{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coastal Waste monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Load my libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import os\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "from matplotlib.ticker import FuncFormatter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Load my data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'monitoring_items_mb.csv'\n",
    "filename = os.path.join('./', file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Open the file\n",
    "infile = open(filename, 'r') # 'r' says we are opening the file to read, infile is the opened file object that we will read from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Store the data from the file in a variable\n",
    "data = infile.read()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>ID</th>\n",
       "      <th>MonitoringID</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Datetime</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>WasteCategory</th>\n",
       "      <th>WasteCode</th>\n",
       "      <th>User</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1189</td>\n",
       "      <td>Monitoraggio Alberoni WWF</td>\n",
       "      <td>45.348295</td>\n",
       "      <td>12.329182</td>\n",
       "      <td>10/04/2022 13:20</td>\n",
       "      <td>2</td>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>G87</td>\n",
       "      <td>David Poletto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1186</td>\n",
       "      <td>Monitoraggio Alberoni WWF</td>\n",
       "      <td>45.348295</td>\n",
       "      <td>12.329182</td>\n",
       "      <td>10/04/2022 13:20</td>\n",
       "      <td>7</td>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>G79</td>\n",
       "      <td>David Poletto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1188</td>\n",
       "      <td>Monitoraggio Alberoni WWF</td>\n",
       "      <td>45.348295</td>\n",
       "      <td>12.329182</td>\n",
       "      <td>10/04/2022 13:19</td>\n",
       "      <td>13</td>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>G4</td>\n",
       "      <td>David Poletto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1187</td>\n",
       "      <td>Monitoraggio Alberoni WWF</td>\n",
       "      <td>45.348295</td>\n",
       "      <td>12.329182</td>\n",
       "      <td>10/04/2022 13:18</td>\n",
       "      <td>1</td>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>G73</td>\n",
       "      <td>David Poletto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1185</td>\n",
       "      <td>Monitoraggio Alberoni WWF</td>\n",
       "      <td>45.348295</td>\n",
       "      <td>12.329182</td>\n",
       "      <td>10/04/2022 13:18</td>\n",
       "      <td>1</td>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>G82</td>\n",
