afval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1e98df03-8834-43b9-af2f-9943c3725715",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data_raw = pd.read_csv('afval_weging.csv', delimiter=';')\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7407fdd0-60ec-4db3-9b15-da6d585a9612",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_raw.copy()\n",
    "data['weging_datum_weging'] = pd.to_datetime(data['weging_datum_weging'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d549c0d6-f2d3-4c7a-a893-abce93dda2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['year'] = data['weging_datum_weging'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c13b3726-3f56-45bb-a166-ac7a4b7431ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Arek/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(2050497, 68)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.countplot(data['year'])\n",
    "plt.show()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "87c23e1f-90e5-4a4b-8c3d-664cf83e131d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1630590, 67)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data['weging_datum_weging'].between('2017-01-01','2020-12-31')]\n",
    "data.shape # 1.63M rows to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7e3ea9f0-557e-4a94-85d6-7a21c0083680",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.describe(include = 'all').T\n",
    "\n",
    "# cluster_id - some identification of the actual container, need to dig deeper\n",
    "# weging_id - unique identifier\n",
    "# weging_datum_weging - date of the weighing\n",
    "# weging_fractie_omschrijving\t- type of waste\n",
    "# weging_eerste_weging - first weighing (container + waste)\n",
    "# weging_tweede_weging - second weighing (empty container)\n",
    "# weging_netto_gewicht - the difference between two above, netto weight of the waste\n",
    "# weging_wgs84_lat - containers(cluster's in fact) latitude in WGS84 coordinate system\n",
    "# weging_wgs84_lon - containers(cluster's in fact) longitude in WGS84 coordinate system\n",
    "# gbd_ggwgebied_naam - district name\n",
    "# gbd_ggwgebied_code - district identifier (one of many; this will be used to match the economical/population data)\n",
    "# bag_adres_openbare_ruimte_naam - streetname of where the container is\n",
    "\n",
    "data = data[['cluster_id','weging_id','weging_datum_weging','weging_fractie_omschrijving','weging_eerste_weging','weging_tweede_weging','weging_netto_gewicht','weging_wgs84_lat','weging_wgs84_lon','gbd_ggwgebied_naam','gbd_ggwgebied_code','bag_adres_openbare_ruimte_naam']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32ef8008-0c76-4d64-bec1-6588529318bb",
   "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>cluster_id</th>\n",
       "      <th>weging_id</th>\n",
       "      <th>weging_datum_weging</th>\n",
       "      <th>weging_fractie_omschrijving</th>\n",
       "      <th>weging_eerste_weging</th>\n",
       "      <th>weging_tweede_weging</th>\n",
       "      <th>weging_netto_gewicht</th>\n",
       "      <th>weging_wgs84_lat</th>\n",
       "      <th>weging_wgs84_lon</th>\n",
       "      <th>gbd_ggwgebied_naam</th>\n",
       "      <th>gbd_ggwgebied_code</th>\n",
       "      <th>bag_adres_openbare_ruimte_naam</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128920.051|484940.826</td>\n",
       "      <td>10000~2017-02-17~09:12:03~26</td>\n",
       "      <td>2017-02-17</td>\n",
       "      <td>Rest</td>\n",
       "      <td>820</td>\n",
       "      <td>745.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>52.35202</td>\n",
       "      <td>5.00440</td>\n",
       "      <td>IJburg, Zeeburgereiland</td>\n",
       "      <td>DX16</td>\n",
       "      <td>Eva Besnyöstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>120064.880|486202.591</td>\n",
       "      <td>10000~2017-03-30~07:39:31~21</td>\n",
       "      <td>2017-03-30</td>\n",
       "      <td>Rest</td>\n",
       "      <td>685</td>\n",
       "      <td>585.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>52.36260</td>\n",
       "      <td>4.87426</td>\n",
       "      <td>Oud-West, De Baarsjes</td>\n",
       "      <td>DX05</td>\n",
       "      <td>Overtoom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10000~2017-03-31~11:18:50~15</td>\n",
       "      <td>2017-03-31</td>\n",
       "      <td>Rest</td>\n",
       "      <td>590</td>\n",
       "      <td>-225.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>52.36653</td>\n",
