",
       "      <td>56.1</td>\n",
       "      <td>2348.185714</td>\n",
       "      <td>3366</td>\n",
       "      <td>114.400000</td>\n",
       "      <td>203</td>\n",
       "      <td>9.027429</td>\n",
       "      <td>22.5</td>\n",
       "      <td>26.314286</td>\n",
       "      <td>49</td>\n",
       "      <td>32.171429</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedan</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>100.619792</td>\n",
       "      <td>120.9</td>\n",
       "      <td>177.585417</td>\n",
       "      <td>208.1</td>\n",
       "      <td>66.260417</td>\n",
       "      <td>71.7</td>\n",
       "      <td>54.337500</td>\n",
       "      <td>56.7</td>\n",
       "      <td>2610.593750</td>\n",
       "      <td>4066</td>\n",
       "      <td>130.822917</td>\n",
       "      <td>326</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>23.0</td>\n",
       "      <td>25.322917</td>\n",
       "      <td>45</td>\n",
       "      <td>30.833333</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wagon</th>\n",
       "      <td>-0.160000</td>\n",
       "      <td>1</td>\n",
       "      <td>102.156000</td>\n",
       "      <td>114.2</td>\n",
       "      <td>181.304000</td>\n",
       "      <td>198.9</td>\n",
       "      <td>66.256000</td>\n",
       "      <td>71.4</td>\n",
       "      <td>56.728000</td>\n",
       "      <td>59.8</td>\n",
       "      <td>2784.240000</td>\n",
       "      <td>3750</td>\n",
       "      <td>123.840000</td>\n",
       "      <td>183</td>\n",
       "      <td>10.316000</td>\n",
       "      <td>21.5</td>\n",
       "      <td>24.040000</td>\n",
       "      <td>31</td>\n",
       "      <td>28.720000</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            symboling      wheel-base         ...   city-mpg     highway-mpg    \n",
       "                 mean max        mean    max  ...       mean max        mean max\n",
       "body-style                                    ...                               \n",
       "convertible  2.833333   3   92.700000   98.4  ...  20.500000  24   26.000000  30\n",
       "hardtop      1.875000   3   98.500000  112.0  ...  21.625000  31   27.250000  37\n",
       "hatchback    1.585714   3   95.535714  102.9  ...  26.314286  49   32.171429  54\n",
       "sedan        0.333333   2  100.619792  120.9  ...  25.322917  45   30.833333  50\n",
       "wagon       -0.160000   1  102.156000  114.2  ...  24.040000  31   28.720000  37\n",
       "\n",
       "[5 rows x 20 columns]"
      ]
     },
     "execution_count": 130,
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    "id": "Kt_4uO7HSKcb"
   },
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   "source": []
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