       "      <td>David Poletto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID               MonitoringID        Lat        Lon          Datetime  \\\n",
       "0  1189  Monitoraggio Alberoni WWF  45.348295  12.329182  10/04/2022 13:20   \n",
       "1  1186  Monitoraggio Alberoni WWF  45.348295  12.329182  10/04/2022 13:20   \n",
       "2  1188  Monitoraggio Alberoni WWF  45.348295  12.329182  10/04/2022 13:19   \n",
       "3  1187  Monitoraggio Alberoni WWF  45.348295  12.329182  10/04/2022 13:18   \n",
       "4  1185  Monitoraggio Alberoni WWF  45.348295  12.329182  10/04/2022 13:18   \n",
       "\n",
       "   Quantity WasteCategory WasteCode           User  \n",
       "0         2      PLASTICS       G87  David Poletto  \n",
       "1         7      PLASTICS       G79  David Poletto  \n",
       "2        13      PLASTICS       G4   David Poletto  \n",
       "3         1      PLASTICS       G73  David Poletto  \n",
       "4         1      PLASTICS       G82  David Poletto  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(filename, sep = ';')\n",
    "# Print the Data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Descriptive statistics  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>WasteCategory</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GLASS/CERAMIC</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.322876</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>METAL</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PLASTICS</td>\n",
       "      <td>138.0</td>\n",
       "      <td>6.137681</td>\n",
       "      <td>10.686093</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PROCESSED/WORKED WOOD</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RUBBER</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TEXTILE</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           WasteCategory  count      mean        std  min  25%  50%   75%  \\\n",
       "0          GLASS/CERAMIC    9.0  2.000000   1.322876  1.0  1.0  1.0  3.00   \n",
       "1                  METAL    4.0  1.500000   1.000000  1.0  1.0  1.0  1.50   \n",
       "2               PLASTICS  138.0  6.137681  10.686093  1.0  1.0  2.0  7.00   \n",
       "3  PROCESSED/WORKED WOOD    2.0  3.000000   2.828427  1.0  2.0  3.0  4.00   \n",
       "4                 RUBBER    6.0  1.166667   0.408248  1.0  1.0  1.0  1.00   \n",
       "5                TEXTILE    4.0  1.250000   0.500000  1.0  1.0  1.0  1.25   \n",
       "\n",
       "    max  \n",
       "0   4.0  \n",
       "1   3.0  \n",
       "2  77.0  \n",
       "3   5.0  \n",
       "4   2.0  \n",
       "5   2.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Summary statistics by waste category\n",
