Esercizio2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85305688-bd08-4ce6-a3af-55a3f9f9a786",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c19b712b-3d56-4391-821e-b3a576e9f085",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PIEMONTE'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv = pd.read_csv(\"./Datasets2/posti_letto_per_struttura_ospedaliera.csv\",encoding='latin-1')\n",
    "csv[\"Descrizione Regione\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0d26c396-bf6a-48b5-867b-4a66d539339b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Anno                                 int64\n",
       "Codice Regione                       int64\n",
       "Descrizione Regione                 object\n",
       "Codice Azienda                       int64\n",
       "Tipo Azienda                         int64\n",
       "Codice struttura                     int64\n",
       "Denominazione struttura             object\n",
       "Indirizzo                           object\n",
       "Codice Comune                        int64\n",
       "Comune                              object\n",
       "Sigla provincia                     object\n",
       "Codice tipo struttura              float64\n",
       "Descrizione tipo struttura          object\n",
       "Tipo di Disciplina                  object\n",
       "Posti letto degenza ordinaria        int64\n",
       "Posti letto degenza a pagamento      int64\n",
       "Posti letto Day Hospital             int64\n",
       "Posti letto Day Surgery              int64\n",
       "Totale posti letto                   int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e4a00f69-0375-467c-8aec-0a84544afd0e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "csv = csv.convert_dtypes(convert_floating=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ec732ac-c600-4b04-901c-9479bdee5955",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "csv2015 = csv[csv[\"Anno\"]==2015]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d87932e2-01f1-4de4-948c-c9a72493b494",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "letti2015 = csv2015['Totale posti letto']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "88d571a8-cd48-4023-9cfe-6f6b2ad17582",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          75.0\n",
       "mean     121.213333\n",
       "std      255.733301\n",
       "min             1.0\n",
       "25%            20.0\n",
       "50%            49.0\n",
       "75%           117.5\n",
       "max          2097.0\n",
       "Name: Totale posti letto, dtype: Float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letti2015.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "34e67b41-f949-41b7-a0af-a2f5695cf2cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram = letti2015.hist(bins = 50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}