Matrimoni_Lecce.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quando ci si sposa a Lecce?\n",
    "Ormai da diversi anni, il Comune di Lecce è impegnato fruttuosamente nell'apertura dei suoi dati. Tra questi, vi sono alcuni dataset riguardanti i matrimoni celebrati nei mesi degli anni 2015-2017 (i dati 2018 sono parziali). Anche se questo campione non è molto numeroso, possiamo usarlo per farci un'idea sui mesi in cui vengono celebrati più matrimoni nella splendida città salentina."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Domenico Delle Side (nico @ delleside.org)\n",
    "# tutto il codice e le figure presenti in questo notebook jupyter sono rilasciati\n",
    "# con licenza Creative Commons Attribution 4.0 International (CC BY 4.0)\n",
    "# https://creativecommons.org/licenses/by/4.0/\n",
    "\n",
    "# carichiamo i moduli python necessari\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.api.types import CategoricalDtype\n",
    "\n",
    "# per semplificare le operazioni, creiamo un tipo di dato categorico\n",
    "# per i mesi dell'anno\n",
    "months_cat = CategoricalDtype(['gennaio', 'febbraio', 'marzo',\n",
    "                               'aprile', 'maggio', 'giugno',\n",
    "                               'luglio', 'agosto', 'settembre',\n",
    "                               'ottobre', 'novembre', 'dicembre'], ordered=True)\n",
    "\n",
    "# creiamo un array associativo (dict), con l'url del csv per ogni anno\n",
    "anni_url = {'2015': 'https://goo.gl/P5NUSS', \n",
    "            '2016': 'https://goo.gl/lgc15l', \n",
    "            '2017': 'https://goo.gl/HIfh7n'}\n",
    "# in questa lista ospiteremo di dati dei singoli anni\n",
    "dati = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carichiamo i dati\n",
    "for anno in anni_url:\n",
    "    dati_tmp = pd.read_csv(anni_url[anno])\n",
    "    dati_tmp = dati_tmp.fillna(0)\n",
    "    dati.append(dati_tmp[['Anno', 'mese', 'matrimoni religiosi', 'matrimoni civili']])\n",
    "\n",
    "del dati_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# concateniamo i dati in un unico dataframe\n",
    "alldata = pd.concat(dati)\n",
    "# creiamo una singola serie contenente la somma di matrimoni civili e religiosi\n",
    "alldata['matrimoni'] = alldata['matrimoni civili'] + alldata['matrimoni religiosi']\n",
    "del alldata['matrimoni civili']\n",
    "del alldata['matrimoni religiosi']\n",
    "alldata.mese = alldata.mese.astype(months_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# raggruppiamo i dati per mese\n",
    "permonth = alldata.groupby('mese').agg({'matrimoni': np.sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>matrimoni</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mese</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gennaio</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>febbraio</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marzo</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aprile</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maggio</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>giugno</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>luglio</th>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agosto</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>settembre</th>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ottobre</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>novembre</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dicembre</th>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           matrimoni\n",
       "mese                \n",
       "gennaio           31\n",
       "febbraio          22\n",
       "marzo             15\n",
       "aprile            58\n",
       "maggio           105\n",
       "giugno           216\n",
       "luglio           169\n",
       "agosto           190\n",
       "settembre        260\n",
       "ottobre           98\n",
       "novembre          32\n",
       "dicembre          83"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "permonth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(12,6))\n",
    "permonth['matrimoni'].plot(kind='bar')\n",
    "plt.title('Matrimoni 2015-2017 per mese')\n",
    "plt.xticks(rotation=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come si vede dalla figura, il periodo estivo è quello in cui ci sono più matrimoni: settembre e giugno sono i mesi più gettonati, con agosto e luglio che seguono a ruota. Gli altri mesi sono senz'altro meno 'caldi'.\n",
    "\n",
    "Il mese che in assoluto vede meno matrimoni è marzo. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}