    "df.groupby(\"WasteCategory\")['Quantity'].describe().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Plot data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's compare the distribution of waste quantity for the 6 waste category\n",
    "\n",
    "#transform quantity into log\n",
    "df['log_vals'] = np.log(df['Quantity'])\n",
    "\n",
    "plt.figure(figsize=(8,5), dpi= 80)\n",
    "\n",
    "for g in pd.unique(df['WasteCategory']):\n",
    "    df.loc[df['WasteCategory']==g,'log_vals'].hist(bins=10, alpha=0.65, label=g, density=True)\n",
    "    #groupby('WasteCategory')['log_vals'].hist(bins=10, alpha=0.65, density=True)\n",
    "    plt.gca().set(title='Histogram Waste ditribution by category (10/04/2022)/ Alberoni ')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.savefig('Waste_ditribution.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001163F3C53C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001163F3D8390>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001163F30F9B0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001163F451FD0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001163F5D0668>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001163F5FECC0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8TbmWqon8+cAhEXEiVW3zC41G1XszgbWApS3z1mbqncRNZW+jKhjnUxWMi6lO2vZqMqg2G0bEV4F1I+KNwEXAQfRXMoRCvkvgpcCvgXWojvc30n930puTmV9qmV4M/FdEvKepgBq7I1pErA/8PVXCXgzcMFUHIETENKof7sPAhpl5R8Mh9VRE7ELV5PgTHj+J+2vgkMz8epOxqXfqGuvGVIOn/kAfDk6NiL8CXgb8BrgR+BDw8cy8f8w39lDbANe/pfpOb+qnAa4R8erM/FbTcYwnIs4DfsTKrRZ/k5lvbiKmvrqNaUTsnJkXNx1H06bi9xARM4AX8vhJ3O1jPSVKg6UenPpxqhO3B6h+By8AjsjMvhnnUl/l8Uhm3tkyb7PMvL7BsJ4gIi7LzFdExN7AAVRjZLYGzszMvhgrExFLgK8A783MPzQdz2jqkfj7U31/w60W1wKfbWokfmOXfA2LiOmZuaKefH6jwTRoqn8PdYJ+wpOhImLfzHQ06dTQ94NTI+IY4NXAzIi4GTggM4eoTjZe0WhwI9sXeEWfDnC9jurvenVE/DewIDN/3XBMK8nMRyLiHOCXPN4C9L0mL51r6pKv50XEhRHxK+D/IuIXEfF1YEo1hfo9jOuhpgNQz5QwOHVHYJvM3IyqH/sz9fxpzYU0ohIGuA5l5nnApsB9wFci4uaIOL/huJ6gvrLlYqorB55DVeO+KCL2ayqmpmraC4DDW5uU6mfwng5s1VBMTfB7GENmfrHpGNQzJQxOnVbXrMnMD0TEORHxAfrrxALKGOA6DSAzH6a6ucqnI2IO1QC1ftJ3V7Y0lbRntfcBZeZ1EdFQOI3xewAi4nKqm0G0mkZ1Nr5lAyGpxzLz8xFxEU8cnPqRPhuc+qWIuAF4bWbeB+xDNYJ882bDeqLMnA8rDXB9a58NcD2ofUbdNXJjA7GMpe+ubGkqad8SEafxxBF5O1GN0ptK/B4qhwGfp7pLkoPPpqg6QX+tdV4/DcrMzBMi4kKqY5XMfBR4Tb/eybFuFRi+HO2OPvsubxlpfj/FWPtX4KaIWOnKlqYCaippHwDsRtX8NXxWfTHV7eymEr8HIDOvj4izgRdn5pTad62snwdlZubPRpjdtyea/fxdDuvnGDPzaxFxCX10ZUtfXfIlaWqKiOdRXau/CVUSnE51NcHBmfm/TcY2ktZEExEHZ+YJTcc0rITvsoQYx9LklS2NX/IlSRQwKLM90UTEnxJNo4GtrO+/S8qIcSyNXdni87Ql9YMRB2U2FcwoFlDd/exZmblBZj6Hqs/z9IbjalfCd1lCjKNq8soWa9qS+kEJgzJLudqjhO+yhBj78soWk7akflDCoMwiEg1lfJclxAh9eGWLA9EkaQLq657bE801wAXDN13R4KlvoHNnv1zZYtKWJKkQDkSTJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpECZtSZIKYdKWJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpED7lq09ExAbAT4FbW2ZPAz4F/B9wUmZuNMb73wucCGzR+lzaiJgJfBR4LTBUb/O/gI8DLwTOrVd9KrAu8LN6+kyqJ+7clplPrre1BjAfeBvVb+dJwNeAYzLz0XqdD9bLpwFrUD0R6YjMfGxVvxOp1VjHSGaeNsryJwO/AvbJzP+rt/N04GPA9sBDwAqq4+D4zFxerzPmbz0i9qY6Nn/GE/0oM99eb2PMY2Gs5eNtPyL+BXgP8Ot6/kyqp40dk5k/afnODqQ6rj8MrJeZ99XzP071BKsXZGa2xPOyzHxLPb0fsH+97SHgZuDIzPxFvfwMYAfg9/XHTa+/71My8z/qdYaAp2fmonr65cBXgPdl5rkRcRfwKLCkbT8PyMxr6/ffBiyvv6dlwH9m5slt6xMRpwBLMvPgeno2sAj4Sma+rZ63BnAfsHlm3h4Rz6P6LWwKPFjHckpmntay3RnAocDu9fcAcAVwdMv32bof0+p/XwL+PTO7+nQwk3Z/WZKZLxmeiIi5VD/YD03gvfsB5wAHAW9tmX8Q8DzgpZm5LCLWBS4DFmXmqcBL6s/aG3hTZu7c8vkbtH3GZ4GnAK/MzPsjYp36MxcAe0bEP1I9wm6LzFwSEbOA84B/AY6Y0DcgjW3EYyQibqR66lb78mlUJ7MfBf4pIv6M6slcnwf2r4+JpwCfA86mSqIwzm+9Xueq1uOl1XjHwgSPlVG3X/tSZh7Y8pl7ApdFxN9k5uJ69q7APsCbgG2BC+v5O1OdhPwD8Il63iuoT+Ij4jhgY2DnzPxlREwH9gC+FxGbZeav6veckJnHtcTwHOD2iLgoM+9o+052qb+/t2bmt1sW7Z6ZN46xn9u3JP2nARdHxFqZ+cm29S6hOjkZ9mrgO8CrI2JmZi6lSs731gl7A+BK4CjgnzJzqP49nRMRz8/Mw+vt/BfwGLB1Zt5XV4QOrr+LTTLzgfb9aPm9nAC8d4x9W2U2j/exzPw18BPg7rHWi4jtqGrKHwR2jYhntyx+JtWZ8pr1Nu+nKnSuXZVY6h/47sA76m2QmQ9RnSwMFwTPpKoxrFUvfwQ4sGW51FUtx8iGo6wyC/gLqtoVVDXHH2TmJ4ZrQJn5B+DtwCsjYtMJ/tbHM96x0PVjJTPPBm6nPvGoT1CenJm/pEpo29XzN6AqDz5FlbSJiCcBWwKXRsSz6n19c/1eMnNFZp5FdWJxOKN7FlUt84HWmRGxB3Ay8Nq2hL2q+7gIOAT45/qErNV3qo+Kp9bTuwBfoPpOtq3nvRL4ev36cODczDxz+NGq9e/pLcD8iHhmRGwBbA68fbhWnZlL65aEO6i+p5HifIjq7/muiJjT6f6OxJp2H6t/MH9NfWCP4QDgnMy8OyIuo/qxHFovO56qIFgUEddT1TLOy8wfrGI4LwMWtpzBA5CZ91A1d0HVpL4zcE9E3ER1YvDVzPzuKn6WNCEtx8j11EkwIn5Yv16PKlmfT9UdBHViat9OZj4SEVdTPSv7F4z/WwfYpv6sVp/KzNMZ/1iYyLEy1vZHcwvwt/Xr1wHfqF9fAny6fr0LcDHwXeBv6trri4AfZ+bvIuKNwO31yUy7bwPHtkwfXCfkOfW/q4HX1clv2IHA0cAnRyl3zomI1ubxRzNzs3H28RnA03i8aZ7MfDAirgW2jYiLgNdQJfjnUJ2cfIcqaf9H/ZYtqboIniAzfxsRt1Ml6+cB3xulifvbwKt4vKWifTu/iojFQADfH2N/VolJu7+s1XKQzqDqj9mdlft7/iQingHsRpVUoSoMTomIj2TmQ3Uz1iYR8SKqPrztqZp1DhmpX2gMKxinZaaulby67ifanurM/usRcXJmHjrWe6UJGvEYqZtwN6CleTwiXkNV0/paZj7Yso2Zo2x7Tao+y3F/67VRm6/HOxYmeKyM1zw+kiHg4fr1rsC/1q+/BzynroXuAvxbZi6tT/JfSTW+5est2xnvOxp2QmYeVzcHf4mqX/eKtvfsDLy83r/LM/OStuXjNY+3G/78kcrF4RaF3wE/qZuzLwa+XHdBbNwW30T2c6LfxWixPjzOOqvEpN1fntAfN6xu/h7NO6l+GF+LCKgKmznAXsDJEfEfwILM/DHwY+Az9ZnxYVTNVRN1PfDCiJjd0ocz3Kd4KlWf2XuBqzPzWqrBc/8ZEVtT1WxM2uqGEY+RkWTmNyPieKoCe7if9xqqQv3/ta4bEU+m6u/8CNXAtfF+62OqB3WNeiyMt3wi+zeKTYHTI2JNYMPMvLX+LpZFxHeAHanGsVxVr/91qtaFjanGvwBcBzw/Ip5Rty602p4RutYy86G6T/12qv7e41sW75qZv64Htp0TEZtm5k8nuY8/azsRG3YJ1diEh6laE8jM2+qE/Q/ANXVXBDz+W3hCl0T9d/5L4AbgHuD9EbF2ZrYn3+2p+sRHFBHPpRqYN5l9XYl92gWrR0K+E9gvMzeo/z2HajTk/LrPZz3gXyNi7fo904CNqEaCTlhm3k01sOK04T6a+v+TqQZ2LAHWBv6tpU8Jqqa6VfosqYuOo+pfHR6gdDJVQj6sPn6oB6KdCXw3M2+Y4G99POMdC10/ViLiHVTNuf9NNajssrZVLqEa93JFPSgLqubzVwLPBn4Af+rXPRH4Yp3Ahrc/D3gj8O8jfX7dnP5+4MOt76OqfZOZX6SqjZ8/XB51sI9/UX/+cSMtz8yFVKPld6VO2rVvUA3wa21N+Cjw5ojYq2X7zwK+CJycmXdndSXOlcAZ9e+EiFgjIo6gGkfxuVHi/DOq7oiTWk4SusKadjleGBHtZ5a7U514ndM2/wSqy1V2ourv/ijwo4h4lOpvfhnV5SKr6gCqvqlrI2IZVfPQhTw+uv1fqZoWhy/VWIOqL+fNHXyWNGl1E/CBwDcj4j/rWtcWVL/VH0fEUqqWqnN5YiIY77cOI/c5L8vMTRj/WJjIsTLW9gHeUtfOh6jKgQS2q/vnd6VKPq0uAf4T+NOo67r/9kGqftuhlvmH1ycBX61rqWtS1Ty3yMyfM4rMPCci3kn1Xf7TCKscRFVTX8DjI/Xb+7ShSnYL6teXR8Ryqsu+hoDTxuna+zbwqrp1cdjXqf6mw3381F0qWwDH1kl4GfAIVWvKqS3v3YOqb/zKutKzJlUZuuXwQMW2/VhO9ff8ClUFqqumDQ2N1yQvSZL6gc3jkiQVwqQtSVIhTNqSJBXCpC1JUiFM2pIkFaInl3xtttlmQ3Pnzh1/RWmKWrhw4aLMfHrTcfSKZYI0urHKg54k7blz53L++ef34qOkIkXEqNe+DiLLBGl0Y5UH3lxF0koiYjOqZwFv1zZ/F+AYqhtRnJaZn4+Itaju8b0e1d3H9srM3yOp6+zTlvQE9X2xF1A91rJ1/kyqu+29muoBEO+qH1izP3BrZm4DnEX1fGJJq4FJW1K7nwJvGGH+C4E7M/MPmfkY1WMYt6F64MTw4y4voXpcoaTVoG+axxcvWcrS5Su6sq2Za0xnzlqjPU1N0lgy8yv1Yy7bzQFa77X8ANXDGVrnD8+btG6VCZYH6oZ++T32TdJeunwFe5/eneeEnzFv065sR9ITLAZmt0zPBv7YNn943qR1q0ywPFA39MvvsW+StqS+dzvVc5afCjwIbEv1NKfnUj1R7gaq5zVfNeoWJE2KSVvSmCLibcCTM/PUiDgE+CbVeJjTMvPXEfFZ4MyIuBp4jMcfuSipy0zaklaSmXcBm9evz22Z/zXga23rPgz8Yy/jk6YqR49LklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFmNF0AJL6R0RMB04GNgYeBfbNzDvrZS8B/l/L6psDuwE3AP8L3FbPvyAzP9WzoKUpxKQtqdVuwKzM3CIiNgc+CewKkJk/BLYDiIh/BO7OzEsj4lXAFzPzvQ3FLE0ZJm1JrbYGLgXIzOsiYpP2FSJiHeDDwLb1rJcBL42IK4HfAe/LzN/0KF5pSplQ0o6I9YCbgB2AZcAZwBBVc9h7MnPF6gpQUk/NAe5vmV4eETMyc1nLvHcAX87MRfX0HcBNmfntiNgd+DTwpt6EK00t4w5Ei4iZwOeAJfWs44GjMnMbYBp105mkgbAYmN0yPb0tYQPsDixomb4MuLx+fQHwd6svPGlqm8jo8eOAU4C76+mXAVfWry8BXrUa4pLUjGuAnQDqPu1bWxdGxLrAmpn5y5bZC4A31q9fSdUqJ2k1GDNpR8TewO8z85sts6dl5lD9+gFg3dUUm6TeuwB4JCKuBU4ADo6IQyLiH+rlGwJ3tb3nMGD/iLgC2A+Y36NYpSlnvD7tfYChenToS4CzgPVals8G/riaYpPUY/X4lP3aZt/Rsvz7VCPMW9/zM2D71R+dpDFr2pm5bWa+PDO3A34IvB24JCK2q1fZEbhqtUYoSZKAzi75ej/w+Yh4EnA7cF53Q5IkSSOZcNKua9vDXt79UCRJ0li897gkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaWLucqwAABbXSURBVEuSVIhOnqctaUBFxHTgZGBj4FFg38y8s2X5icBWwAP1rF2BmcC5wFrA3cC8zHy4l3FLU4U1bUmtdgNmZeYWwGHAJ9uWvxR4TWZuV/+7HzgGODcztwF+ALy7pxFLU4hJW1KrrYFLATLzOmCT4QV1Lfz5wKkRcU1E7NP+HuAS4FW9C1eaWkzaklrNAe5vmV4eEcPdaOsAnwb2AF4LHBARL257zwPAuj2KVZpy7NOW1GoxMLtlenpmLqtfPwx8ari/OiIuo+r7Hn7Pkvr/P/YuXGlqsaYtqdU1wE4AEbE5cGvLsg2BqyNijYiYSdUsfnPre4Adgat6F640tVjTltTqAmCHiLgWmAbMi4hDgDsz86KIOAe4DlgKnJWZCyPiWODMiHgnsAh4W1PBS4POpC3pTzJzBbBf2+w7Wpb/B/Afbe/5LVUft6TVzOZxSZIKYdKWJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpECZtSZIKMeZ12vVdj04DNgDWBI4FfgycAQwBtwHvqa/tlCRJq9F4Ne09gHvrR+7tCJwEHA8cVc+bRvU8XUmStJqNl7S/DBzdMr0MeBlwZT3tY/gkSeqRMZvHM/NBgIiYDZwHHAUcl5lD9So+hk+SpB4ZdyBaRDwbuBw4OzPPBVr7r30MnyRJPTJm0o6I9YFvAYdm5mn17B9ExHb1ax/DJ0lSj4z3lK8jgKcAR0fEcN/2fODEiHgScDtVs7kkSVrNxuvTnk+VpNu9fPWE0x3TgHsffHTS25m5xnTmrDVz8gFJktQFA/k87eVDQ+xzxo2T3s4Z8zbtQjSSJHWHd0STJKkQJm1JkgoxkM3jkjoTEdOBk4GNgUeBfTPzzpblBwNvrSe/kZkfjohpwK+An9Tzv5eZh/cwbGnKMGkXZPGSpSxdPvnbvE+fNo0VQ0PjrzgBDtYbOLsBszJzi4jYHPgk9a2KI+J5wO7AZlTPHrgqIi4AHgZuzsxdGopZmjJM2gVZunwFe5/+/Ulv57S9N+nKQD1wsN4A2hq4FCAzr4uITVqW/RJ4bWYuhz89UOgRqlsbz42Iy4ElwMGZmb0NW5oaTNo90K0acpcqx9JY5gD3t0wvj4gZmbksM5cCi+rm8E8AP8jM/42IZwAfz8wvR8TWwBcAz+ak1cCk3QPdrCFLq9liqtsTD5uemcuGJyJiFtXjeh8ADqhn30j1MCEy8+qImBsR01qeUSCpSxw