Esercizio 1 w11

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12ccf886-ca57-497d-9c75-27bdc3fb0dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6232e039-e718-43ab-a439-5e320cd67d29",
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    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ccomune</th>\n",
       "      <th>cprovincia</th>\n",
       "      <th>cregione</th>\n",
       "      <th>cnome</th>\n",
       "      <th>canno_inserimento</th>\n",
       "      <th>cdata_e_ora_inserimento</th>\n",
       "      <th>cidentificatore_in_openstreetmap</th>\n",
       "      <th>clongitudine</th>\n",
       "      <th>clatitudine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td></td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-06-25T23:17:43Z</td>\n",
       "      <td>1339088150</td>\n",
       "      <td>13.733257</td>\n",
       "      <td>45.575830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>Lenny's Pub</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011-07-29T17:22:56Z</td>\n",
       "      <td>1375887295</td>\n",
       "      <td>12.418681</td>\n",
       "      <td>46.747584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>Murrayfield Pub</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-10-24T09:28:06Z</td>\n",
       "      <td>3323888102</td>\n",
       "      <td>9.029585</td>\n",
       "      <td>45.831340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>ALTRO</td>\n",
       "      <td>Snop?e</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-09-22T08:32:52Z</td>\n",
       "      <td>921157802</td>\n",
       "      <td>13.640939</td>\n",
       "      <td>45.954607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ala di Stura</td>\n",
       "      <td>TORINO</td>\n",
       "      <td>Piemonte</td>\n",
       "      <td></td>\n",
       "      <td>2012</td>\n",
       "      <td>2012-05-21T14:28:45Z</td>\n",
       "      <td>1760949034</td>\n",
       "      <td>7.307306</td>\n",
       "      <td>45.313150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2492</th>\n",
       "      <td>Barletta (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>Santa Croce</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-17T16:15:08Z</td>\n",
       "      <td>615632993</td>\n",
       "      <td>16.285782</td>\n",
       "      <td>41.320671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2493</th>\n",
       "      <td>Bisceglie (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>Auld Dublin</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014-03-03T20:56:16Z</td>\n",
       "      <td>2613737619</td>\n",
       "      <td>16.497204</td>\n",
       "      <td>41.241814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2494</th>\n",
       "      <td>Bisceglie (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>Ferus</td>\n",
       "      <td>2014</td>\n",
       "      <td>2014-01-07T19:22:18Z</td>\n",
       "      <td>2613737620</td>\n",
       "      <td>16.506175</td>\n",
       "      <td>41.240957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2495</th>\n",
       "      <td>Trani (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>Re Artù</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009-09-04T07:57:05Z</td>\n",
       "      <td>482836935</td>\n",
       "      <td>16.418124</td>\n",
       "      <td>41.281518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2496</th>\n",
       "      <td>Trani (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>Well's Fargo</td>\n",
       "      <td>2009</td>\n",
       "      <td>2009-08-10T12:44:03Z</td>\n",
       "      <td>387223648</td>\n",
       "      <td>16.436765</td>\n",
       "      <td>41.267264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2497 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ccomune             cprovincia  cregione            cnome  \\\n",
       "0              ALTRO                  ALTRO     ALTRO                    \n",
       "1              ALTRO                  ALTRO     ALTRO      Lenny's Pub   \n",
       "2              ALTRO                  ALTRO     ALTRO  Murrayfield Pub   \n",
       "3              ALTRO                  ALTRO     ALTRO           Snop?e   \n",
       "4       Ala di Stura                 TORINO  Piemonte                    \n",
       "...              ...                    ...       ...              ...   \n",
       "2492   Barletta (BT)  BARLETTA ANDRIA TRANI    Puglia      Santa Croce   \n",
       "2493  Bisceglie (BT)  BARLETTA ANDRIA TRANI    Puglia      Auld Dublin   \n",
       "2494  Bisceglie (BT)  BARLETTA ANDRIA TRANI    Puglia            Ferus   \n",
       "2495      Trani (BT)  BARLETTA ANDRIA TRANI    Puglia          Re Artù   \n",
       "2496      Trani (BT)  BARLETTA ANDRIA TRANI    Puglia     Well's Fargo   \n",
       "\n",
       "      canno_inserimento cdata_e_ora_inserimento  \\\n",
       "0                  2011    2011-06-25T23:17:43Z   \n",
       "1                  2011    2011-07-29T17:22:56Z   \n",
       "2                  2015    2015-10-24T09:28:06Z   \n",
       "3                  2010    2010-09-22T08:32:52Z   \n",
       "4                  2012    2012-05-21T14:28:45Z   \n",
       "...                 ...                     ...   \n",
       "2492               2010    2010-01-17T16:15:08Z   \n",
       "2493               2014    2014-03-03T20:56:16Z   \n",
       "2494               2014    2014-01-07T19:22:18Z   \n",
       "2495               2009    2009-09-04T07:57:05Z   \n",
       "2496               2009    2009-08-10T12:44:03Z   \n",
       "\n",
       "      cidentificatore_in_openstreetmap  clongitudine  clatitudine  \n",
       "0                           1339088150     13.733257    45.575830  \n",
       "1                           1375887295     12.418681    46.747584  \n",
       "2                           3323888102      9.029585    45.831340  \n",
       "3                            921157802     13.640939    45.954607  \n",
       "4                           1760949034      7.307306    45.313150  \n",
       "...                                ...           ...          ...  \n",
       "2492                         615632993     16.285782    41.320671  \n",
       "2493                        2613737619     16.497204    41.241814  \n",
       "2494                        2613737620     16.506175    41.240957  \n",
       "2495                         482836935     16.418124    41.281518  \n",
       "2496                         387223648     16.436765    41.267264  \n",
       "\n",
       "[2497 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"../Downloads/Mappa-dei-pub-circoli-locali-in-Italia (1).json\"\n",
    "df = pd.read_json(file_path, encoding='latin1')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a4538808-5afc-4fb9-9505-b07b3fa06b58",
   "metadata": {
    "collapsed": true,
    "jupyter": {
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   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>canno_inserimento</th>\n",
       "      <th>cidentificatore_in_openstreetmap</th>\n",
       "      <th>clongitudine</th>\n",
       "      <th>clatitudine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2497.000000</td>\n",
       "      <td>2.497000e+03</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>2497.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.816179</td>\n",
       "      <td>1.847805e+09</td>\n",
       "      <td>11.412889</td>\n",
       "      <td>44.106531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.915547</td>\n",
       "      <td>1.057789e+09</td>\n",
       "      <td>2.381334</td>\n",
       "      <td>2.108361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2007.000000</td>\n",
       "      <td>3.203094e+07</td>\n",
       "      <td>6.708958</td>\n",
       "      <td>36.680786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>9.195678e+08</td>\n",
       "      <td>9.214541</td>\n",
       "      <td>42.885316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.769765e+09</td>\n",
       "      <td>11.301761</td>\n",
       "      <td>45.049664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2.616963e+09</td>\n",
       "      <td>12.682012</td>\n",
       "      <td>45.617151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>4.012443e+09</td>\n",
