lab-visualization/ADM Lab 2 - Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - MatplotLib\n",
    "\n",
    "We will work with the [matplotlib](http://matplotlib.org/) module in get a 101 introduction on data visualizion as an approach of understanding data and representing certain aspects of it. We will use the [pyplot](http://matplotlib.org/api/pyplot_api.html) module that combines pyplot with numpy into a single namespace to provides a MATLAB-like plotting framework.\n",
    "\n",
    "The introduction is based on the [2nd Laboratory notes](http://ichatz.me/uniroma1/adm-2019/uniroma1-datascience-adm-ichatz-lab2-visualization.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([3, 11, 2, 54], label= 'year 2010')\n",
    "plt.plot([44, 40, 48, 38], label = 'year 2020')\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Traffic (# cars)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(211)\n",
    "plt.plot([45, 22, 11])\n",
    "plt.subplot(212)\n",
    "plt.plot([111, 1, 13])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V7Hc62BZDVV410c+gf2Scd860caiumXcb2hge85GfnsQXtvjHT717db6OjalUDPD5DB9d6qLG5eZgfTPDYz7WF2ewr8rBY7eXUpi5tLs8a6KfomdojLdPt3KovoXfnm1ndNxHYWYyu7b4B+q4s1yH2FMqlvUOj/Fra+DzT692kxAn7NxYxH6ngx0bCpfk/39N9ED34ChvnfIPsff78x2MeQ227BR2VZSwp9LG9rJcHWJPqWXoXGsfNbUefnHUQ0f/KAUZyTy+3d83f23R0hn4fNkm+s7+Ed461crBumb+cKGTcZ/BnpvKnkobuypKuM2es2y6XimlZjbm9fFeQzvVLjfvnGnD6zNsL8thv9PBI1ttZEb5wOfLKtG39Q7z5skWDta18NGlTnwGyvPT2F1pY0+FTUe2UUrNqq1vmFc+9ffNP9/WT2piPLsr/QOf37UqLypzyLJI9G+fbuW///Yin1zxD7G3pjCdPZU2dlfY2GTLjMo/jFIquhljOObuptrl4VfHm+gfGWdlfhr7quw8XmWPqmqzyyLRv1zr4X/87qI1xF4J64qXTtuaUir6DY16eeNkM9WfePjDxU5E4HPrCtnvtPPQ5uKId71eFoneGKNn7UqpsLjaOciBWjcHaj009QyTk5YYKK62ZUV2RGJaFoleKaXCzeszfHC+g5paD2+ebGF03MdmWxb7nXb23lZKbnr4KtZqoldKqRDrHhzlteNNVLvc1Df2khQfx0Nb/AOf37u2IOTdtzXRK6VUGJ1q6qWm1s0rnzZybXAMW3YKj2+380SVnfKC9JCsUxO9UkpFwMi4l7dPt1HtcvP+2XZ8Bu5alcc+p4M9lSWLWvVWE71SSkVYS88wLx/1UONyc7lzkIzkBL641cY+p4PtZTkL7kyiiV4ppaKEMYZPLl+j2uXm1yeaGRrzsqYwnX1OB1/ZXkpR5q0NfD7XRB+SKj4isktEGkTkvIj8IBTrUEqppUJEuHNVHv913zY++Y8P8rePV5KblsSzh85wqK4l5Otf9CGSRCQe+EfgIcADfCIirxljTi32upRSaqnJSE7gX91Rxr+6o4wL7f1hKZUcijP6O4HzxpiLxphR4OfA3hCsRymllrQ1hRlkhaFwWigGPS0F3EHvPcBdUxcSkWeAZ6y3/SLScIvrKwA6bvGzoaRxzY/GNX/RGpvGNT8LiWvlXBaK2OjWxpjngOcW+j0i4prLzYhw07jmR+Oav2iNTeOan3DEFYqmm0bAEfTebk1TSikVAaFI9J8A60RklYgkAU8Cr4VgPUoppeZg0ZtujDHjIvInwJtAPPC8MebkYq8nyIKbf0JE45ofjWv+ojU2jWt+Qh5XVDwwpZRSKnSW3rDnSiml5kUTvVJKxbioTvSzlVIQkWQRecma/5GIlAfN+6E1vUFEHg5zXH8uIqdE5ISIvC0iK4PmeUXkmPWzqDep5xDX0yLSHrT+bwXNe0pEzlk/T4U5rr8PiumsiHQHzQvl9npeRNpEpP4m80VE/sGK+4SIbA+aF5LtNYeYvmbFUiciH4rItqB5l63px0Rk0YtHzSG2HSLSE/T3+sugeSErizKHuP4iKKZ6a5/Ks+aFZJuJiENE3rXywEkR+dNplgnf/mWMicof/DdyLwCrgSTgOLB5yjJ/DPyT9fpJ4CXr9WZr+WRglfU98WGM6wEgzXr9RxNxWe/7I7i9ngZ+Ms1n84CL1u9c63VuuOKasvx38d/AD+n2sr77PmA7UH+T+XuAQ4AAdwMfhWF7zRbTPRPrAnZPxGS9vwwURHB77QBeX+g+sNhxTVn2UeCdUG8zwAZst15nAmen+f8Ytv0rms/o51JKYS/wM+v1AeDzIiLW9J8bY0aMMZeA89b3hSUuY8y7xphB6+0R/M8ShNpCSk88DBw2xnQZY64Bh4FdEYrrq8CLi7TuGRlj3ge6ZlhkL/CC8TsC5IiIjRBur9liMsZ8aK0TwrdvTax7tu11MyEtizLPuMKyfxljmo0xR63XfcBp/FUDgoVt/4rmRD9dKYWpGyqwjDFmHOgB8uf42VDGFeyb+I/aE1JExCUiR0TksUWKaT5xPW5dJh4QkYkH26Jie1lNXKuAd4Imh2p7zcXNYg/l9pqPqfuWAd4SkVrxlxiJhM+IyHEROSQiW6xpUbG9RCQNf8J8OWhyyLeZ+JuUbwc+mjIrbPvXgvrRi8jzwBeBNmNMxZR5/x74r0ChMSYa60uEnIh8HXAC9wdNXmmMaRSR1cA7IlJnjLkQppB+BbxojBkRkW/jvxraGaZ1z8WTwAFjjDdoWiS3V9QSkQfwJ/p7gybfa22rIuCwiJyxznbD5Sj+v1e/iOwBXgHWhXH9s3kU+MAYE3z2H9JtJiIZ+A8sf2aM6V2s7513HFab0K19WOQ+oB//5UdF0HQH8FNgI1A1W6IvKCgw5eXltxyHUkotR7W1tR3GmMLZllvQGb0x5n0J6ukS5O+B7wGvzuV7ysvL0RGmlFJqfkTkylyWW/Q2ehHZCzQaY44v9ncrpVQsGB7z8uqxRr720yN8cD70LduLWuvGutnxI+ALc1g2UI++rKxsMcNQSqmoY4zhhKeHapeb14430Tc8jj03lb7h8ZCve7GLmq3B32viuL+XI3bgqIjcaYyZNDCiCapH73Q6teCOUiomdfaP8MtPG6lxeWho7SM5IY49lTb2Oe3cvSqfuDgJeQyLmuiNMXVA0cR7EbkMOJdrrxul1PI07vXx27PtVLvcvH26jXGfYZsjh7/5cgWPblsRluEDgy20e+WL+J+GKxARD/BXxph/XozAlFJqqbnQ3k+Ny8PLRz20942Qn57ENz5bzj6ng/XFmRGLa6G9br46y/zyhXy/UkpFu/6RcX59oolql4faK9eIjxMe2FDEPqednRuLSIyP/HOpERszVimllipjDJ9cvka1y82vTzQzNOZlTWE6P9y9kS9vL6UoMyXSIU6iiV4ppeaopWeYl496qHG5udw5SEZyAo/dvoInqhxsL8vB6oQSdTTRK6XUDEbGvfzmVBs1tW7eP9uOz8Bdq/L47s517K4sIS0p+tNo9EeolFIRcKqpl2qXm1eONdI9OIYtO4V/+8BanqiyszI/PdLhzYsmeqWUsnQPjvLqsSZqat3UN/aSFB/HQ1uK2e90cO/aAuLD0Oc9FDTRK6WWNa/P8MH5Dqpdbt462cqo18eWFVn871/awpe2rSA3PSnSIS6YJnql1LJ0tXOQmlo3L9d6aOoZJjs1kX99VxlPVNmpKM2OdHiLShO9UmrZGBr1cqi+mWqXmyMXuxCB+9YV8qNHNvHgpmJSEuMjHWJIaKJXSsU0YwyfurupcXn41fEm+kfGKctL4z98YT1f2W5nRU5qpEMMOU30SqmY1N43wi8/9VDt8nC+rZ/UxPhAMbE7y/PCUkwsWmiiV0rFjDGvj3fPtFFT6+GdM214fYbtZTk8+5VKHtlqIzPMxcSihSZ6pdSSd661j5paD7846qGjf5SCjGS+9blV7KtysLYoI9LhRZwmeqXUktQ7PMbrx5upqXXz6dVuEuKEnRuL2O90cP+GwqgoJhYtNNErpZYMn8/w0aUualxuDtY3MzzmY11RBv/xkU08dnspBRnJkQ4xKmmiV0pFvcbuIV6u9VBT68bdNURmcgJf2W5nv9PBNnt21BYTixaa6JVSUWl4zMvhU61Uu9z8/nwHxsA9a/L59w9t4OEtJaQmxWaf91DQRK+UihrGGE5axcRePdZEz9AYpTmpfHfnOvZV2XHkpUU6xCVJE71SKuKuDYzyyrFGql0eTjf3kpQQx64tJex3OrhnTXgG0I5lmuiVUhHh9RneP9dOjcvNb061Mer1UVmazX/eu4UvbSslO2159nkPBU30SqmwutwxYBUTa6Sld5jctES+fvdK9jntbLJlRTq8mHTLiV5Enge+CLQZYyqsaf8FeBQYBS4A3zDGdC9GoEqppWtgZJyDdc3UuDx8fLmLOIEdG4r4q0c38/lNxSQlaJ/3UFrIGf3/BH4CvBA07TDwQ2PMuIj8LfBD4PsLWIdSaokyxnD06jWqP/Hw+okmBka9rCpI53u7NvD4djvFWdE1gHYsu+VEb4x5X0TKp0x7K+jtEeCJW/1+pdTS1NY7zMtHG6mpdXOxfYC0pHi+uNXGPqcD58pc7fMeAaFso/83wEsh/H6lVJQYHffxzplWalwe3jvbjtdnuKM8l+/cv4ZHKm2kJ+vtwEgKydYXkR8D48C/zLDMM8AzAGVlZaEIQykVYg0tfVS73Pzy00a6BkYpykzm2/et5okqO6sLtZhYtFj0RC8iT+O/Sft5Y4y52XLGmOeA5wCcTudNl1NKRZeeoTFeO97EAZeb454eEuOFBzf5B9D+3LoCErSYWNRZ1EQvIruA7wH3G2MGF/O7lVKR4/MZ/nCxk2qXmzfqWxgZ97GxJJP/7Yubeey2FeRrMbGotpDulS8CO4ACEfEAf4W/l00ycNi64XLEGPOdRYhTKRUB7q5BXj7qocblobF7iKyUBPY7Hex3OqgozdIbq0vEQnrdfHWayf+8gFiUUlFgeMzLmydbqHa5+eB8JyJw79oCvrfLX0wsVgfQjmV6K1wphTGGE54eamr9xcT6hsex56by7x5cz+NVpdhztZjYUqaJXqllrLN/hF9+2kiNy0NDax/JCXGBAbTvXqXFxGKFJnqllplxr4/3z7VT/YmH35xuZdxn2ObI4W++XMGj21aQtUwH0I5lmuiVWiYutPdT4/IPoN3WN0J+ehLf+Gw5+5wO1hdnRjo8FUKa6JWKYf0j4/z6RBPVLg+1V64RHyc8sKGQfU4HOzcW6QDay4QmeqVijDGGTy5fo9rl5tcnmhka87KmMJ0f7t7Il7eXUpSpxcSWG030SsWIlp5hq8+7m8udg6QnxbP3thXsczrYXpajfd6XMU30Si1hI+Ne3j7dRrXLzftn2/EZuGtVHt/duY7dlSWkJel/cbXEE/21gVEGRscpyEjWhzjUsnIqMIB2I9cGxyjJSuGPd6zliSo75QXpkQ5PRZklnehrat38HwfPAJCdmkhhZjJFmclTfqdQGDQtOzVRL2HVktQ9OMprx5uodrmpb+wlKT6Oh7b4i4ndu7aAeO3zrm5iSSf6BzYUkZ2aSHvfCG19I4Hfn17tpq1vmOEx3w2fSYqPozAzmYJpDgqFGckUZaUEXuvwZirSvD7DB+c7qHa5eetUK6PjPrasyOKvH93M3ttKyU1PinSIaglY0ol+XXEm627S/9cYQ//I+A0HAf/vYdr7RnB3DXL0yjU6B0an/Y6ctMQbrgwmHxSSKcxIISs1Qa8S1KK62jnIgVo3B2o9NPUMk52ayL++s4wnquxUlGZHOjy1xCzpRD8TESEzJZHMlMRZB0AY8/ro7B+ddBCYelBwXemirXeEkfFprhIS4oIS//UDwKQmpKxkCjKStd+yuqmhUS+H6v0DaP/hor+Y2H3rCvnRI5t4cFOx3odStyxmE/18JMbHUZKdQkl2CnDzsyVjDH0j47T1Tj4ItAddMVzpHMR15RpdN7lKyEtPmnRQKJz4HXzVkJVMZrJeJSwHxhiOubupdnl4/XgTfSPjlOWl8R++sJ6vbLezIic10iGqGKCJfh5EhKyURLJSEllbNPNVwui4j86BkaCDwpSDQ/8IF9sHaO8fYXSaq4SUxLjrTURTm42CDgoFGUk6os8S1N43wi8/9VDt8nC+rZ/UxPhAMbE7y/O0mJhaVJroQyQpIQ5bdiq27JnPyIwx9A6N094/7D8o9I8E/R72HxA6+jlyqZPuwbEbPi8CeWlJNxwApuuBlKFXCRE15vXx7pk2amo9vHOmDa/PsL0sh2e/UskjW21kajExFSKa6CNMRMhOSyQ7LZG1RTMXlhoZ99IxcS/BOghMHBQmrhoutnfS3jfCqPfGq4TUxPhpDwBTbzbnpetVwmI619pHTa2/mFhH/ygFGcl8695V7HPaZ/2bK7UYNNEvIckJ8ZTmpFI6S7utMYaeobEbmouCDwrn2vr58EInPUM3XiXECeSlT39VMPWgkJ6su9B0+obHeP1EM9UuN59e7SYhTti5sYj9Tgf3byjUm/IqrPR/aQwSEXLSkshJS5q1/OzwmJeO/hu7oPqvVs7yAAAY8UlEQVR//AeIs619tPeNMO4zN3w+LSl+2ofTpjYf5acnx/wDPT6f4aNLXdS43Bysb2Z4zMe6ogx+vGcTj91eSmGmDqCtIkMT/TKXkhiPPTdt1qHifD5D99DYtF1QJ6adaenl/XMj9A2P3/D5OIH8jJkfUpuYttTqszR1D/FyrYeaWg9XuwbJTE7gK9vt7Hc62GbP1vsiKuIW9D9KRJ4Hvgi0GWMqrGl5wEtAOXAZ2G+MubawMFWkxcUJeelJ5KUnsaFk9quE6weC4UndTyd+n2nuo71/BO80VwkZyQnTXhUEDgpW99S8tKSI9U4ZHvNy+FQr1S43vz/fgTFwz5p8/t1D69i1xUZqkvZ5V9FDjLnxP9qcPyxyH9APvBCU6P8O6DLGPCsiPwByjTHfn+l7nE6ncblctxyHWpp8PsO1wdFpm42mPqPQN3LjVUJ8nFCQkXS92Sjj+sNpwQ+uFWUtXtG7+sYealxuXjnWRM/QGKU5qTxeZWdflR1Hng6grcJLRGqNMc7ZllvQGb0x5n0RKZ8yeS+ww3r9M+A9YMZEr5anuDghPyOZ/IxkNtlmXnZo1HvDcwjBD6619Q1T39hDR/8I01wkkJmcMP3DaZmTDw6501wlXBsY5ZVjjVS7PJxu7iUpIY5dW0rY73RwzxodQFtFv1A0hhYbY5qt1y1AcQjWoZaZ1KR4yvLTKMuf+azZ6zN0DYxOf1DoH6G9d4STTb2829vGwKj3hs8nxAkFQU8uA/zuXAejXh+Vpdn8571b+NK2UrLTtM+7WjpCetfLGGNEZNq2IRF5BngGoKysLJRhqGUkPk4CbfubyZpx2QGr6F3gOYQpzyY09wwzODrO1+9eyT6nnU22mb9PqWgVikTfKiI2Y0yziNiAtukWMsY8BzwH/jb6EMSh1IzSkxNIT07QgTpUzAvFUxuvAU9Zr58CXg3BOpRSSs3RQnvdvIj/xmsB0Ar8FfAKUA2UAVfwd6/smuV72q1lb0UB0HGLnw0ljWt+NK75i9bYNK75WUhcK40xhbMttKBEHw1ExDWX7kXhpnHNj8Y1f9Eam8Y1P+GISwtuKKVUjNNEr5RSMS4WEv1zkQ7gJjSu+dG45i9aY9O45ifkcS35NnqllFIzi4UzeqWUUjOI6kQvIrtEpEFEzlsF0qbOTxaRl6z5HwXX3RGRH1rTG0Tk4TDH9ecickpETojI2yKyMmieV0SOWT+vhTmup0WkPWj93wqa95SInLN+npr62RDH9fdBMZ0Vke6geaHcXs+LSJuI1N9kvojIP1hxnxCR7UHzQrK95hDT16xY6kTkQxHZFjTvsjX9mIgsepXAOcS2Q0R6gv5efxk0b8Z9IMRx/UVQTPXWPpVnzQvJNhMRh4i8a+WBkyLyp9MsE779yxgTlT9APHABWA0kAceBzVOW+WPgn6zXTwIvWa83W8snA6us74kPY1wPAGnW6z+aiMt63x/B7fU08JNpPpsHXLR+51qvc8MV15Tlvws8H+rtZX33fcB2oP4m8/cAhwAB7gY+CsP2mi2meybWBeyeiMl6fxkoiOD22gG8vtB9YLHjmrLso8A7od5mgA3Ybr3OBM5O8/8xbPtXNJ/R3wmcN8ZcNMaMAj/HXxkz2F78FTIBDgCfFxGxpv/cGDNijLkEnLe+LyxxGWPeNcYMWm+PAPZFWveC4prBw8BhY0yX8Y8dcBjYFaG4vgq8uEjrnpEx5n1gpof59uIvwW2MMUeAHPGX9QjZ9potJmPMh+b6+A7h2rcm1j3b9rqZheybix1XWPYvY0yzMeao9boPOA2UTlksbPtXNCf6UsAd9N7DjRsqsIwxZhzoAfLn+NlQxhXsm/iP2hNSRMQlIkdE5LFFimk+cT1uXSYeEBHHPD8byriwmrhWAe8ETQ7V9pqLm8Ueyu01H1P3LQO8JSK14i8aGAmfEZHjInJIRLZY06Jie4lIGv6E+XLQ5JBvM/E3Kd8OfDRlVtj2r6U1ZtsSIyJfB5zA/UGTVxpjGkVkNfCOiNQZYy6EKaRfAS8aY0ZE5Nv4r4Z2hmndc/EkcMAYE1w/OJLbK2qJyAP4E/29QZPvtbZVEXBYRM5YZ7vhchT/36tfRPbgL4eyLozrn82jwAdmckmWkG4zEcnAf2D5M2NM72J973xF8xl9I+AIem+3pk27jIgkANlA5xw/G8q4EJEHgR8DXzLGjExMN8Y0Wr8v4h+U5fZwxWWM6QyK5adA1Vw/G8q4gjzJlMvqEG6vubhZ7KHcXrMSka34/357jTGdE9ODtlUb8EsWr7lyTowxvcaYfuv1QSBRRAqI8PYKMtP+tejbTEQS8Sf5fzHG/GKaRcK2f83aj17mMS6s1T7+3/DfZBgEnp5op5pJQUGBKS8vv/V/hVJKLUO1tbUdZg5FzebSdPM/gZ8ALwRN+wHwtrk+LuwP8A8XuBv/pdo64C7g/7F+z6i8vJxbGTN2eMxLQpyQEB/NFyZKKRUaIjKnqr+zJnozv3FhA3eRgSMikiPWICRzC3t+fv7xVf72jQa2rMhiqz2HbY5sttlzWJmfhv/iQiml1K3ejL3ZuLA3u1t8Q6KXRRhKsNKezZN3Ojjh6eFfPrrC8x/4AMhOTWSrPdv6yeE2Rw7FWSm3tA6llFrqFtzrxpibjws7y+cWPJRg1co8qlbmATDm9XG2tY8Tnh5OeLo57u7hn357Ea/P/9XFWcn+s34r+W+1Z5OTlnQrq1VKqSXlVhP9zcaFjdjd9cT4OLasyGbLimy+eqf/CmF4zMvJpl6Ou7s54enmhKeHw6daA59ZmZ/GNivpb3PksGVFFmlJ2uNUKRVbbjWrTYwL+yyTx4V9DfgTEfk5/puwPaFqn5+LlMR4qlbmUrUyNzCtZ2iM+sYejnu6OeHu4ZPLXbx2vAmAOIH1xZmBxL/NnsOGkkwS9WavUmoJm0v3yjmPC2t1r/wJ/qfPBoFvGGNm7U7jdDrNrfS6WSxtfcOccFtNPlbTz7XBMQCSEuLYbMsKNPlsc2SzuiCDuDi92auUiiwRqTVzGIYwKurRRzrRT2WMwd015D/rt5J/fWMPg6P+BzYzkxOoKM1mq9XLZ6s9m9KcVO3po5QKq7kmem2QnoaIUJafRll+Go9uWwGA12e40N7PcXe3dQDo4fnfX2LM6z9Q5qcnTWry2WrPJj8jOZL/DKWUAjTRz1l8nLC+OJP1xZnsc/rvN4+MeznT3Bc46z/u7ua9s+1MXCSV5qSyzXG9l09laTaZKYkR/FcopZYjTfQLkJwQ7z+Dd+Twv1jT+kfGqW+c3N5/sK4FABFYU5jhP/O3kv8mWxYpifGR+0copWKeJvpFlpGcwN2r87l7dX5gWtfAaKCXzwlPN++f7eAXR/29ThPjhQ0lmWyzW00+jmzWFWUSrzd7lVKLRG/GRoAxhuaeYU54ujlmJf86Tw99I+MApCbGU1GaFWjyuc2RQ1melnVQSk2mN2OjmIiwIieVFTmp7KqwAeDzGS51DgSe6j3u6eb/PXKF0XF/WYectEQqS7MnPeClZR2UUnOhZ/RRbMzro6ElqKyDp4ezrX2Bsg4lWSmBpL/Vns3W0hyy0/Rmr1LLhfajj1FDo15ONvUEbvSe8PRwqWMgML88P816sMtf12fLimxSk/Rmr1KxSJtuYlRqUjzO8jyc5XmBaT2DY9RZZR2Ou7v5+NL1sg7xccK6oozAjV4t66DU8qNn9DGqrXc4cNZ/zO0/8+8Z8pd1SE6IY/OKrEB7/1Z7DqsL0rWsg1JLjDbdqEmMMVztGvQnfyvx1zX2MDR2vaxD5UQ9H3s2Wx05rMhO0Z4+SkUxbbpRk4gIK/PTWZmfzpessg7jXh/n2/s5YfXyOeHp4Z9/fzFQ1qEgIynQxXOitENeutbwV2qp0US/jCXEx7GxJIuNJVnsv8Nf1mF4zMuZlr5JTT7vNrQFyjrYc1MnNflU2rPJSNbdSKlopk03alZ9w2PUN/YGevkcc3fT2D0E+Ms6rC3MCJRw3mrPYZMtk+QE7emjVKhp041aNJkpiXxmTT6fWXO9rENn/wgnPNebfH57to2Xj3oAf1mHjSVZgcS/zZ7D2qIMLeugVIToGb1aFMYYmnqGr5dxdvtv9vZbZR3SkuKpWGEN2O7I4TZ7Do48reGv1EJorxsVcT6f4WLHRFkH/5O9p5p7A2UdctMSqQwasH2bPZsiLeug1Jxp042KuLg4YW1RBmuLMvjKdjsAo+M+zrb2Bc76j3u6+cd327GqOmDLTgnc6N1m3ezNTtWyDkothCZ6FVZJCXFUlGZTUZrN1+7yTxscHedkUy/HrV4+JzzdvHmyNfCZVQXpgRr+2xz+sg5aw1+pudNEryIuLSmBO8rzuCOorEP34OikYm5HLnby6rHrZR3WF2dOGrB9fbGWdVDqZrSNXi0Zrb3Dk8bsnVrWYcuKrEndPFfla1kHFdv0ZqyKecYYrnQOBhL/cXc39U09DI/5b/ZmpiQEtff7f9u0rIOKIWG5GSsil4E+wAuMG2OcIpIHvASUA5eB/caYawtZj1LTERHKC9IpL0hn722lgL+sw7m2/klj9v6P9y8y7pso65DMtqAa/tvsOeRqWQcV4xZ0Rm8leqcxpiNo2t8BXcaYZ0XkB0CuMeb7M32PntGrUBoe83KquTdQzO24p5uLHQOBsg6OvNRJZ/2Vpdmka1kHtQREsnvlXmCH9fpnwHvAjIleqVBKSYxne1ku28tyA9P6hv01/CeafI5d7ebXJ5oBiBNYW5QRSP4bbVmsL87Ubp5qyVroGf0l4BpggP9ujHlORLqNMTnWfAGuTby/GT2jV9Ggo38kMGbvRNNP18BoYL4tO4UNJZlsKM70/y7JZE1hhnb1VBETrjP6e40xjSJSBBwWkTPBM40xRkSmPZKIyDPAMwBlZWULDEOphSvISGbnxmJ2biwGrpd1aGjp5UxLH2db+jjT0scH5zsCpZzj44Ty/DQ2lvjP+icOAGV5aVrbR0WNRet1IyJ/DfQD/yuwwxjTLCI24D1jzIaZPqtn9GopGfP6uNwx4E/+rf7k39DSx9WuwcAyKYlxrC/OZH1xJhut5L+hOJPCzGTt9aMWTcjP6EUkHYgzxvRZr78A/CfgNeAp4Fnr96u3ug6lolFifBzrijNZV5w5afrAyDjn2vppaOmloaWfhtZe3mto40CtJ7BMblpiUPNPFhtKMllfnEFmirb/q9BZSNNNMfBL6+wkAfj/jDFviMgnQLWIfBO4AuxfeJhKRb/05ARuc+Rwm2PyLamO/pFAs8/EFUBNrYfBUW9gmdKcVDaWZLK+5PoVwOqCDJIS9GlftXD6wJRSEeDzGRq7h6Y0//RysX0g0Oc/IU5YXZge1PyTxYbiTOy5qfrErwK0eqVSUS0uTnDkpeHIS+OhzcWB6aPjPi529NNgtfs3tPTx6dVuXre6fgKkJ8Wzrvh675+JK4GCjORI/FPUEqCJXqkokpRwfRzfYH3DY5xt9R8A/FcAvbx1qoWXXO7AMgUZSVabv5X8rZvB+vCX0j1AqSUgMyWRqpW5VK28/tCXMYb2/pFJZ/8NrX28+PHVQL0fgLK8tMm9f0oyWVWQrtU+lxFN9EotUSJCUWYKRZkpfG5dYWC612dwdw0G2v8bWvxXAO82tOG12v+T4uNYXZgeSPwTVwClOTq8YyzSRK9UjImPu17sbVdFSWD68JiXC+39gTP/hpY+Pr7UFajzD5CZnMD6khv7/2vht6VNE71Sy0RKYjxbVvhH6ArWMzQWOPOf+Pn1iSZe/Hg8sExRZvIN5R/WFWWSmqTlH5YCTfRKLXPZqYk3jPBljKG1d4QzLb2Tnv594ciVwODuIlCen8764gw2lGQFmn/K89NI0Pb/qKKJXil1AxGhJDuFkuwUdmwoCkz3+gyXOwcmnf2fbe3j8KnWwADvSQlxrCvKmHT2v6Ekk5IsHfQlUvSBKaXUgg2PeTnX2m+1/fcGbgS39o4ElslKSQhK/FmBA4GWf751+sCUUipsUhLjqbRnU2mf3P5/bWCUhtbJxd9e/bSJvpGrgWVs2SmTbv6uL85kbZGWf15MmuiVUiGTm57E3avzuXt1fmBacPnnhpb+wBXAhxduLP/svwGcpeWfF0gTvVIqrESE0pxUSnNSA7X/4Xr554ZA3/8+Tjb1cqi+JTDs49TyzxO/tfzzzLSNXikV1QZHxznb2h+oANrQ6r8S6Oi/3v6fm5Y4ufhbSQbrizNjvvyzttErpWJCWtL05Z87J8o/tF4v/3Cg1sPAlPLPU5/+XVO4/Mo/a6JXSi1J+RnJ3LM2mXvWFgSmTZR/nkj8E+Wf3z/bPqn886qC9EnJf2NJVkyXf9ZEr5SKGcHlnx+cpfzzMffk8s9pVvnnjVP6/8dC+WdN9EqpmDdb+efg4m/TlX9eXzy5+WeplX9eOpEqpdQim2v557Otffz8YzdDY9fb/5dS+WdN9EopFeRm5Z99PsPVrsHrN3+t+wDB5Z8T44U1hRnXm36sK4FIl3/WRK+UUnMQF1T++eEtN5Z/Dn7695Mp5Z8zkhNuKP62sSR85Z810Sul1AIstPzzj/Zs4rHbS0MaoyZ6pZQKgZnKPwcXfyvJTgl5LJrolVIqTILLP9+/vnD2DyyS6Ls9rJRSalFFRa0bEWkHrtzixwuAjkUMZ7FoXPOjcc1ftMamcc3PQuJaaYyZ9dIgKhL9QoiIay5FfcJN45ofjWv+ojU2jWt+whGXNt0opVSM00SvlFIxLhYS/XORDuAmNK750bjmL1pj07jmJ+RxLfk2eqWUUjOLhTN6pZRSM4jqRC8iu0SkQUTOi8gPppmfLCIvWfM/EpHyoHk/tKY3iMjDYY7rz0XklIicEJG3RWRl0DyviByzfl4Lc1xPi0h70Pq/FTTvKRE5Z/08Fea4/j4oprMi0h00L5Tb63kRaROR+pvMFxH5ByvuEyKyPWheSLbXHGL6mhVLnYh8KCLbguZdtqYfE5FFH5tzDrHtEJGeoL/XXwbNm3EfCHFcfxEUU721T+VZ80KyzUTEISLvWnngpIj86TTLhG//MsZE5Q8QD1wAVgNJwHFg85Rl/hj4J+v1k8BL1uvN1vLJwCrre+LDGNcDQJr1+o8m4rLe90dwez0N/GSaz+YBF63fudbr3HDFNWX57wLPh3p7Wd99H7AdqL/J/D3AIUCAu4GPwrC9Zovpnol1AbsnYrLeXwYKIri9dgCvL3QfWOy4piz7KPBOqLcZYAO2W68zgbPT/H8M2/4VzWf0dwLnjTEXjTGjwM+BvVOW2Qv8zHp9APi8iIg1/efGmBFjzCXgvPV9YYnLGPOuMWbQensEsC/SuhcU1wweBg4bY7qMMdeAw8CuCMX1VeDFRVr3jIwx7wNdMyyyF3jB+B0BckTERgi312wxGWM+tNYJ4du3JtY92/a6mYXsm4sdV1j2L2NMszHmqPW6DzgNTK1cFrb9K5oTfSngDnrv4cYNFVjGGDMO9AD5c/xsKOMK9k38R+0JKSLiEpEjIvLYIsU0n7gety4TD4iIY56fDWVcWE1cq4B3giaHanvNxc1iD+X2mo+p+5YB3hKRWhF5JgLxAHxGRI6LyCER2WJNi4rtJSJp+BPmy0GTQ77NxN+kfDvw0ZRZYdu/tKhZCInI1wEncH/Q5JXGmEYRWQ28IyJ1xpgLYQrpV8CLxpgREfk2/quhnWFa91w8CRwwxniDpkVye0UtEXkAf6K/N2jyvda2KgIOi8gZ62w3XI7i/3v1i8ge4BVgXRjXP5tHgQ+MMcFn/yHdZiKSgf/A8mfGmN7F+t75iuYz+kbAEfTebk2bdhkRSQCygc45fjaUcSEiDwI/Br5kjBmZmG6MabR+XwTew3+kD0tcxpjOoFh+ClTN9bOhjCvIk0y5rA7h9pqLm8Ueyu01KxHZiv/vt9cY0zkxPWhbtQG/ZPGaK+fEGNNrjOm3Xh8EEkWkgAhvryAz7V+Lvs1EJBF/kv8XY8wvplkkfPvXYt+EWKwf/FcbF/Ffyk/cwNkyZZl/y+SbsdXW6y1Mvhl7kcW7GTuXuG7Hf/Np3ZTpuUCy9boAOMci3ZSaY1y2oNdfBo6Y6zd/Llnx5Vqv88IVl7XcRvw3xiQc2ytoHeXc/ObiI0y+WfZxqLfXHGIqw3/P6Z4p09OBzKDXHwK7FnNbzSG2kom/H/6EedXadnPaB0IVlzU/G387fno4tpn1734B+L9nWCZs+9ei7gQh2Kn24L9bfQH4sTXtP+E/SwZIAWqsHf9jYHXQZ39sfa4B2B3muH4DtALHrJ/XrOn3AHXWjl4HfDPMcf2fwElr/e8CG4M++2+s7Xge+EY447Le/zXw7JTPhXp7vQg0A2P420G/CXwH+I41X4B/tOKuA5yh3l5ziOmnwLWgfctlTV9tbafj1t/4x4u5reYY258E7V9HCDoYTbcPhCsua5mn8XfQCP5cyLYZ/iY1A5wI+lvtidT+pU/GKqVUjIvmNnqllFKLQBO9UkrFOE30SikV4zTRK6VUjNNEr5RSMU4TvVJKxThN9EopFeM00SulVIz7/wEAuF/EdZOoOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(311)\n",
    "plt.plot([4, 22, 1])\n",
    "plt.subplot(312)\n",
    "plt.plot([11, 10, 14])\n",
    "plt.subplot(313)\n",
    "plt.plot([101, 51, 9])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(221)\n",
    "plt.plot([55, 44, 33, 22])\n",
    "plt.subplot(222)\n",
    "plt.plot([1, 22, 3, 44])\n",
    "plt.subplot(224)\n",
    "plt.plot([1, 2, 3, 4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot([1,2,3,4])\n",
    "plt.subplot(212)\n",
    "plt.plot([3, 5, 9, 12])\n",
    "plt.title('My 1st Figure')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot([4, 1, 3])\n",
    "plt.title('My 2nd Figure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot([0,1,2,3,4], [.2, 50, 900, 12, 3000])\n",
    "plt.ylim(-500, 3500)\n",
    "plt.xlim(0, 10)\n",
    "plt.xticks([0,1,2,3,4,5,9,10], rotation = 70)\n",
    "plt.yticks([-10, 900, 2000], ['Low', 'Normal', 'High'], rotation = 'vertical')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([10, 100, 1000, 10000])\n",
    "plt.yscale('log')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Pandas and MatplotLib\n",
    "\n",
    "As in [ADM Introductory Laboratory](../lab-intro/ADM%20Lab%201%20-%20Intro.ipynb) we work with the data provided by the [Open Data portal of the City of Rome](http://dati.comune.roma.it/). We use the dataset related to registered hosting structures, that is, hotels, hostels, B&B, etc., that were active during January 2019. For information on how to import the data from these datasets into a data structure that will support further studies, check out the [ADM Introductory Laboratory](../lab-intro/ADM%20Lab%201%20-%20Intro.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"../data/opendata_suar_gennaio.csv\",\n",