9LqnVNcBOAHWf9q3DC+oa9leBWzLz3cPN5MCHgIPqdTYGfmHCllYPa9qSWl0A7BAR11LdXHBeRBwC3AmsQXU3xDUjYsd6/cOBfwO+EBGvo6px793zqKUpwqQt6U8ycwWwX9vsO1pezxrlra9bPRFJamXzuCRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCG+uMoZpwL0PPjrp7figD0lSN5i0x7B8aKgrj7D0QR+SpG4waasvdOvxpTPXmM6ctWZ2ISJJ6j8mbfWFbj2+9Ix5PsZZ0uByIJokSYUwaUuSVAibxzUpjrCXpN4xaWtSHGEvSb1j87gkSYWwpi2NoFuXoIGXoUnqno6SdkRMB04GNgYeBfbNzDu7GZjUpG5dggZlXYY23rEdEe8E3g0sA47NzIsj4mnAucBawN3AvMx8uOfBS1NApzXt3YBZmblFRGwOfBLYtXthSZ1xYNykjXpsR8QzgPcBmwCzgKsj4n+AY4BzM/OMiDiMKqmf0Ej00oDrNGlvDVwKkJnXRYSjiNQXHBg3aWMd238PXJOZjwKPRsSdwIvr93ysXueS+rVJW1oNOk3ac4D7W6aXR8SMzFw20soLFy5cFBE/7/CzVtnWJ7mdXmynm9sa1O0AbDmxbT23e584KWMd2+3LHgDWbZs/PG9MvSwTJvj9Sz0x2fKg06S9GJjdMj19tIQNkJlP7/BzJPXWWMd2+7LZwB9b5i9pmTcmywSpM51e8nUNsBNA3e91a9ciktSksY7tG4BtImJWRKwLvBC4rfU9wI7AVb0LV5papg11MOKmZYTpi6nG/szLzDu6HJukHhvp2KZKyHdm5kX16PF3UZ3wfywzvxIR6wNnUtWyFwFvy8yHGtkBacB1lLQlSVLveUc0SZIKYdKWJKkQJm1JkgrReNKuB75oNYiINZuOYSwRsVZEPKnpOMYTEes1HYMkQUMD0SLiecDxVLdDXEZ18nArcHBm/m/PAypcROwCnAQsBY7MzC/V8y/LzFc0GlyLiPhLqjtl3QOcBywAlgPzM/PiJmNrFREbts06C3g7gL/P1SciZlKNWl+X6lrv2zLzsWaj6o6ImJ2ZD9SvN6K6t/vNmXl7s5GpNE095WsBcHhmXj88o74m9HRgq4ZiKtmRwN9RXaLz5YiYlZln1tP95HTgQ8AGVEl7Q+ARqltf9k3SBr4NPEz18ItpQACfA4aAvjkJGiQR8Trg48BPgAepLh97QUQckZkXNhpcd3wVeEVEzAMOAC4DDoiIMzPz1GZD6666ZWobHj/5+l5m/qbZqLojIp6fmT+pX+9EVe7elJmX9iqGppL2rNaEDX+6z3FD4YwuIi4H2puZpwFDmbllAyGN5LHMvA8gInYFLouIX1AlmX4yIzOvBK6MiO0z83cAETHq3fQasglwCvDZzPyfiLg8M7dvOqgBdySwdWYuHp5R38Dl28AgJO1h7wC2z8wH65aFy4GBSdoRsS/VdfxXU93SdiPgiIhYkJmnNBpcd3yO6uTrMKp77n8deEdEbJaZH+5FAE0l7Vsi4jSqBxPcT3VWvRPwo4biGcthwOeB11M15fejuyLieODozHwgIt4AfBP4s4bjapcRsQB4V2buDVD/+O9pNKo2mfm7iHgzcFxElPNczbLNpGrdaLWE/jvx7NTsiHgq1W99uBxZBvT9mI5VNA/YKjOXDs+ox61cQ3UiPCheR3XytSwiTgGuBAY6aR9A9QjArakeNrCYqnn0gobiGVVmXh8RZwMvzsy+i6+2D7AHdQGXmb+MiO2BwxuNamXvBHbJzBUt834FnNhQPKOq77d9UETsTR8M2JwCTgVujoirqU7k51CVD3332+jQtVRN5M8HDomIE6lqo19oNKrum0n1XPWlLfPWZnBOvtaLiL8DfkP1G72Pan9n9SoA74gmqS/Ut0P9ex4/kb8hM3/bbFTdFRHTgHWoWhU2HLTbP9eDYo+nGpswfPL118Ahmfn1JmPrhog4GHgp8DLgi8CnqO6/f3hmntOLGEzakvpWROzcT1cWdNsg7l9EzKB6mMzwydftYz0FsnQRMad1LMbqZrOfpL7Sdu+G5zcWyGoy6PuXmcsy89bMvKb+f1k9QG0gZebiXu5fU33akvQn7fduqBPbrcDBjQbWJYO+fxMw6E9969n+mbQl9YNBv3fDoO/fmDLzi03HsDr1cv9M2pL6QTH3bujQoO8fUMx9LTrWD/tn0pbUD0q6d0MnBn3/hpVwX4vJaHz/HD0uqXH1pVDt9264BrggM4svpAZ9/1pFxAeAO/v4vhaT0vT+mbQlSSqEl3xJklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUiBlNB6CVRcQQcBuwHBgC1gYWA/tn5o0RsR1wUmZu1Pa+k4BFmfkvEXEGsAPwe2AasCbwP8DBmblsAsv/BXgP8Ou28C7NzMNGWD4NmANcALw/M4e6821IiogTgW3ryRcBPwOW1NOPAusB97e97ViqY/pG4MTM/HS9rWnAV4Ef1/8Oqdd/Tr3N39fT7wX+CnhTZu4cEXsPvx4hvjN4vDxpdVpmnriKu6sxmLT71/aZuWh4IiL+Gfg0sMUqbOOEzDyufv8s4FrgLcA5E1z+pcw8cIztP2F5RDwF+BHwzfqfpC7IzPcNv46Iu4DdM/PGevoK4AOZed5I742INwHfjYjrM/MG4CNUJ+lHZOYK4Kx6vTOA24bLhHreX61CmCe0vlerh83jBYiIGVRnwfdNYjPrUB2o93S4fCLWp2oV+MMktiGpizLzFuD9wH9HxB7APwFvrRO2CmNNu39dXjeTPw14BLgYmLeK2zi4PkjXoGrmuha4ehWWvyUitm7b5qGZ+c225esATwVuBt5dn81L6p1PRMRRbfNemZn3AmTmgoh4BXAm8NLMXB0n1sPlSas9M/PW1fBZU5ZJu39tn5mLIuKlwDeAyzPzd/Wy0c6Qp1P1gw9rbf5eB/gScCLw7gkun1DzeEQ8CTiJqq/ta6uyk5K6YtTmcYCIWBd4GfBbYBfgltUQg83jPWDzeJ/LzJuBg4EzImKDevYi4M9HWH194N5RtvMQsIDHB7Os0vJxYnwMOBBYF/jEqr5f0upTDzz7AnA51WCxD0bEK5uNSp0yaRcgM78I3ACcUM+6A3gkIt4yvE5EvAjYnmq06EoiYjqwY72dVV4+gRgfA/YHDoiIv+tkG5JWiw8DzwTmZ+ZCqlHh50bEXzQbljph83g5DgR+FBGvycxvRsTOwPF1P9Y04EGq/qOftLxnuI9p+LKxm4EDVmH5SH3av8jMfxgpwMy8OiLOAT4TEVt52ZfUMyP1aZ9P1Qx+ALBJZj4KkJlnRsS2VAPTtsvMZavwOa+NiAdbpv+Ymc+qX4/Up31dZu63CtvXOKYNDVmuSpJUApvHJUkqhElbkqRCmLQlSSqESVuSpEKYtCVJKkRPLvnabLPNhubOnduLj5KKtHDhwkWZ+fSm4+gVywRpdGOVBz1J2nPnzuX888/vxUdJRYqInzcdQy9ZJkijG6s88OYqkiYtItYAPg8E1f3v52XmT5uNSho89mlL6oZdADJzK+AY4Phmw5EGk0lb0qRl5oXAu+rJ51I9TUpSl/VN8/jiJUtZurw7z2SfucZ05qw1syvbkjQxmbksIs4EXg+8abLb61aZYHmgQdI3SXvp8hXsffr3u7KtM+Zt2pXtSFo1mblXRBwKXB8RL6of+dqRbpUJlgcaJDaPS5q0iNgzIg6vJx8GVlANSJPURX1T05ZUtPOB0yPiu8BM4KDMfKThmKSBY9KWNGl1M/ibm45DGnQ2j0uSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYUwaUuSVAiTtiRJhTBpS5JUCJO2JEmFMGlLklQIk7YkSYWY1ANDImI94CZgh8y8ozshSZKkkXRc046ImcDngCXdC0eSJI1mMs3jxwGnAHd3KRZJkjSGjpJ2ROwN/D4zv9ndcCRJ0mg6rWnvA+wQEVcALwHOiohndC0qSZK0ko4GomXmtsOv68S9X2be062gJEnSyrzkS5KkQkzqki+AzNyuC3FIkqRxWNOWJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpECZtSZIKMelLviRNbfXDg04DNgDWBI7NzIsaDUoaUNa0JU3WHsC9mbkNsCNwUsPxSAPLmrakyfoycF7L9LKmApEGnUlb0qRk5oMAETGbKnkf1WxE0uCyeVzSpEXEs4HLgbMz89ym45EGlTVtSZMSEesD3wIOzMzvNB2PNMhM2pIm6wjgKcDREXF0PW/HzFzSYEzSQDJpS5qUzJwPzG86DmkqsE9bkqRCmLQlSSqESVuSpEKYtCVJKoRJW5KkQpi0JUkqhElbkqRCmLQlSSqESVuSpEKYtCVJKoRJW5KkQpi0JUkqhElbkqRCmLQlSSqESVuSpEKYtCVJKoRJW5KkQpi0JUkqhElbkqRCmLQlSSrEjE7eFBEzgdOADYA1gWMz86IuxiVJktp0WtPeA7g3M7cBdgRO6l5IkiRpJB3VtIEvA+e1TC/rQiySJGkMHSXtzHwQICJmUyXvo7oZlCRJWlnHA9Ei4tnA5cDZmXlu90KSJEkj6XQg2vrAt4ADM/M73Q1JkiSNpNOa9hHAU4CjI+KK+t9aXYxLUmEiYrOIuKLpOKRB1mmf9nxgfpdjkVSoiPggsCfwUNOxSIPMm6tI6oafAm9oOghp0Jm0JU1aZn4FWNp0HNKg6/Q6bUmSpozFS5aydPmKSW9n5hrTmbPWzI7fb9KWJGkcS5evYO/Tvz/p7Zwxb9NJvd/mcUmSCmFNW1JXZOZdwOZNxyENMmvakiQVwqQtSVIhTNqSJBXCpC1JUiFM2pIkFcKkLUlSIUzakiQVwqQtSVIhTNqSJBXCO6JJI+jWwwFg8g8IkKRhJm1pBN16OABM/gEBkjTM5nFJkgph0pYkqRAmbUmSCmHSliSpECZtSZIKYdKWJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpECZtSZIKYdKWJKkQJm1Jkgph0pYkqRAmbUmSCmHSliSpEDM6eVNETAdOBjYGHgX2zcw7uxmYpHJYJki90WlNezdgVmZuARwGfLJ7IUkqkGWC1AOdJu2tgUsBMvM6YJOuRSSpRJYJUg901DwOzAHub5leHhEzMnPZSCsvXLhwUUT8vMPPWmVbntSrT5LGN8Hf43NXcxirW9+WCZYH6ieTLQ86TdqLgdkt09NHOzgBMvPpHX6OpDJYJkg90Gnz+DXATgARsTlwa9ciklQiywSpBzqtaV8A7BAR1wLTgHndC0lSgSwTpB6YNjQ01HQMkiRpAry5iiRJhTBpS5JUCJO2JEmFMGlLklSITkePT1pEzAReDKwL/BG4LTMfayqeboiIjYBHWu+5HBGbZeb1DYbVFRHx6sz8VtNxTFZE/Hlm3hsRfw28BPhxZv646bg0eGXCIJcHMBhlQonlQSOjxyPidcDHgZ8AD1LdlOEFwBGZeWHPA+qCiDgaeA0wE7gZOCAzhyLissx8RbPRrbqIeFfbrEOA4wEy89TeRzR5EXEScBfwW+Bg4LvA5sB5mXlcg6FNeYNWJgxaeQCDVyaUWh40VdM+Etg6MxcPz4iIdYFvA8UdoLWdgC3rA/MTwGeAA6iuWS3RbsBTgEuo9mFN4JmNRjR5L83MAyPiu8A2mflQRMwAvgf07UE6RQxamTBo5QEMXplQZHnQVJ/2TODhtnlLgJIvGp+WmUMAmfkBYN2I+ADl7tPrgMuoTuw+AtyVmR/OzA83G9akTIuIpwL/B6xdz5tD2QXpoBi0MmHQygMYvDKhyPKgqZr2qcDNEXE11UMG5lA9JejEhuLphi9FxA3AazPzPmAf4CKq5pbi1AXOkRHxRuA8YFbDIXXDR4ArqW6xeUtEfB/YCDi80agEg1cmDFR5AANZJhRZHjR2R7SIWB/4e6qDczFwQ2b+tpFguiQi/hL4RWYub5m3W4l9cq0i4m+At2fmoU3HMlkR8WRgS+BpwL3ATZm5qNmoBINXJgxqeQCDUyYUWR4MDQ31zb8NN9xw56ZjcJ+m1v4M6j4Nyr9B+9sM2v4M4j71+/40fp12RLTG8PzGAumiQdunQdsfGMx9GhSD9rcZtP2BwdunkvankT7tiHge1aUCmwDL6i/sVqph90UatH0atP2BwdynQTFof5tB2x8YvH0qdX+aGoi2ADi89SYD9TN4Twe2aiimyRq0fRq0/YHB3KdBMWh/m0HbHxi8fSpyf5pqHp/VflegzLyuoVi6ZdD2adD2BwZznwbFoP1tBm1/YPD2qcj9aaqmfUtEnAZcSnV5x2yqmxH8qKF4umHQ9mnQ9gcGc58GxaD9bQZtf2Dw9qnI/WkqaR9AdXedrXn88o6LgQsaiqcbBm2fBm1/YDD3aVAM2t9m0PYHBm+fityfxq7TliRJq6bxS74kSdLEmLQlSSqESVuSpEKYtCVJKoRJW5KkQvx/RxgWomE0EeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot multiple plots of the different wate categories\n",
    "df['log_vals'].hist(bins=10, by=df['WasteCategory'], \n",
    "                     alpha=0.8, figsize=(8,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute sum per category\n",
    "sum_byCategory= df.groupby(['WasteCategory']).sum('Quantity');\n",
    "\n",
    "#create new dataframe\n",
    "# Create the pandas DataFrame with column name is provided explicitly\n",
    "df_new = pd.DataFrame(sum_byCategory, columns=[ 'Quantity'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WasteCategory\n",
       "GLASS/CERAMIC             18\n",
       "METAL                      6\n",
       "PLASTICS                 847\n",
       "PROCESSED/WORKED WOOD      6\n",
       "RUBBER                     7\n",
       "TEXTILE                    5\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quantity = df_new['Quantity']\n",
    "Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 880x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    " plt.figure(figsize=(11,8), dpi= 80)\n",
    "\n",
    "# create dataset\n",
    "Quantity = df_new['Quantity']\n",
    "bars =['GLASS/CERAMIC', 'METAL','PLASTICS', 'PROCESSED WOOD','RUBBER', 'TEXTILE']\n",
    "\n",
    "x_pos = np.arange(len(bars))\n",
    " \n",
    "# Create bars and choose color\n",
    "plt.bar(x_pos, Quantity, color = ['g','b','r', 'deeppink','orange','dodgerblue'])\n",
    "\n",
    "# Add title and axis names\n",
    "plt.title('My title')\n",
    "plt.xlabel('categories')\n",
    "plt.ylabel('Total Quantity')\n",
    "# Create names on the x axis\n",
    "plt.xticks(x_pos, bars)\n",
    "plt.gca().set(title='Total Waste quantity by category (10/04/2022)/ Alberoni ')\n",
    "plt.legend(loc='upper right')\n",
    "plt.savefig('total_Waste_ditribution.png')\n",
    "# Show graph\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}