       "      <td>18.444577</td>\n",
       "      <td>46.983781</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       canno_inserimento  cidentificatore_in_openstreetmap  clongitudine  \\\n",
       "count        2497.000000                      2.497000e+03   2497.000000   \n",
       "mean         2012.816179                      1.847805e+09     11.412889   \n",
       "std             1.915547                      1.057789e+09      2.381334   \n",
       "min          2007.000000                      3.203094e+07      6.708958   \n",
       "25%          2012.000000                      9.195678e+08      9.214541   \n",
       "50%          2013.000000                      1.769765e+09     11.301761   \n",
       "75%          2014.000000                      2.616963e+09     12.682012   \n",
       "max          2016.000000                      4.012443e+09     18.444577   \n",
       "\n",
       "       clatitudine  \n",
       "count  2497.000000  \n",
       "mean     44.106531  \n",
       "std       2.108361  \n",
       "min      36.680786  \n",
       "25%      42.885316  \n",
       "50%      45.049664  \n",
       "75%      45.617151  \n",
       "max      46.983781  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0c9d1cb9-7b38-4799-bc85-459c733e4b7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2497 entries, 0 to 2496\n",
      "Data columns (total 9 columns):\n",
      " #   Column                            Non-Null Count  Dtype  \n",
      "---  ------                            --------------  -----  \n",
      " 0   ccomune                           2497 non-null   object \n",
      " 1   cprovincia                        2497 non-null   object \n",
      " 2   cregione                          2497 non-null   object \n",
      " 3   cnome                             2497 non-null   object \n",
      " 4   canno_inserimento                 2497 non-null   int64  \n",
      " 5   cdata_e_ora_inserimento           2497 non-null   object \n",
      " 6   cidentificatore_in_openstreetmap  2497 non-null   int64  \n",
      " 7   clongitudine                      2497 non-null   float64\n",
      " 8   clatitudine                       2497 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(5)\n",
      "memory usage: 175.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "044ba2b8-f309-4284-867d-403b9b694543",
   "metadata": {},
   "outputs": [],
   "source": [
    "#metadati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "89800d62-acf8-4b45-a1ed-1e730547068c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ccomune                              object\n",
       "cprovincia                           object\n",
       "cregione                             object\n",
       "cnome                                object\n",
       "canno_inserimento                     int64\n",
       "cdata_e_ora_inserimento              object\n",
       "cidentificatore_in_openstreetmap      int64\n",
       "clongitudine                        float64\n",
       "clatitudine                         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51e4542e-b5b6-42c5-a58e-ca6574fa646e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'ccomune': 'comune'}, inplace=True)\n",
    "df.rename(columns={'ccprovincia': 'provincia'}, inplace=True)\n",
    "df.rename(columns={'cregione ': 'regione '}, inplace=True)\n",
    "df.rename(columns={'cnome ': 'nome '}, inplace=True)\n",
    "df.rename(columns={'canno_inserimento': 'anno_inserimento'}, inplace=True)\n",
    "df.rename(columns={'cdata_e_ora_inserimento': 'data_e_ora_inserimento'}, inplace=True)\n",
    "df.rename(columns={'cidentificatore_in_openstreetmap ': 'identificatore_in_openstreetmap '}, inplace=True)\n",
    "df.rename(columns={'clongitudine ': 'longitudine '}, inplace=True)\n",
    "df.rename(columns={'clatitudine': 'latitudine'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "788e00f9-e88e-433d-b684-37a59dbdf5e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [col.lstrip(\"c\") for col in df.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "524a1834-6e4b-435f-9a44-72f466944f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'comune':'comune'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f9e292a2-17bf-49f1-928d-06e0b69595e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "omune                               object\n",
       "provincia                           object\n",
       "regione                             object\n",
       "nome                                object\n",
       "anno_inserimento                     int64\n",
       "data_e_ora_inserimento              object\n",
       "identificatore_in_openstreetmap      int64\n",
       "longitudine                        float64\n",
       "latitudine                         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "550c3a95-4d2e-4d87-a416-6c3d891b2468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "omune                                             ALTRO\n",
       "provincia                                         ALTRO\n",
       "regione                                           ALTRO\n",
       "nome                                                   \n",
       "anno_inserimento                                   2011\n",
       "data_e_ora_inserimento             2011-06-25T23:17:43Z\n",
       "identificatore_in_openstreetmap              1339088150\n",
       "longitudine                                   13.733257\n",
       "latitudine                                     45.57583\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45fb6b39-c53e-4fd8-8be9-67ba41c0add5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "omune                                         Trani (BT)\n",
       "provincia                          BARLETTA ANDRIA TRANI\n",
       "regione                                           Puglia\n",
       "nome                                        Well's Fargo\n",
       "anno_inserimento                                    2009\n",
       "data_e_ora_inserimento              2009-08-10T12:44:03Z\n",
       "identificatore_in_openstreetmap                387223648\n",
       "longitudine                                    16.436765\n",
       "latitudine                                     41.267264\n",
       "Name: 2496, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1b2ff656-99dd-498b-8484-ddb78ea3078b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>omune</th>\n",
       "      <th>provincia</th>\n",
       "      <th>regione</th>\n",
       "      <th>nome</th>\n",
       "      <th>anno_inserimento</th>\n",
       "      <th>data_e_ora_inserimento</th>\n",
       "      <th>identificatore_in_openstreetmap</th>\n",
       "      <th>longitudine</th>\n",
       "      <th>latitudine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>Pieve Tesino</td>\n",
       "      <td>TRENTO</td>\n",
       "      <td>Trentino-Alto Adige</td>\n",
       "      <td></td>\n",
       "      <td>2015</td>\n",
       "      <td>2015-12-31T21:17:15Z</td>\n",
       "      <td>1656566875</td>\n",
       "      <td>11.581854</td>\n",
       "      <td>46.077892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2485</th>\n",
       "      <td>Barletta (BT)</td>\n",
       "      <td>BARLETTA ANDRIA TRANI</td>\n",
       "      <td>Puglia</td>\n",
       "      <td>La Gioconda</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010-01-17T16:15:07Z</td>\n",
       "      <td>615632971</td>\n",
       "      <td>16.282666</td>\n",
       "      <td>41.321021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>Laion</td>\n",
       "      <td>BOLZANO</td>\n",
       "      <td>Trentino-Alto Adige</td>\n",
       "      <td>Zur Sonne</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012-07-30T20:12:19Z</td>\n",
       "      <td>850636739</td>\n",
       "      <td>11.564930</td>\n",
       "      <td>46.608952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Frassinello Monferrato</td>\n",
       "      <td>ALESSANDRIA</td>\n",
       "      <td>Piemonte</td>\n",
       "      <td>Vecchia Rocka</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016-01-19T09:23:35Z</td>\n",
       "      <td>1593550726</td>\n",
       "      <td>8.386496</td>\n",
       "      <td>45.033049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>Montepulciano</td>\n",