    "                     sep=',', delimiter = None,\n",
    "                     header='infer', names=None,\n",
    "                     index_col = None, usecols = None,\n",
    "                     encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Insegna', 'Classificazione', 'Indirizzo', 'Municipio', 'Tipologia',\n",
       "       'Singole', 'Doppie', 'Triple', 'Quadruple', 'Quintuple', 'Sestuple',\n",
       "       'Unitaâ Abitative', 'Posti Letto Unitaâ Abitative', 'Sito Web',\n",
       "       'Email', 'Telefono', 'Fax', 'Cellulare', 'Contatto Facebook',\n",
       "       'Contatto Twitter', 'Contatto Instagram', 'Contatto Altro Social',\n",
       "       'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns=['Unitaâ Abitative', 'Posti Letto Unitaâ Abitative', 'Sito Web',\n",
    "                                'Email', 'Telefono', 'Fax', 'Cellulare', 'Contatto Facebook',\n",
    "                                'Contatto Twitter', 'Contatto Instagram', 'Contatto Altro Social', \n",
    "                                'Unnamed: 22', 'Unnamed: 23', 'Unnamed: 24'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Address'] = dataset['Indirizzo'] + \" \" + dataset['Municipio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset.drop(columns=['Indirizzo', 'Municipio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Insegna</th>\n",
       "      <th>Classificazione</th>\n",
       "      <th>Tipologia</th>\n",
       "      <th>Singole</th>\n",
       "      <th>Doppie</th>\n",
       "      <th>Triple</th>\n",
       "      <th>Quadruple</th>\n",
       "      <th>Quintuple</th>\n",
       "      <th>Sestuple</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Casa e Appartamento per Vacanze \"Trastevere_ho...</td>\n",
       "      <td>Categoria 2</td>\n",
       "      <td>Mun. XII</td>\n",
       "      <td>Casa Vacanze NON impr (Appartamento)</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VIA FEDERICO ROSAZZA 52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa e Appartamento per Vacanze \"VIVES 63\"</td>\n",
       "      <td>Categoria 1</td>\n",
       "      <td>Mun. XII</td>\n",
       "      <td>Casa Vacanze NON impr (Appartamento)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VIA DI MONTE VERDE 63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASA E APPARTAMENTO PER VACANZE \"LA CASA DI FA...</td>\n",
       "      <td>Categoria 2</td>\n",
       "      <td>Mun. I</td>\n",
       "      <td>Casa Vacanze NON impr (Appartamento)</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VIA AUGUSTO AUBRY 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>casa e appartamento per vacanze Valentina's house</td>\n",
       "      <td>Categoria 2</td>\n",
       "      <td>Mun. I</td>\n",
       "      <td>Casa Vacanze NON impr (Appartamento)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VIALE GIULIO CESARE  51/A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OLD CITY TESTACCIO CASA E APPARTAMENTO PER VAC...</td>\n",
       "      <td>Categoria 2</td>\n",
       "      <td>Mun. I</td>\n",
       "      <td>Casa Vacanze NON impr (Appartamento)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VIA ORAZIO ANTINORI 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Insegna Classificazione  \\\n",
       "0  Casa e Appartamento per Vacanze \"Trastevere_ho...     Categoria 2   \n",
       "1         Casa e Appartamento per Vacanze \"VIVES 63\"     Categoria 1   \n",
       "2  CASA E APPARTAMENTO PER VACANZE \"LA CASA DI FA...     Categoria 2   \n",
       "3  casa e appartamento per vacanze Valentina's house     Categoria 2   \n",
       "4  OLD CITY TESTACCIO CASA E APPARTAMENTO PER VAC...     Categoria 2   \n",
       "\n",
       "   Tipologia                               Singole Doppie Triple Quadruple  \\\n",
       "0  Mun. XII   Casa Vacanze NON impr (Appartamento)      1    NaN       NaN   \n",
       "1  Mun. XII   Casa Vacanze NON impr (Appartamento)    NaN      1       NaN   \n",
       "2    Mun. I   Casa Vacanze NON impr (Appartamento)      1    NaN       NaN   \n",
       "3    Mun. I   Casa Vacanze NON impr (Appartamento)    NaN      2       NaN   \n",
       "4    Mun. I   Casa Vacanze NON impr (Appartamento)      1      1       NaN   \n",
       "\n",
       "   Quintuple  Sestuple                    Address  \n",
       "0        NaN       NaN    VIA FEDERICO ROSAZZA 52  \n",
       "1        NaN       NaN      VIA DI MONTE VERDE 63  \n",
       "2        NaN       NaN        VIA AUGUSTO AUBRY 1  \n",
       "3        NaN       NaN  VIALE GIULIO CESARE  51/A  \n",
       "4        NaN       NaN      VIA ORAZIO ANTINORI 2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12137, 10)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2518"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1135"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Triple'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3222"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Quadruple'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['Singole', 'Doppie', 'Triple', 'Quadruple', 'Quintuple', 'Sestuple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Singole 0\n",
      "Doppie 2518\n",
      "Triple 1135\n",
      "Quadruple 3222\n",
      "Quintuple 3578\n",
      "Sestuple 3660\n"
     ]
    }
   ],
   "source": [
    "for col in cols:\n",
