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Copy pathEsercizio 1 w.ipynb.txt
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Esercizio 1 w.ipynb.txt
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{
"cells": [
{
"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",
"metadata": {
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"<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",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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": {
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"jupyter": {
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}
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\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>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 "
]
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"source": [
"df.describe()"
]
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"id": "0c9d1cb9-7b38-4799-bc85-459c733e4b7a",
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{
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"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",
"<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>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": {
"text/html": [
"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>sex</th>\n",
" <th>bmi</th>\n",
" <th>children</th>\n",
" <th>smoker</th>\n",
" <th>region</th>\n",
" <th>charges</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19</td>\n",
" <td>female</td>\n",
" <td>27.900</td>\n",
" <td>0</td>\n",
" <td>yes</td>\n",
" <td>southwest</td>\n",
" <td>16884.92400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>18</td>\n",
" <td>male</td>\n",
" <td>33.770</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>southeast</td>\n",
" <td>1725.55230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>male</td>\n",
" <td>33.000</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>southeast</td>\n",
" <td>4449.46200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>33</td>\n",
" <td>male</td>\n",
" <td>22.705</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>northwest</td>\n",
" <td>21984.47061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>32</td>\n",
" <td>male</td>\n",
" <td>28.880</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>northwest</td>\n",
" <td>3866.85520</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",
" </tr>\n",
" <tr>\n",
" <th>1333</th>\n",
" <td>50</td>\n",
" <td>male</td>\n",
" <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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