       "      <td>SIENA</td>\n",
       "      <td>Toscana</td>\n",
       "      <td></td>\n",
       "      <td>2012</td>\n",
       "      <td>2012-09-26T09:18:22Z</td>\n",
       "      <td>1933429320</td>\n",
       "      <td>11.850851</td>\n",
       "      <td>43.135908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       omune              provincia              regione  \\\n",
       "866             Pieve Tesino                 TRENTO  Trentino-Alto Adige   \n",
       "2485           Barletta (BT)  BARLETTA ANDRIA TRANI               Puglia   \n",
       "799                    Laion                BOLZANO  Trentino-Alto Adige   \n",
       "156   Frassinello Monferrato            ALESSANDRIA             Piemonte   \n",
       "1728           Montepulciano                  SIENA              Toscana   \n",
       "\n",
       "               nome  anno_inserimento data_e_ora_inserimento  \\\n",
       "866                              2015   2015-12-31T21:17:15Z   \n",
       "2485    La Gioconda              2010   2010-01-17T16:15:07Z   \n",
       "799       Zur Sonne              2012   2012-07-30T20:12:19Z   \n",
       "156   Vecchia Rocka              2016   2016-01-19T09:23:35Z   \n",
       "1728                             2012   2012-09-26T09:18:22Z   \n",
       "\n",
       "      identificatore_in_openstreetmap  longitudine  latitudine  \n",
       "866                        1656566875    11.581854   46.077892  \n",
       "2485                        615632971    16.282666   41.321021  \n",
       "799                         850636739    11.564930   46.608952  \n",
       "156                        1593550726     8.386496   45.033049  \n",
       "1728                       1933429320    11.850851   43.135908  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c6747890-48f5-4403-b686-b3a86ab5a3d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2011, 2015, 2010, 2012, 2014, 2016, 2013, 2008, 2009, 2007],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anno_inserimento = df['anno_inserimento'].unique()\n",
    "anno_inserimento "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3c7d29a-4d23-473f-87e2-b0fbded531b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "attività =df[((df['longitudine']>=9) & (df['longitudine'] <= 10)) & ((df['latitudine']>=45) & (df['latitudine'] <= 46))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3bd31932-3dc4-41f1-a899-4efc8abd8ba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attività.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "51b14d54-3549-4724-ba74-9f669913111a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a9f5ac4a-6003-4e3d-882f-b6687dded2b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>charges</th>\n",
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       "      <th>0</th>\n",
       "      <td>19</td>\n",
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       "      <td>27.900</td>\n",
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       "      <td>southwest</td>\n",
       "      <td>16884.92400</td>\n",
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       "      <td>18</td>\n",
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       "      <td>28</td>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
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       "      <td>northwest</td>\n",
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       "      <th>4</th>\n",
       "      <td>32</td>\n",
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       "      <th>1333</th>\n",
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       "      <td>30.970</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.54830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.920</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.850</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.800</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.94500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.070</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex     bmi  children smoker     region      charges\n",
       "0      19  female  27.900         0    yes  southwest  16884.92400\n",
       "1      18    male  33.770         1     no  southeast   1725.55230\n",
       "2      28    male  33.000         3     no  southeast   4449.46200\n",
       "3      33    male  22.705         0     no  northwest  21984.47061\n",
       "4      32    male  28.880         0     no  northwest   3866.85520\n",
       "...   ...     ...     ...       ...    ...        ...          ...\n",
       "1333   50    male  30.970         3     no  northwest  10600.54830\n",
       "1334   18  female  31.920         0     no  northeast   2205.98080\n",
       "1335   18  female  36.850         0     no  southeast   1629.83350\n",
       "1336   21  female  25.800         0     no  southwest   2007.94500\n",
       "1337   61  female  29.070         0    yes  northwest  29141.36030\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"../Downloads/beginner datasets 1/insurance.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bb83ad93-e80a-4cea-bee5-1ea195d35b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1338, 7)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18e292d5-2134-405d-8f45-fcf26f44c241",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>28</td>\n",
       "      <td>male</td>\n",
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       "      <th>3</th>\n",
       "      <td>33</td>\n",
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       "      <td>22.705</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
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       "      <td>northwest</td>\n",
       "      <td>3866.85520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex     bmi  children smoker     region      charges\n",
       "0   19  female  27.900         0    yes  southwest  16884.92400\n",
       "1   18    male  33.770         1     no  southeast   1725.55230\n",
       "2   28    male  33.000         3     no  southeast   4449.46200\n",
       "3   33    male  22.705         0     no  northwest  21984.47061\n",
       "4   32    male  28.880         0     no  northwest   3866.85520"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "63c91f29-133f-46c2-a1df-74d5b86658c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region', 'charges'], dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6bb2e87e-9688-4d11-b1d5-df08b647f420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "northeast    13406.384516\n",
       "northwest    12417.575374\n",
       "southeast    14735.411438\n",
       "southwest    12346.937377\n",
       "Name: charges, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"region\")[\"charges\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "053d974a-d265-4a86-a658-6e3986d3cc2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "smoker\n",
       "no      8434.268298\n",
       "yes    32050.231832\n",
       "Name: charges, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"smoker\")[\"charges\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "567fe9f0-9d3a-4161-9c50-766047b077de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "female    12569.578844\n",
       "male      13956.751178\n",
       "Name: charges, dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"sex\")[\"charges\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a7d0cd3d-5524-4607-be2c-474bc1df4a88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>47.740</th>\n",
       "      <th>48.070</th>\n",
       "      <th>49.060</th>\n",
       "      <th>50.380</th>\n",
       "      <th>52.580</th>\n",
       "      <th>53.130</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age</th>\n",
       "      <th>count</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>27.666667</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12.020815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.504385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>25.250000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>33.750000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.0000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>26.00000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>21.000</td>\n",