    "    print(col, dataset[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Singole 0\n",
      "Doppie 0\n",
      "Triple 0\n",
      "Quadruple 0\n",
      "Quintuple 0\n",
      "Sestuple 0\n"
     ]
    }
   ],
   "source": [
    "for col in cols:\n",
    "    dataset[col] = dataset[col].fillna(0)\n",
    "    print(col, dataset[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "must be str, not int",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-b65dd45e8795>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Doppie'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mstat_func\u001b[0;34m(self, axis, skipna, level, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m   9591\u001b[0m                                       skipna=skipna, min_count=min_count)\n\u001b[1;32m   9592\u001b[0m         return self._reduce(f, name, axis=axis, skipna=skipna,\n\u001b[0;32m-> 9593\u001b[0;31m                             numeric_only=numeric_only, min_count=min_count)\n\u001b[0m\u001b[1;32m   9594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9595\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mset_function_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstat_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   3219\u001b[0m                                           'numeric_only.'.format(name))\n\u001b[1;32m   3220\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3221\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3223\u001b[0m         return delegate._reduce(op=op, name=name, axis=axis, skipna=skipna,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     75\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m                 \u001b[0;31m# we want to transform an object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnansum\u001b[0;34m(values, axis, skipna, min_count)\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mis_timedelta64_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    335\u001b[0m         \u001b[0mdtype_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 336\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    337\u001b[0m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_null_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial)\u001b[0m\n\u001b[1;32m     34\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     35\u001b[0m          initial=_NoValue):\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
      "\u001b[0;31mTypeError\u001b[0m: must be str, not int"
     ]
    }
   ],
   "source": [
    "dataset['Doppie'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Unable to parse string \"Casa Vacanze NON impr (Appartamento)\" at position 1059",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"Casa Vacanze NON impr (Appartamento)\"",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-831568900bf2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_series\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Doppie'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast)\u001b[0m\n\u001b[1;32m    131\u001b[0m             \u001b[0mcoerce_numeric\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'raise'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m             values = lib.maybe_convert_numeric(values, set(),\n\u001b[0;32m--> 133\u001b[0;31m                                                coerce_numeric=coerce_numeric)\n\u001b[0m\u001b[1;32m    134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/src/inference.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.maybe_convert_numeric\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unable to parse string \"Casa Vacanze NON impr (Appartamento)\" at position 1059"
     ]
    }
   ],
   "source": [
    "new_series = pd.to_numeric(dataset['Doppie'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Casa Vacanze NON impr (Appartamento)'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'][1059]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['Doppie'] = pd.to_numeric(dataset['Doppie'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'][1059]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16404.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "415.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11237"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Doppie'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Insegna             ERGIFE  PALACE\n",
       "Classificazione           4 Stelle\n",
       "Tipologia               Mun. XIII \n",
       "Singole                    Albergo\n",
       "Doppie                         415\n",
       "Triple                         289\n",
       "Quadruple                        0\n",
       "Quintuple                        0\n",
       "Sestuple                         0\n",
       "Address            VIA AURELIA 619\n",
       "Name: 11237, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.loc[11237]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3f099cf8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAFZNJREFUeJzt3X2Q3VWd5/H3N+k885AHAsYkkrCmRKZqRjGLsM4wFhkV0QW2Fhwca4zKyNSsuzq6WyMMOztjrTUrOitCaYmMuKIDCsM4QiGjgwEdkCHQDBAgIabDQx4M6c7zE510Omf/uCfhptOdvt19b9++x/erqqt/v/M7995z7rn96d8993d/v0gpIUkq17hmN0CS1FgGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwbc1uAMApp5ySFixY0OxmSFJLeeKJJ7aklGYPVm9MBP2CBQtob29vdjMkqaVExMu11HPqRpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0knQcHZ27efSFrc1uxoiMiS9MSdJY9Xtf/hcAXvrC+5rckuFzj16SCmfQS1LhDHpJKlzNQR8R4yPiyYi4N68vjIjlEdEREXdExMRcPimvd+TtCxrTdElSLYayR/8pYFXV+nXA9SmlNwLbgStz+ZXA9lx+fa4nSWqSmoI+IuYB7wO+mdcDuAC4K1e5Fbg0L1+S18nbl+T6kqQmqHWP/ivAnwGH8vosYEdK6WBe3wDMzctzgfUAefvOXP8oEXFVRLRHRHtXV9cwmy9JGsygQR8R7wc6U0pP1POBU0o3p5QWp5QWz5496AVSJEnDVMsXpt4BXBwRFwGTgZOAG4DpEdGW99rnARtz/Y3AfGBDRLQBJwOt/bUySWphg+7Rp5SuSSnNSyktAK4AHkgpfQh4ELgsV1sK3J2X78nr5O0PpJRSXVstSarZSI6j/yzwmYjooDIHf0suvwWYlcs/A1w9siZKkkZiSOe6SSn9DPhZXn4BOKefOt3A5XVomySpDvxmrCQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkq3KBBHxGTI+KxiHg6Ip6LiM/l8oURsTwiOiLijoiYmMsn5fWOvH1BY7sgSTqeWvbo9wMXpJR+C3gLcGFEnAtcB1yfUnojsB24Mte/Etiey6/P9SRJTTJo0KeKPXl1Qv5JwAXAXbn8VuDSvHxJXidvXxIRUbcWS5KGpKY5+ogYHxFPAZ3A/cBaYEdK6WCusgGYm5fnAusB8vadwKx6NlqSVLuagj6l1JtSegswDzgHOHOkDxwRV0VEe0S0d3V1jfTuJEkDGNJRNymlHcCDwHnA9Ihoy5vmARvz8kZgPkDefjKwtZ/7ujmltDiltHj27NnDbL4kaTC1HHUzOyKm5+UpwLuAVVQC/7JcbSlwd16+J6+Ttz+QUkr1bLQkqXZtg1dhDnBrRIyn8o/hzpTSvRGxEvh+RHweeBK4Jde/BfhuRHQA24ArGtBuSVKNBg36lNIK4K39lL9AZb6+b3k3cHldWidJGjG/GStJhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhBg36iJgfEQ9GxMqIeC4iPpXLZ0bE/RGxJv+ekcsjIm6MiI6IWBERZze6E5KkgdWyR38Q+O8ppbOAc4FPRMRZwNXAspTSImBZXgd4L7Ao/1wFfL3urZYk1WzQoE8pbUop/Vte3g2sAuYClwC35mq3Apfm5UuA76SKR4HpETGn7i2XJNVkSHP0EbEAeCuwHDgtpbQpb3oFOC0vzwXWV91sQy6TJDVBzUEfEScA/wD8aUppV/W2lFIC0lAeOCKuioj2iGjv6uoayk0lSUNQU9BHxAQqIX9bSukHuXjz4SmZ/Lszl28E5lfdfF4uO0pK6eaU0uKU0uLZs2cPt/2SpEHUctRNALcAq1JKX67adA+wNC8vBe6uKv9wPvrmXGBn1RSPJGmUtdVQ5x3AHwLPRMRTuezPgS8Ad0bElcDLwAfytvuAi4AOYB/w0bq2WJI0JIMGfUrpYSAG2Lykn/oJ+MQI2yVJqhO/GStJhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgpn0EtS4Qx6SSqcQS9JhTPoJalwBr0kFc6gl6TCGfSSVDiDXpIKZ9BLUuEMekkqnEEvSYUz6CWpcAa9JBXOoJekwhn0klQ4g16SCmfQS1LhBg36iPhWRHRGxLNVZTMj4v6IWJN/z8jlERE3RkRHRKyIiLMb2XhJ0uBq2aP/NnBhn7KrgWUppUXAsrwO8F5gUf65Cvh6fZopSRquQYM+pfQvwLY+xZcAt+blW4FLq8q/kyoeBaZHxJx6NVaSNHTDnaM/LaW0KS+/ApyWl+cC66vqbchlx4iIqyKiPSLau7q6htkMqfG27tnPj1ZsGryiNEaN+MPYlFIC0jBud3NKaXFKafHs2bNH2gypYT7+nXY+cfu/sWXP/mY3RRqW4Qb95sNTMvl3Zy7fCMyvqjcvl0kta+OOVwE42Dvk/RlpTBhu0N8DLM3LS4G7q8o/nI++ORfYWTXFI7W0NPQ3rtKY0DZYhYj4HvBO4JSI2AD8JfAF4M6IuBJ4GfhArn4fcBHQAewDPtqANkujKohmN0EakUGDPqX0wQE2LemnbgI+MdJGSWNRcodeLcpvxkqDCHfo1eIMekkapq7d++no3D3k21334+e5cdmaBrSof4NO3UiS+vc7X3yA7p5DvPSF9w3pdl//2VoAPrlkUSOadQz36KUaOUWvvrp7DjW7CTUx6KVBOEWvVmfQS1UuuuEhLv3aL/rdljzsRjV49/U/5/KbHml2M47iHL1UZeWmXceUhYfdaAh+uXlPs5twDPfopeM4dCixu7sH8Dh6tS6DXjqOr/z0l+zqPtjsZqhQm3d1j8rjGPTScdzr6YmVbd97oO73+fa/XsYzG3bW/X77MuglqQY3NOgLTmu7Gj+nb9BLx+PnsCqAR91IA7jka7/gha69zW6GmujvHn35yHI9Dq/95kMv8IuOLSO+n6Fyj14awNPrdzS7CWqy//nDZ48s3/7YOt7+1z8d0f19/kereHD16F861T16SapBT29i866BLye5dc9+Hn9p2yi2qHYGvSTVwce+/ThPH+cImjsfXz+KrTmaQS/VyC9MqdqT67Yftb5++6tHrf/5Pz7D7cvX8fHfWcjfPvTiaDbtGM7RS/04fEHwav/5pkfG7Ftzjb6f//Loufa+B2jdvnwdQNNDHgx6qV/dPb3HlHXt3s/lN/1rE1qjsahe1xIejYvOG/RSPz59x1PNboLGqJe39n/I7Vg+951BL/VjxSh8LV1Hu+7Hz3PfM2P/lBO/+6Wf8dKWvXUL9nq9MzgeP4yVNCYcvrzeUC/L1wiPrN3CVx/oGHD75d/4V2ZNm9indOzu0hv0ktTHp77/FF27Bz5mvmv3/uNuH2ucupGkPoZ6KO1FNzw07Kmc0fgw1j16SRqh/q5MVqu9+489wqve3KOXpD627Bm9aZlDo/BNPINekppoNL5xbdBLUhPV4/THgzHoJamJRuMUSga9JDWRUzeSpBEz6CWpyt/8ZHWzm1B3Br0kVfnqgwOf+qARnKOXxqDROEpCvz6e+1XjT6Bn0EtDdMicVx09ua7xF6E36KUh6jXpi/X8K8M/lcFwjcY5Lw16KXvXl39eU72Dhw41uCVqlgu/8tDoP+goJL1BL2VrOvfUVO+ge/Sqp1Y9jj4iLoyI1RHRERFXN+IxDuvo3MP//efVfkCmUdPbW95rrbunlx37DhxZ//5j63h4zZYj6/sP9rJt74H+bsrOV3t49cDAZ2Ds3NXdb/mrB3rZ1d0z5LZ27uo+6j737j/I7u4eenoPsXWAk5H19B4asP3NtvfAwYY/Rt1PUxwR44GvAe8CNgCPR8Q9KaWV9X4sgMtueoQd+3r4+PlnMD6CvfsPcupJk+t2/z29h1j+wjbmz5zC/BlTGTfutfdZG7bv49QTJzOx7dj/l909vfzl3c/xP97zJmafOOlI+eZd3Zw4uY2e3sT/vncl/+s/nsVJkyf0+9gpJV7euo8Fp0yrqa1b9uxnUts4Thzg/l7Z2c3JUyYwZeJ41nbtYcK4cbxh1lRe2rKX02dNJSJ4ccteFtbweDv2HSAlmDapjc7d3cybcfT9HG7/um37mDFtIh2dewjg9FnTmHnMlXmGprunl+37DjDn5CnHbFv5q13MOmEi+w708q2HX+SvLv4Nxo+r73vjnqqpm+17DzAugpOnTqBr936mTBzPlt37jzwPL23Ze2T8jtfuww4cPET7y9uYN30qr58+mXERrN++j9NnHTsmfV8f1eN76FBi/fZ9nDxlAinBpAnjOP+LD3Lm607ik0sWcfDQIWZOm8hn71rBK7u6OWFSG2u79vLH55/Bc7/axcMdlZBfet7pvO83X88HvlG5KPoZs6fRuWs/Z805iT9557/jFx1b+ObDLwLw+4vnM3XSeH60YhN/8PY38Pv/fj5ffaCD25av43MX/wY/XbWZ/3bBIpa/sJVFp53Ap+94mld7evnYOxay49XXQvi5X+3kza87iXXb9jF10nimTmxjy+79vGHmVN72+fvZvq/yz+GzF57JdT9+/sjtFp16wpF3ZecsnMljL27jmveeydRJbfzFD58F4IPnzGflpt1ce9GbmTpxPH/wt4/yu286lSVnnsqy5zuH+Eqoj827Gn+mzKj3nnBEnAf8VUrpPXn9GoCU0v8Z6DaLFy9O7e3tQ36sdVv3cf6XHhxuU1ve5Anj6O4ZfL54xtQJR/44anXOgpl07dnP3v0H6WzwlXROOWEiW/a89oc+feoEdvTT3jfPOYlVIzjvd7VPXvBGVm7axaQJ49m8s5spE8fzUNUebL195D8s4NuPvATAlb+9kGmT2rhx2Zqj6kyZMJ5XewbeM775D99G76HE3y1/mWkT2/jnlZsb1l6NruFePjEinkgpLR6sXiMuPDIXWF+1vgF4ewMe56j/5r+Oagl5YMghD/DYS9uGfJvhqg55oN+QB+oW8gA3Hud6oI1wOOQBbsl7wH0dL+QBrvruE/Vskn6NNO3D2Ii4KiLaI6K9q6trWPfxF+8/q86tkqTR9di1Sxr+GI3Yo98IzK9an5fLjpJSuhm4GSpTN8N5oNedPHlMXDFeksayRuzRPw4sioiFETERuAK4pwGPI0mqQd336FNKByPivwI/AcYD30opPVfvx5Ek1aYRUzeklO4D7mvEfUuShsZvxkpS4Qx6SSqcQS9JhTPoJalwBr0kFa7u57oZViMiuoCXh3nzU4DGnaRk9JTSDyinL/ZjbLEfxzo9pTR7sEpjIuhHIiLaazmpz1hXSj+gnL7Yj7HFfgyfUzeSVDiDXpIKV0LQ39zsBtRJKf2AcvpiP8YW+zFMLT9HL0k6vhL26CVJx9HSQT+aFyEfjoiYHxEPRsTKiHguIj6Vy2dGxP0RsSb/npHLIyJuzP1ZERFnV93X0lx/TUQsbVJ/xkfEkxFxb15fGBHLc3vvyKelJiIm5fWOvH1B1X1ck8tXR8R7mtCH6RFxV0Q8HxGrIuK8VhyPiPh0fk09GxHfi4jJrTIeEfGtiOiMiGeryuo2BhHxtoh4Jt/mxoio70WDj9+PL+XX1oqI+MeImF61rd/neqAcG2g8hyWl1JI/VE6BvBY4A5gIPA2c1ex29WnjHODsvHwi8EvgLOCLwNW5/Grgurx8EfBPQADnAstz+Uzghfx7Rl6e0YT+fAa4Hbg3r98JXJGXbwL+JC//F+CmvHwFcEdePiuP0yRgYR6/8aPch1uBP8rLE4HprTYeVC7X+SIwpWocPtIq4wGcD5wNPFtVVrcxAB7LdSPf9r2j2I93A215+bqqfvT7XHOcHBtoPIfV1tF6cTbgST4P+EnV+jXANc1u1yBtvht4F7AamJPL5gCr8/I3gA9W1V+dt38Q+EZV+VH1Rqnt84BlwAXAvfmPaEvVi/rIeFC5FsF5ebkt14u+Y1Rdb5T6cDKVgIw+5S01Hrx2XeaZ+fm9F3hPK40HsKBPQNZlDPK256vKj6rX6H702fafgNvycr/PNQPk2PH+vobz08pTN/1dhHxuk9oyqPx2+a3AcuC0lNKmvOkV4LS8PFCfxkJfvwL8GXD4iuSzgB0ppYP9tOlIe/P2nbl+s/uxEOgC/l+egvpmREyjxcYjpbQR+BtgHbCJyvP7BK03HtXqNQZz83Lf8mb4GJV3FDD0fhzv72vIWjnoW0ZEnAD8A/CnKaVd1dtS5d/1mD70KSLeD3SmlJ5odltGqI3KW+2vp5TeCuylMk1wRIuMxwzgEir/uF4PTAMubGqj6qgVxmAwEXEtcBC4rdltgdYO+pouQt5sETGBSsjfllL6QS7eHBFz8vY5QGcuH6hPze7rO4CLI+Il4PtUpm9uAKZHxOGrlFW36Uh78/aTga00vx8bgA0ppeV5/S4qwd9q4/F7wIsppa6UUg/wAypj1GrjUa1eY7AxL/ctHzUR8RHg/cCH8j8tGHo/tjLweA5ZKwf9mL8Ief60/xZgVUrpy1Wb7gEOHyWwlMrc/eHyD+cjDc4Fdua3sz8B3h0RM/Le3Ltz2ahIKV2TUpqXUlpA5Xl+IKX0IeBB4LIB+nG4f5fl+imXX5GPAlkILKLywdmoSCm9AqyPiDfloiXASlpsPKhM2ZwbEVPza+xwP1pqPPqoyxjkbbsi4tz83Hy46r4aLiIupDLFeXFKaV/VpoGe635zLI/PQOM5dKPxwUsDPwi5iMqRLGuBa5vdnn7a99tU3oKuAJ7KPxdRmX9bBqwBfgrMzPUD+FruzzPA4qr7+hjQkX8+2sQ+vZPXjro5I79YO4C/Bybl8sl5vSNvP6Pq9tfm/q2mQUdDDNL+twDteUx+SOWIjZYbD+BzwPPAs8B3qRzN0RLjAXyPymcLPVTeZV1ZzzEAFufnZS3wVfp8+N7gfnRQmXM//Pd+02DPNQPk2EDjOZwfvxkrSYVr5akbSVINDHpJKpxBL0mFM+glqXAGvSQVzqCXpMIZ9JJUOINekgr3/wGUM+oBaryTIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Doppie'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Singole', 'Doppie', 'Triple', 'Quadruple', 'Quintuple', 'Sestuple']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in cols:\n",