       "      <td>4.86935</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>126423.325|481859.655</td>\n",
       "      <td>10000~2017-05-08~07:48:44~1</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>Rest</td>\n",
       "      <td>1145</td>\n",
       "      <td>655.0</td>\n",
       "      <td>490.0</td>\n",
       "      <td>52.32406</td>\n",
       "      <td>4.96816</td>\n",
       "      <td>Bijlmer-Oost</td>\n",
       "      <td>DX21</td>\n",
       "      <td>Gravestein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>121060.889|482000.344</td>\n",
       "      <td>10000~2017-12-01~13:20:22~221</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>Rest</td>\n",
       "      <td>870</td>\n",
       "      <td>610.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>52.32489</td>\n",
       "      <td>4.88950</td>\n",
       "      <td>Buitenveldert, Zuidas</td>\n",
       "      <td>DX11</td>\n",
       "      <td>Van Boshuizenstraat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cluster_id                      weging_id weging_datum_weging  \\\n",
       "0  128920.051|484940.826   10000~2017-02-17~09:12:03~26          2017-02-17   \n",
       "1  120064.880|486202.591   10000~2017-03-30~07:39:31~21          2017-03-30   \n",
       "2                    NaN   10000~2017-03-31~11:18:50~15          2017-03-31   \n",
       "3  126423.325|481859.655    10000~2017-05-08~07:48:44~1          2017-05-08   \n",
       "4  121060.889|482000.344  10000~2017-12-01~13:20:22~221          2017-12-01   \n",
       "\n",
       "  weging_fractie_omschrijving  weging_eerste_weging  weging_tweede_weging  \\\n",
       "0                        Rest                   820                 745.0   \n",
       "1                        Rest                   685                 585.0   \n",
       "2                        Rest                   590                -225.0   \n",
       "3                        Rest                  1145                 655.0   \n",
       "4                        Rest                   870                 610.0   \n",
       "\n",
       "   weging_netto_gewicht  weging_wgs84_lat  weging_wgs84_lon  \\\n",
       "0                  75.0          52.35202           5.00440   \n",
       "1                 100.0          52.36260           4.87426   \n",
       "2                 815.0          52.36653           4.86935   \n",
       "3                 490.0          52.32406           4.96816   \n",
       "4                 260.0          52.32489           4.88950   \n",
       "\n",
       "        gbd_ggwgebied_naam gbd_ggwgebied_code bag_adres_openbare_ruimte_naam  \n",
       "0  IJburg, Zeeburgereiland               DX16               Eva Besnyöstraat  \n",
       "1    Oud-West, De Baarsjes               DX05                       Overtoom  \n",
       "2                      NaN                NaN                            NaN  \n",
       "3             Bijlmer-Oost               DX21                     Gravestein  \n",
       "4    Buitenveldert, Zuidas               DX11            Van Boshuizenstraat  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "e9e7dd6e-b3ba-4414-94c6-cd13f985c00c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1349239, 12)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nulls in:\n",
    "# cluster_id\n",
    "# bag_adres_openbare_ruimte_naam\t\n",
    "# bag_adres_nummeraanduiding_postcode\t\n",
    "# bag_openbareruimte_naam\t\n",
    "# gbd_stadsdeel_naam\t\n",
    "# gbd_wijk_naam\n",
    "\n",
    "data = data.dropna()\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data.shape # #1.35M left, acceptable & nulls cannot really be attributed back to their true locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "25654596-5824-4d28-95ae-263277f58af4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['calculated_net'] = data['weging_eerste_weging']-data['weging_tweede_weging']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "30955211-2c2b-44ab-9f13-206c40b5ff0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0       1247289\n",
       "-10.0         6645\n",
       "-20.0         3350\n",
       "-30.0         2269\n",
       "-40.0         1381\n",
       "            ...   \n",
       "-2195.0          1\n",
       "-2175.0          1\n",
       " 55.0            1\n",
       "-2155.0          1\n",
       "-3815.0          1\n",
       "Name: diff, Length: 976, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1349239, 14)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['diff'] = data['calculated_net'] - data['weging_netto_gewicht']\n",