       "      <td>19.0000</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>32.0000</td>\n",
       "      <td>19.000</td>\n",
       "      <td>...</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>54.00000</td>\n",
       "      <td>47.0000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>46.0000</td>\n",
       "      <td>58.0000</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>22.0000</td>\n",
       "      <td>18.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">children</th>\n",
       "      <th>count</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.154701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">charges</th>\n",
       "      <th>count</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>4904.000350</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>7813.353433</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2455.844784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4619.986089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>3167.455850</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>3732.625100</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>4035.728100</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>5305.302600</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>4904.000350</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>6877.980100</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>5772.272600</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>9853.717600</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1694.7964</td>\n",
       "      <td>6640.544850</td>\n",
       "      <td>14455.64405</td>\n",
       "      <td>12829.455100</td>\n",
       "      <td>2775.19215</td>\n",
       "      <td>2585.269</td>\n",
       "      <td>1621.3402</td>\n",
       "      <td>2680.9493</td>\n",
       "      <td>32734.1863</td>\n",
       "      <td>1727.785</td>\n",
       "      <td>...</td>\n",
       "      <td>12592.5345</td>\n",
       "      <td>63770.42801</td>\n",
       "      <td>8083.9198</td>\n",
       "      <td>46113.511</td>\n",
       "      <td>9748.9106</td>\n",
       "      <td>9432.9253</td>\n",
       "      <td>11381.3254</td>\n",
       "      <td>2438.0552</td>\n",
       "      <td>44501.3982</td>\n",
       "      <td>1163.4627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 548 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "bmi                15.960       16.815       17.195        17.290      17.385  \\\n",
       "age      count     1.0000     2.000000      1.00000      3.000000     1.00000   \n",
       "         mean     18.0000    29.500000     26.00000     27.666667    23.00000   \n",
       "         std          NaN    12.020815          NaN      9.504385         NaN   \n",
       "         min      18.0000    21.000000     26.00000     18.000000    23.00000   \n",
       "         25%      18.0000    25.250000     26.00000     23.000000    23.00000   \n",
       "         50%      18.0000    29.500000     26.00000     28.000000    23.00000   \n",
       "         75%      18.0000    33.750000     26.00000     32.500000    23.00000   \n",
       "         max      18.0000    38.000000     26.00000     37.000000    23.00000   \n",
       "children count     1.0000     2.000000      1.00000      3.000000     1.00000   \n",
       "         mean      0.0000     1.500000      2.00000      1.333333     1.00000   \n",
       "         std          NaN     0.707107          NaN      1.154701         NaN   \n",
       "         min       0.0000     1.000000      2.00000      0.000000     1.00000   \n",
       "         25%       0.0000     1.250000      2.00000      1.000000     1.00000   \n",
       "         50%       0.0000     1.500000      2.00000      2.000000     1.00000   \n",
       "         75%       0.0000     1.750000      2.00000      2.000000     1.00000   \n",
       "         max       0.0000     2.000000      2.00000      2.000000     1.00000   \n",
       "charges  count     1.0000     2.000000      1.00000      3.000000     1.00000   \n",
       "         mean   1694.7964  4904.000350  14455.64405   7813.353433  2775.19215   \n",
       "         std          NaN  2455.844784          NaN   4619.986089         NaN   \n",
       "         min    1694.7964  3167.455850  14455.64405   3732.625100  2775.19215   \n",
       "         25%    1694.7964  4035.728100  14455.64405   5305.302600  2775.19215   \n",
       "         50%    1694.7964  4904.000350  14455.64405   6877.980100  2775.19215   \n",
       "         75%    1694.7964  5772.272600  14455.64405   9853.717600  2775.19215   \n",
       "         max    1694.7964  6640.544850  14455.64405  12829.455100  2775.19215   \n",
       "\n",
       "bmi               17.400     17.480     17.670      17.765    17.800  ...  \\\n",
       "age      count     1.000     1.0000     1.0000      1.0000     1.000  ...   \n",
       "         mean     21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "         std         NaN        NaN        NaN         NaN       NaN  ...   \n",
       "         min      21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "         25%      21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "         50%      21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "         75%      21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "         max      21.000    19.0000    26.0000     32.0000    19.000  ...   \n",
       "children count     1.000     1.0000     1.0000      1.0000     1.000  ...   \n",
       "         mean      1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "         std         NaN        NaN        NaN         NaN       NaN  ...   \n",
       "         min       1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "         25%       1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "         50%       1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "         75%       1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "         max       1.000     0.0000     0.0000      2.0000     0.000  ...   \n",
       "charges  count     1.000     1.0000     1.0000      1.0000     1.000  ...   \n",
       "         mean   2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "         std         NaN        NaN        NaN         NaN       NaN  ...   \n",
       "         min    2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "         25%    2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "         50%    2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "         75%    2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "         max    2585.269  1621.3402  2680.9493  32734.1863  1727.785  ...   \n",
       "\n",
       "bmi                 46.750       47.410     47.520     47.600     47.740  \\\n",
       "age      count      1.0000      1.00000     1.0000      1.000     1.0000   \n",
       "         mean      52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "         std           NaN          NaN        NaN        NaN        NaN   \n",
       "         min       52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "         25%       52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "         50%       52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "         75%       52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "         max       52.0000     54.00000    47.0000     37.000    52.0000   \n",
       "children count      1.0000      1.00000     1.0000      1.000     1.0000   \n",
       "         mean       5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "         std           NaN          NaN        NaN        NaN        NaN   \n",
       "         min        5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "         25%        5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "         50%        5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "         75%        5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "         max        5.0000      0.00000     1.0000      2.000     1.0000   \n",
       "charges  count      1.0000      1.00000     1.0000      1.000     1.0000   \n",
       "         mean   12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "         std           NaN          NaN        NaN        NaN        NaN   \n",