    "    dataset[col] = pd.to_numeric(dataset[col], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3f007048>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[  ['Doppie', 'Triple']  ].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3efd5710>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset [ cols ].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3ef81358>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Doppie'].plot.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "tipo_set = dataset.groupby(['Tipologia']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Singole</th>\n",
       "      <th>Doppie</th>\n",
       "      <th>Triple</th>\n",
       "      <th>Quadruple</th>\n",
       "      <th>Quintuple</th>\n",
       "      <th>Sestuple</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipologia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/D</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>157.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Categoria 2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. I</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8560.0</td>\n",
       "      <td>29234.0</td>\n",
       "      <td>6535.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. II</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1459.0</td>\n",
       "      <td>3801.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. III</th>\n",
       "      <td>0.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. IV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. IX</th>\n",
       "      <td>0.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. V</th>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>2178.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VIII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. X</th>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XIII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>4741.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XIV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VIA DI COLLE PIZZUTO</th>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Singole  Doppie   Triple  Quadruple  Quintuple  Sestuple\n",
       "Tipologia                                                                     \n",
       " 1/D                      0.0     0.0      1.0        0.0        0.0       0.0\n",
       "1                         0.0     0.0      0.0        2.0        1.0       0.0\n",
       "16                        0.0     0.0      1.0        0.0        0.0       0.0\n",
       "171                       0.0     0.0      0.0        2.0        0.0       0.0\n",
       "21                        0.0     0.0      4.0        0.0        0.0       0.0\n",
       "3                         0.0     0.0      0.0      157.0        4.0       0.0\n",
       "44                        0.0     0.0      0.0        2.0        0.0       1.0\n",
       "45                        0.0     0.0      1.0        1.0        0.0       0.0\n",
       "49                        0.0     0.0      0.0        1.0        0.0       0.0\n",
       "56                        0.0     0.0      0.0        1.0        0.0       0.0\n",
       "70                        0.0     0.0      0.0        1.0        0.0       0.0\n",
       "71                        0.0     0.0      1.0        0.0        0.0       0.0\n",
       "76                        0.0     0.0      0.0        1.0        0.0       0.0\n",
       "Categoria 2               0.0    44.0      0.0        0.0        0.0       3.0\n",
       "Mun. I                    0.0  8560.0  29234.0     6535.0     1679.0     174.0\n",
       "Mun. II                   0.0  1459.0   3801.0      599.0      133.0       7.0\n",
       "Mun. III                  0.0   323.0    691.0      117.0       17.0       2.0\n",
       "Mun. IV                   0.0   303.0    646.0      315.0       19.0       5.0\n",
       "Mun. IX                   0.0   298.0   2722.0      291.0       41.0       2.0\n",
       "Mun. V                    0.0   270.0   1070.0      114.0       33.0       0.0\n",
       "Mun. VI                   0.0   200.0    926.0      115.0       76.0       0.0\n",
       "Mun. VII                  0.0   687.0   2178.0      301.0       33.0       1.0\n",
       "Mun. VIII                 0.0   258.0   1056.0      175.0       28.0       0.0\n",
       "Mun. X                    0.0   339.0    671.0       40.0       21.0       0.0\n",
       "Mun. XI                   0.0   269.0   2241.0       77.0        6.0       0.0\n",
       "Mun. XII                  0.0   689.0   2264.0      362.0       56.0      11.0\n",
       "Mun. XIII                 0.0  1661.0   4741.0      680.0      119.0       8.0\n",
       "Mun. XIV                  0.0   696.0   1521.0      276.0       42.0       5.0\n",
       "Mun. XV                   0.0   348.0    879.0      168.0       35.0       1.0\n",