    "display(data['diff'].value_counts())\n",
    "\n",
    "sns.displot(data['diff']) \n",
    "plt.show()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "cbd5eaa9-c6d9-481c-b1ba-2df053efee21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0.0       17224\n",
       " 175.0     16969\n",
       " 190.0     16859\n",
       " 180.0     16749\n",
       " 170.0     16680\n",
       "           ...  \n",
       " 2490.0        1\n",
       " 2885.0        1\n",
       " 3690.0        1\n",
       " 2725.0        1\n",
       "-3535.0        1\n",
       "Name: calculated_net, Length: 1262, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1349239, 14)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(data['calculated_net'].value_counts()) # use the calculated values and drop the negative outliers\n",
    "\n",
    "sns.displot(data['calculated_net'], bins = 50) \n",
    "plt.show()\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "74a633e0-cbfb-4bed-aa07-e54e62f370aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['calculated_net'] > 0] # exclude negatives.. makes sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "a90622f8-c3b6-40c6-86a6-70470f37d385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1228430, 14)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.displot(data['calculated_net'], bins = 50) \n",
    "plt.show()\n",
    "data.shape # Still at #1.2M, this will be enough"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "62cb5451-19fe-47f4-8ec9-d44091ba950f",
   "metadata": {},
   "outputs": [],
   "source": [
    "district = pd.read_excel('2020-22-gebieden-3-15.xlsx', engine='openpyxl')\n",
    "district = district.dropna()\n",
    "district.reset_index(drop=True,inplace=True)\n",
    "district = district.drop([0,23])\n",
    "district.columns = ['district_id','district_name','citizens_in_district','citizens_with_income','average_personal_income','number_of_households','average_household_income','ind']\n",
    "district = district.drop(['ind'], axis = 1)\n",
    "\n",
    "for column in district.columns[2:]:\n",
    "    district[column] = district[column].apply(lambda x: x*1000) \n",
    "district\n",
    "district.to_csv('district.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "56ff4246-23cb-4025-9cc5-31130f00fe33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['weging_eerste_weging','weging_tweede_weging','weging_netto_gewicht','diff'], axis = 1) # drop unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "aeb486ce-6b2b-4852-b225-923f5f41f0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Time for RegEx KING\n",
    "\n",
    "import re\n",
    "from datetime import datetime as dt\n",
    "\n",
    "pattern = '\\d{2}:\\d{2}:\\d{2}'\n",
    "data['time'] = 0\n",
    "data['time'] = data['weging_id'].apply(lambda x: dt.strptime(re.findall(pattern, x)[0], '%H:%M:%S').time())\n",
    "# created a pickup time from the unique 'weging_id' colummn, may be useful in time-analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "52ea1a06-2773-456d-950d-a811a26fe835",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mixed waste    1021708\n",
       "Paper           100798\n",
       "Plastic          65020\n",
       "Glass            40888\n",
       "Textiles            16\n",
       "Name: weging_fractie_omschrijving, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cleaning the waste type column\n",
    "import numpy as np\n",
    "\n",
    "data['weging_fractie_omschrijving'] = data['weging_fractie_omschrijving'].apply(lambda x: x.capitalize())\n",
    "\n",
    "data['weging_fractie_omschrijving'] = np.where(data['weging_fractie_omschrijving'] == 'Rest', 'Mixed waste', data['weging_fractie_omschrijving'])\n",
    "data['weging_fractie_omschrijving'] = np.where(data['weging_fractie_omschrijving'] == 'Papier', 'Paper', data['weging_fractie_omschrijving'])\n",
    "data['weging_fractie_omschrijving'] = np.where(data['weging_fractie_omschrijving'] == 'Glas', 'Glass', data['weging_fractie_omschrijving'])\n",
    "data['weging_fractie_omschrijving'] = np.where(data['weging_fractie_omschrijving'] == 'Kunststof', 'Plastic', data['weging_fractie_omschrijving'])\n",
    "data['weging_fractie_omschrijving'] = np.where(data['weging_fractie_omschrijving'] == 'Textiel', 'Textiles', data['weging_fractie_omschrijving'])\n",