       "         min    12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "         25%    12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "         50%    12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "         75%    12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "         max    12592.5345  63770.42801  8083.9198  46113.511  9748.9106   \n",
       "\n",
       "bmi                48.070      49.060     50.380      52.580     53.130  \n",
       "age      count     1.0000      1.0000     1.0000      1.0000     1.0000  \n",
       "         mean     46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "         std          NaN         NaN        NaN         NaN        NaN  \n",
       "         min      46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "         25%      46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "         50%      46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "         75%      46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "         max      46.0000     58.0000    23.0000     22.0000    18.0000  \n",
       "children count     1.0000      1.0000     1.0000      1.0000     1.0000  \n",
       "         mean      2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "         std          NaN         NaN        NaN         NaN        NaN  \n",
       "         min       2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "         25%       2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "         50%       2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "         75%       2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "         max       2.0000      0.0000     1.0000      1.0000     0.0000  \n",
       "charges  count     1.0000      1.0000     1.0000      1.0000     1.0000  \n",
       "         mean   9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "         std          NaN         NaN        NaN         NaN        NaN  \n",
       "         min    9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "         25%    9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "         50%    9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "         75%    9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "         max    9432.9253  11381.3254  2438.0552  44501.3982  1163.4627  \n",
       "\n",
       "[24 rows x 548 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"bmi\").describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d79db095-42b6-428a-b401-63a1a56125c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width         species\n",
       "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
       "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
       "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
       "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
       "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
       "..            ...          ...           ...          ...             ...\n",
       "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
       "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
       "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
       "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
       "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"../Downloads/beginner datasets 2/iris.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "6a0b2c09-41df-48eb-a731-021c73d909a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.758666666666666"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.petal_length.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "6002200b-4d91-4357-8c01-f019e99f81d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        1.464\n",
       "Iris-versicolor    4.260\n",
       "Iris-virginica     5.552\n",
       "Name: petal_length, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"species\")[\"petal_length\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "9ee1a613-e961-4cbf-bea5-84c7747aec32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        0.244\n",
       "Iris-versicolor    1.326\n",
       "Iris-virginica     2.026\n",
       "Name: petal_width, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"species\")[\"petal_width\"].agg(\"mean\",\"max\",\"min\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52946e1e-39ac-47ab-b443-42766aa1e4dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f2ff4e02-37d1-491e-9a32-9df751da6d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
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       "      <th>type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
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       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.99680</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.99700</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.99800</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>6</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.99780</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>6.2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.039</td>\n",
       "      <td>24.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.99114</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.50</td>\n",
       "      <td>11.2</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6493</th>\n",
       "      <td>6.6</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.36</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.047</td>\n",
       "      <td>57.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>0.99490</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6494</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.041</td>\n",
       "      <td>30.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.99254</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.46</td>\n",
       "      <td>9.4</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6495</th>\n",
       "      <td>5.5</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.022</td>\n",
       "      <td>20.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>0.98869</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.38</td>\n",
       "      <td>12.8</td>\n",
       "      <td>7</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.020</td>\n",
       "      <td>22.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.98941</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.32</td>\n",
       "      <td>11.8</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6497 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0               7.4              0.70         0.00             1.9      0.076   \n",
       "1               7.8              0.88         0.00             2.6      0.098   \n",
       "2               7.8              0.76         0.04             2.3      0.092   \n",
       "3              11.2              0.28         0.56             1.9      0.075   \n",
       "4               7.4              0.70         0.00             1.9      0.076   \n",
       "...             ...               ...          ...             ...        ...   \n",
       "6492            6.2              0.21         0.29             1.6      0.039   \n",
       "6493            6.6              0.32         0.36             8.0      0.047   \n",
       "6494            6.5              0.24         0.19             1.2      0.041   \n",
       "6495            5.5              0.29         0.30             1.1      0.022   \n",
       "6496            6.0              0.21         0.38             0.8      0.020   \n",
       "\n",
       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
       "...                   ...                   ...      ...   ...        ...   \n",
       "6492                 24.0                  92.0  0.99114  3.27       0.50   \n",
       "6493                 57.0                 168.0  0.99490  3.15       0.46   \n",
       "6494                 30.0                 111.0  0.99254  2.99       0.46   \n",
       "6495                 20.0                 110.0  0.98869  3.34       0.38   \n",
       "6496                 22.0                  98.0  0.98941  3.26       0.32   \n",
       "\n",
       "      alcohol  quality   type  \n",
       "0         9.4        5    red  \n",
       "1         9.8        5    red  \n",
       "2         9.8        5    red  \n",
       "3         9.8        6    red  \n",