       "VIA DI COLLE PIZZUTO     21.0     0.0      0.0        4.0        2.0       0.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['VIA DI COLLE PIZZUTO'], dtype='object', name='Tipologia')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_set.index[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "values = tipo_set.index[0:14]\n",
    "values = values.append(tipo_set.index[-1:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([' 1/D', '1', '16', '171', '21', '3', '44', '45', '49', '56', '70', '71',\n",
       "       '76', 'Categoria 2', 'VIA DI COLLE PIZZUTO'],\n",
       "      dtype='object', name='Tipologia')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in values:\n",
    "    dataset = dataset[~ dataset['Tipologia'].str.contains(col) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12121, 10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "tipo_set = dataset.groupby(['Tipologia']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Singole</th>\n",
       "      <th>Doppie</th>\n",
       "      <th>Triple</th>\n",
       "      <th>Quadruple</th>\n",
       "      <th>Quintuple</th>\n",
       "      <th>Sestuple</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tipologia</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mun. I</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8560.0</td>\n",
       "      <td>29234.0</td>\n",
       "      <td>6535.0</td>\n",
       "      <td>1679.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. II</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1459.0</td>\n",
       "      <td>3801.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. III</th>\n",
       "      <td>0.0</td>\n",
       "      <td>323.0</td>\n",
       "      <td>691.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. IV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>646.0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. IX</th>\n",
       "      <td>0.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. V</th>\n",
       "      <td>0.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>926.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>687.0</td>\n",
       "      <td>2178.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. VIII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>1056.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. X</th>\n",
       "      <td>0.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>671.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XI</th>\n",
       "      <td>0.0</td>\n",
       "      <td>269.0</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>689.0</td>\n",
       "      <td>2264.0</td>\n",
       "      <td>362.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XIII</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1661.0</td>\n",
       "      <td>4741.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XIV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>696.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mun. XV</th>\n",
       "      <td>0.0</td>\n",
       "      <td>348.0</td>\n",
       "      <td>879.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Singole  Doppie   Triple  Quadruple  Quintuple  Sestuple\n",
       "Tipologia                                                           \n",
       "Mun. I          0.0  8560.0  29234.0     6535.0     1679.0     174.0\n",
       "Mun. II         0.0  1459.0   3801.0      599.0      133.0       7.0\n",
       "Mun. III        0.0   323.0    691.0      117.0       17.0       2.0\n",
       "Mun. IV         0.0   303.0    646.0      315.0       19.0       5.0\n",
       "Mun. IX         0.0   298.0   2722.0      291.0       41.0       2.0\n",
       "Mun. V          0.0   270.0   1070.0      114.0       33.0       0.0\n",
       "Mun. VI         0.0   200.0    926.0      115.0       76.0       0.0\n",
       "Mun. VII        0.0   687.0   2178.0      301.0       33.0       1.0\n",
       "Mun. VIII       0.0   258.0   1056.0      175.0       28.0       0.0\n",
       "Mun. X          0.0   339.0    671.0       40.0       21.0       0.0\n",
       "Mun. XI         0.0   269.0   2241.0       77.0        6.0       0.0\n",
       "Mun. XII        0.0   689.0   2264.0      362.0       56.0      11.0\n",
       "Mun. XIII       0.0  1661.0   4741.0      680.0      119.0       8.0\n",
       "Mun. XIV        0.0   696.0   1521.0      276.0       42.0       5.0\n",
       "Mun. XV         0.0   348.0    879.0      168.0       35.0       1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tipo_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Singole 0.0\n",
      "2 Doppie 16360.0\n",
      "3 Triple 54641.0\n",
      "4 Quadruple 10165.0\n",
      "5 Quintuple 2338.0\n",
      "6 Sestuple 216.0\n"
     ]
    }
   ],
   "source": [
    "for col in cols:        \n",
    "    print(cols.index(col) + 1, col, dataset[col].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f45b9e978>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset['Doppie'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3ef1e6d8>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[['Doppie', 'Triple']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f45c7df98>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset[cols].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3ef5a908>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipo_set['Doppie'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3edd7828>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipo_set[['Doppie', 'Triple']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3ed48400>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tipo_set[cols].plot()"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}