       "4         9.4        5    red  \n",
       "...       ...      ...    ...  \n",
       "6492     11.2        6  white  \n",
       "6493      9.6        5  white  \n",
       "6494      9.4        6  white  \n",
       "6495     12.8        7  white  \n",
       "6496     11.8        6  white  \n",
       "\n",
       "[6497 rows x 13 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"../Downloads/beginner datasets 2/wine.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "a45a8242-a116-4318-8d77-3379a845c112",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',\n",
       "       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',\n",
       "       'pH', 'sulphates', 'alcohol', 'quality', 'type'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "aef2b0a7-478d-45a8-923b-9dc15156949b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la concentrazione di alcohol per qualità è: quality\n",
      "3    10.215000\n",
      "4    10.180093\n",
      "5     9.837783\n",
      "6    10.587553\n",
      "7    11.386006\n",
      "8    11.678756\n",
      "9    12.180000\n",
      "Name: alcohol, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "CA_per_quality = df.groupby(\"quality\")[\"alcohol\"].mean()\n",
    "print(\"la concentrazione di alcohol per qualità è:\", CA_per_quality)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "b4617ab1-397d-4816-a883-e675516c0c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la concentrazione di alcohol per qualità è: type\n",
      "red      10.422983\n",
      "white    10.514267\n",
      "Name: alcohol, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "CA_per_tipo = df.groupby(\"type\")[\"alcohol\"].mean()\n",
    "print(\"la concentrazione di alcohol per qualità è:\", CA_per_tipo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5ac33a91-c792-47af-a812-427f1ab4b25c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la concentrazione di pH per tipo & qualità è: type   quality\n",
      "red    3          3.398000\n",
      "       4          3.381509\n",
      "       5          3.304949\n",
      "       6          3.318072\n",
      "       7          3.290754\n",
      "       8          3.267222\n",
      "white  3          3.187500\n",
      "       4          3.182883\n",
      "       5          3.168833\n",
      "       6          3.188599\n",
      "       7          3.213898\n",
      "       8          3.218686\n",
      "       9          3.308000\n",
      "Name: pH, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pH_per_tipo_e_qualità = df.groupby([\"type\" ,\"quality\"]) [\"pH\"].mean()\n",
    "print(\"la concentrazione di pH per tipo & qualità è:\", pH_per_tipo_e_qualità)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "9b7dc639-f81e-4e42-91ff-f3f0312604f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la concentrazione di sulphati per tipo & qualità è: type   quality\n",
      "red    3          0.570000\n",
      "       4          0.596415\n",
      "       5          0.620969\n",
      "       6          0.675329\n",
      "       7          0.741256\n",
      "       8          0.767778\n",
      "white  3          0.474500\n",
      "       4          0.476135\n",
      "       5          0.482203\n",
      "       6          0.491106\n",
      "       7          0.503102\n",
      "       8          0.486229\n",
      "       9          0.466000\n",
      "Name: sulphates, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "s_per_tipo_e_qualità = df.groupby([\"type\" ,\"quality\"]) [\"sulphates\"].mean()\n",
    "print(\"la concentrazione di sulphati per tipo & qualità è:\", s_per_tipo_e_qualità)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "079c4205-9cc0-4b15-8cae-167e9dfe7105",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "26908b35-e136-4ab4-86a8-23b92f186a64",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
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       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.6</td>\n",
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       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.9</td>\n",
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       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
       "0    0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
       "1    0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
       "2    0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n",
       "3    0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
       "4    0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
       "..       ...   ...    ...   ...    ...    ...   ...     ...  ...  ...   \n",
       "501  0.06263   0.0  11.93     0  0.573  6.593  69.1  2.4786    1  273   \n",
       "502  0.04527   0.0  11.93     0  0.573  6.120  76.7  2.2875    1  273   \n",
       "503  0.06076   0.0  11.93     0  0.573  6.976  91.0  2.1675    1  273   \n",
       "504  0.10959   0.0  11.93     0  0.573  6.794  89.3  2.3889    1  273   \n",
       "505  0.04741   0.0  11.93     0  0.573  6.030  80.8  2.5050    1  273   \n",
       "\n",
       "     ptratio   black  lstat  medv  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "1       17.8  396.90   9.14  21.6  \n",
       "2       17.8  392.83   4.03  34.7  \n",
       "3       18.7  394.63   2.94  33.4  \n",
       "4       18.7  396.90   5.33  36.2  \n",
       "..       ...     ...    ...   ...  \n",
       "501     21.0  391.99   9.67  22.4  \n",
       "502     21.0  396.90   9.08  20.6  \n",
       "503     21.0  396.90   5.64  23.9  \n",
       "504     21.0  393.45   6.48  22.0  \n",
       "505     21.0  396.90   7.88  11.9  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"../Downloads/beginner datasets 1/boston.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "93cee358-8b49-4966-a95d-8e36bb206f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax',\n",
       "       'ptratio', 'black', 'lstat', 'medv'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5ba92bc-1898-460f-80f3-547c18fb1c71",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "765f7426-18ba-4cf8-a11a-bc4634a52c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.538\n",
       "1      0.469\n",
       "2      0.469\n",
       "3      0.458\n",
       "4      0.458\n",
       "       ...  \n",
       "501    0.573\n",
       "502    0.573\n",
       "503    0.573\n",
       "504    0.573\n",
       "505    0.573\n",
       "Name: nox, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "35f7a5c7-f5ec-4cf2-81bb-cea1768e7cd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.284634387351779"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_stanza = df[\"rm\"].mean()\n",
    "media_stanza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3758862d-b6e8-4704-9c4d-ed1a407e8705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "501    False\n",
       "502    False\n",
       "503    False\n",
       "504    False\n",
       "505    False\n",
       "Name: age, Length: 506, dtype: bool"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anno_ab = df [\"age\"] > 1940\n",
    "anno_ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "e521a726-90f1-44ca-84b5-1b102c0e6b88",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'>' not supported between instances of 'list' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[116], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m distanze_ponderate \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdis\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m5\u001b[39m\n\u001b[0;32m      2\u001b[0m distanze_ponderate\n",
      "\u001b[1;31mTypeError\u001b[0m: '>' not supported between instances of 'list' and 'int'"
     ]
    }
   ],
   "source": [
    "distanze_ponderate = [\"dis\"] > 5\n",
    "distanze_ponderate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "d92df4aa-8a0b-4ea4-8154-288dbc8597d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      2\n",
       "2      2\n",
       "3      3\n",
       "4      3\n",
       "      ..\n",
       "501    1\n",
       "502    1\n",
       "503    1\n",
       "504    1\n",
       "505    1\n",
       "Name: rad, Length: 506, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "833e376f-8d09-4944-a076-4777d6362719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero di valori con aliquota dell'imposta fondiarai sul valore pieno per 10000 dollari: 0\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'numpy.int64' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[118], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m count \u001b[38;5;241m=\u001b[39m TAX\u001b[38;5;241m.\u001b[39msum()\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumero di valori con aliquota dell\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimposta fondiarai sul valore pieno per 10000 dollari:\u001b[39m\u001b[38;5;124m\"\u001b[39m , count)\n\u001b[1;32m----> 4\u001b[0m count(TAX)\n",
      "\u001b[1;31mTypeError\u001b[0m: 'numpy.int64' object is not callable"
     ]
    }
   ],
   "source": [
    "TAX = df[\"tax\"] == 10000\n",
    "count = TAX.sum()\n",
    "print(\"Numero di valori con aliquota dell'imposta fondiarai sul valore pieno per 10000 dollari:\" , count)\n",
    "count(TAX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bbf51e9f-694d-4f8d-a80c-8a252785cd2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      15.3\n",
       "1      17.8\n",
       "2      17.8\n",
       "3      18.7\n",
       "4      18.7\n",
       "       ... \n",
       "501    21.0\n",
       "502    21.0\n",
       "503    21.0\n",
       "504    21.0\n",
       "505    21.0\n",
       "Name: ptratio, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ptratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "39ee35ee-8f4b-46fd-bba8-537a480230c9",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for *: 'method' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[130], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m Prezzo_medio \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchas\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedv\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmean\n\u001b[1;32m----> 2\u001b[0m Prezzo_1000 \u001b[38;5;241m=\u001b[39m Prezzo_medio\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1000\u001b[39m\n\u001b[0;32m      3\u001b[0m Prezzo_1000\u001b[38;5;241m.\u001b[39mround()\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'method' and 'int'"
     ]
    }
   ],
   "source": [
    "Prezzo_medio = df.groupby(\"chas\")[\"medv\"].mean\n",
    "Prezzo_1000 = Prezzo_medio*1000\n",
    "Prezzo_1000.round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "ce1c3f5b-b088-4450-93b5-dc1e61f50658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim\n",
       "0.00632     24.0\n",
       "0.00906     32.2\n",
       "0.01096     22.0\n",
       "0.01301     32.7\n",
       "0.01311     35.4\n",
       "            ... \n",
       "45.74610     7.0\n",
       "51.13580    15.0\n",
       "67.92080     5.0\n",
       "73.53410     8.8\n",
       "88.97620    10.4\n",
       "Name: medv, Length: 504, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CRIM = df.groupby(\"crim\")[\"medv\"].mean()\n",
    "CRIM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a2db8372-8f97-4449-a374-5fd64517e7ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='crim', ylabel='medv'>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,[\"crim\", \"medv\"]].plot.scatter (x=\"crim\", y=\"medv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "02336954-85ed-4327-8b46-1251555f1630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ptratio  medv\n",
       "12.6     30.3    7.107\n",
       "         33.3    7.287\n",
       "         34.6    7.274\n",
       "13.0     22.8    5.560\n",
       "         30.1    6.842\n",
       "                 ...  \n",
       "21.2     19.2    6.458\n",
       "         19.6    6.326\n",
       "         23.0    6.372\n",
       "22.0     18.2    5.663\n",
       "         20.6    5.936\n",
       "Name: rm, Length: 440, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_istanza_per_alunni_insegnanti = df.groupby([\"ptratio\",\"medv\"])[\"rm\"].mean()\n",
    "media_istanza_per_alunni_insegnanti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e15d79bb-ecdf-40ff-ac8a-663688ce04c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ptratio', ylabel='medv'>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,[\"ptratio\",\"medv\"]].plot.scatter (x=\"ptratio\", y=\"medv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "3a636699-da91-4397-9515-49e12bd1fdef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,[\"ptratio\",\"medv\"]].plot.hist (x=\"ptratio\", y=\"medv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a833e3cf-17e1-4c24-9931-bb5e200beffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rad  rm     tax\n",
      "1    5.706  304    28.4\n",
      "     5.876  315    19.1\n",
      "     6.030  273    80.8\n",
      "     6.031  304    23.3\n",
      "     6.108  315    32.0\n",
      "                   ... \n",
      "24   7.016  666    97.5\n",
      "     7.061  666    77.0\n",
      "     7.313  666    97.9\n",
      "     7.393  666    99.3\n",
      "     8.780  666    82.9\n",
      "Name: age, Length: 495, dtype: float64 age\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rad  rm     tax\n",
       "1    5.706  304    28\n",
       "     5.876  315    19\n",
       "     6.030  273    81\n",
       "     6.031  304    23\n",
       "     6.108  315    32\n",
       "                   ..\n",
       "24   7.016  666    98\n",
       "     7.061  666    77\n",
       "     7.313  666    98\n",
       "     7.393  666    99\n",
       "     8.780  666    83\n",
       "Name: age, Length: 495, dtype: int32"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "media_età_abitazione = df.groupby([\"rad\", \"rm\", \"tax\"])[\"age\"].mean()\n",
    "print(media_età_abitazione, \"age\")\n",
    "media_età_abitazione.round().astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "4166bca9-d1f4-4cb1-8614-ecfc3d34c434",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,[\"rad\",\"age\"]].plot.hist (x=\"rad\", y=\"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "48b26d15-0edf-42f8-98bc-8cc6fb150711",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n",
      "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
      "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
      "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
      "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
      "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
      "\n",
      "    black  lstat  medv  \n",
      "0  396.90   4.98  24.0  \n",
      "1  396.90   9.14  21.6  \n",
      "2  392.83   4.03  34.7  \n",
      "3  394.63   2.94  33.4  \n",
      "4  396.90   5.33  36.2  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "file_path = \"../Downloads/beginner datasets 1/boston.csv\"\n",
    "df = pd.read_csv(file_path)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "6c7960f5-3cbd-475b-bf31-0e9dc5fc9707",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               rm   ptratio      medv\n",
      "rm       1.000000 -0.355501  0.695360\n",
      "ptratio -0.355501  1.000000 -0.507787\n",
      "medv     0.695360 -0.507787  1.000000\n"
     ]
    }
   ],
   "source": [
    "correlazioni = df[[\"rm\", \"ptratio\",\"medv\"]].corr()\n",
    "print(correlazioni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e76e4760-1c4c-4a47-816c-54e20c110712",
   "metadata": {},
   "outputs": [],
   "source": [
    "1.correlazione tra il numero di stanze e il rapporto alunni-insegnante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "96982ba1-a73c-45f3-ba64-53393118eea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter (df[\"rm\"], df[\"ptratio\"])\n",
    "plt.title(\"correlazione tra numero di stanze e rapporto alunni-insegnanti\")\n",
    "plt.xlabel(\"Numero di stanze (RM)\")\n",
    "plt.ylabel(\"Rapporto alunni-insegnanti(PTRATIO)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1510517e-9ea7-4a8f-b4ac-c96e4b9be82b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "fbd59e7a-8507-44f0-b60d-397f851de43f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "195e616e-11f9-4e20-882c-92f6e4137ea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creazione di DataFrame\n",
    "employees_df = pd.DataFrame({ \n",
    "    'employee_id': [101, 102, 103, 104, 105],\n",
    "    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'], \n",
    "    'department_id': [1, 2, 1, 2, 3]\n",
    "})\n",
    "\n",
    "departments_df = pd.DataFrame({ \n",
    "    'department_id': [1, 2, 3], \n",
    "    'department_name': ['HR', 'IT', 'Finance'], \n",
    "    'location': ['New York', 'San Francisco', 'Chicago'] \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "28429dfe-5536-418e-8e00-1d579d9c03be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>name</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Alice</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>102</td>\n",
       "      <td>Bob</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>103</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>David</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Emma</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id     name  department_id\n",
       "0          101    Alice              1\n",
       "1          102      Bob              2\n",
       "2          103  Charlie              1\n",
       "3          104    David              2\n",
       "4          105     Emma              3"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "2b250bc8-f4b7-4542-868a-c48518da1558",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_id</th>\n",
       "      <th>department_name</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>HR</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department_id department_name       location\n",
       "0              1              HR       New York\n",
       "1              2              IT  San Francisco\n",
       "2              3         Finance        Chicago"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d19cd961-3e9d-4911-ad1a-f8fb5692cd41",
   "metadata": {},
   "outputs": [],
   "source": [
    "emplyees_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "45ce3985-d081-4150-94cb-fbae12c5c871",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>name</th>\n",
       "      <th>department_id</th>\n",
       "      <th>department_name</th>\n",
       "      <th>location</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>Alice</td>\n",
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       "      <td>HR</td>\n",
       "      <td>New York</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>1</td>\n",
       "      <td>HR</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>Bob</td>\n",
       "      <td>2</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104</td>\n",
       "      <td>David</td>\n",
       "      <td>2</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105</td>\n",
       "      <td>Emma</td>\n",
       "      <td>3</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employee_id     name  department_id department_name       location\n",
       "0          101    Alice              1              HR       New York\n",
       "1          103  Charlie              1              HR       New York\n",
       "2          102      Bob              2              IT  San Francisco\n",
       "3          104    David              2              IT  San Francisco\n",
       "4          105     Emma              3         Finance        Chicago"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unione dei DataFrame usando merge()\n",
    "merge_df = employees_df.merge(departments_df, on= \"department_id\")\n",
    "merge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f508dee-eece-4867-840d-33c2b8281d88",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transformazione di dipartiment_id in indice per entrambi i dataframe\n",
    "employees_df.set_index(\"department_id\", inplace=True)\n",
    "departments_df.set_index(\"department_id\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "afa3e1fb-211e-400e-a343-e9c0e3245678",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department_id</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>103</td>\n",
       "      <td>Charlie</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>David</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Emma</td>\n",
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      ],
      "text/plain": [
       "               employee_id     name\n",
       "department_id                      \n",
       "1                      101    Alice\n",
       "2                      102      Bob\n",
       "1                      103  Charlie\n",
       "2                      104    David\n",
       "3                      105     Emma"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "c8dc39e2-8c70-465a-8f82-819fe9c0c3bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_id</th>\n",
       "      <th>department_name</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>HR</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   department_id department_name       location\n",
       "0              1              HR       New York\n",
       "1              2              IT  San Francisco\n",
       "2              3         Finance        Chicago"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "a3950c56-df40-4578-9f6f-6e3c00b26761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employee_id</th>\n",
       "      <th>name</th>\n",
       "      <th>department_id</th>\n",
       "      <th>department_name</th>\n",
       "      <th>location</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>Alice</td>\n",
       "      <td>2.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>2.0</td>\n",
       "      <td>IT</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102</td>\n",
       "      <td>Bob</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104</td>\n",
       "      <td>David</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105</td>\n",
       "      <td>Emma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               employee_id     name  department_id department_name  \\\n",
       "department_id                                                        \n",
       "1                      101    Alice            2.0              IT   \n",
       "1                      103  Charlie            2.0              IT   \n",
       "2                      102      Bob            3.0         Finance   \n",
       "2                      104    David            3.0         Finance   \n",
       "3                      105     Emma            NaN             NaN   \n",
       "\n",
       "                    location  \n",
       "department_id                 \n",
       "1              San Francisco  \n",
       "1              San Francisco  \n",
       "2                    Chicago  \n",
       "2                    Chicago  \n",
       "3                        NaN  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique dei DataFrame trasformati usando join()\n",
    "joined_df = employees_df.join(departments_df)\n",
    "joined_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "2b239607-817d-4a4d-a517-af8039af48c0",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sqlachemy'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[173], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdotenv\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01msqlachemy\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msqlaclchemy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m create_engine\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'sqlachemy'"
     ]
    }
   ],
   "source": [
    "import dotenv\n",
    "import os\n",
    "from sqlaclchemy import create_engine\n",
    "import pandas as pd\n",
    "\n",
    "dotenv.load_dotenv(override=True)\n",
    "\n",
    "username=os.gotenv(\"username\")\n",
    "password=os.gotenv(\"password\")\n",
    "host=os.getenv(\"host\")\n",
    "dbname=os.gotenv(\"dbname\")\n",
    "\n",
    "conn_string = f\"mysql+pymysql://{username}:{password}@{host}/{dbname}\"\n",
    "db_engine = create_engine(conn_string)\n",
    "\n",
    "show_tables = pd.read_sql(\"show Tables\",db_engine)\n",
    "\n",
    "df_employee = pd.read_sql(\"SELECT * FROM dimemployee\",db_engine)\n",
    "df_employee_sales_territory = pd.read_sql(\"SELECT * FROM dimemployeesalesterritory\", db_engine)\n",
    "df_sales_territory = pd.read_sql(\"SELECT * FROM dimemployee\", db_engine)\n",
    "db_engine.dispose()\n",
    "show_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee96db56-59d0-4b08-a4de-6c6bdfca5799",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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