    "display(data['weging_fractie_omschrijving'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f52c5d1c-e2ce-45fe-b52c-5f6967a11a25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change column names & order\n",
    "data.columns = ['cluster_id',\n",
    "                'waste_id',\n",
    "                'waste_date',\n",
    "                'waste_type',\n",
    "                'cluster_lat',\n",
    "                'cluster_lon',\n",
    "                'district_name',\n",
    "                'district_id',\n",
    "                'street_name',\n",
    "                'waste_weight',\n",
    "                'waste_time']\n",
    "\n",
    "data = data[['waste_id','cluster_id','cluster_lat','cluster_lon','waste_date','waste_time','waste_type','waste_weight','district_id','district_name','street_name']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "75860e5f-9a73-46fd-bb88-5485820f950d",
   "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>waste_id</th>\n",
       "      <th>cluster_id</th>\n",
       "      <th>cluster_lat</th>\n",
       "      <th>cluster_lon</th>\n",
       "      <th>waste_date</th>\n",
       "      <th>waste_time</th>\n",
       "      <th>waste_type</th>\n",
       "      <th>waste_weight</th>\n",
       "      <th>district_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>street_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000~2017-02-17~09:12:03~26</td>\n",
       "      <td>128920.051|484940.826</td>\n",
       "      <td>52.35202</td>\n",
       "      <td>5.00440</td>\n",
       "      <td>2017-02-17</td>\n",
       "      <td>09:12:03</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>75.0</td>\n",
       "      <td>DX16</td>\n",
       "      <td>IJburg, Zeeburgereiland</td>\n",
       "      <td>Eva Besnyöstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000~2017-03-30~07:39:31~21</td>\n",
       "      <td>120064.880|486202.591</td>\n",
       "      <td>52.36260</td>\n",
       "      <td>4.87426</td>\n",
       "      <td>2017-03-30</td>\n",
       "      <td>07:39:31</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>100.0</td>\n",
       "      <td>DX05</td>\n",
       "      <td>Oud-West, De Baarsjes</td>\n",
       "      <td>Overtoom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000~2017-05-08~07:48:44~1</td>\n",
       "      <td>126423.325|481859.655</td>\n",
       "      <td>52.32406</td>\n",
       "      <td>4.96816</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>07:48:44</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>490.0</td>\n",
       "      <td>DX21</td>\n",
       "      <td>Bijlmer-Oost</td>\n",
       "      <td>Gravestein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000~2017-12-01~13:20:22~221</td>\n",
       "      <td>121060.889|482000.344</td>\n",
       "      <td>52.32489</td>\n",
       "      <td>4.88950</td>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>13:20:22</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>260.0</td>\n",
       "      <td>DX11</td>\n",
       "      <td>Buitenveldert, Zuidas</td>\n",
       "      <td>Van Boshuizenstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000~2017-12-22~08:20:49~37</td>\n",
       "      <td>123789.260|485255.579</td>\n",
       "      <td>52.35434</td>\n",
       "      <td>4.92904</td>\n",
       "      <td>2017-12-22</td>\n",
       "      <td>08:20:49</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>160.0</td>\n",
       "      <td>DX15</td>\n",
       "      <td>Watergraafsmeer</td>\n",
       "      <td>Wakkerstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349233</th>\n",
       "      <td>9~2020-01-24~08:19:15~30</td>\n",
       "      <td>124324.612|485506.262</td>\n",
       "      <td>52.35671</td>\n",
       "      <td>4.93702</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>08:19:15</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>150.0</td>\n",
       "      <td>DX15</td>\n",
       "      <td>Watergraafsmeer</td>\n",
       "      <td>Newtonstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349234</th>\n",
       "      <td>1001~2020-06-30~09:12:52~17</td>\n",
       "      <td>119059.610|485875.456</td>\n",
       "      <td>52.35950</td>\n",
       "      <td>4.85941</td>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>09:12:52</td>\n",
       "      <td>Plastic</td>\n",
       "      <td>70.0</td>\n",
       "      <td>DX05</td>\n",
       "      <td>Oud-West, De Baarsjes</td>\n",
       "      <td>J.J. Cremerplein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349236</th>\n",
       "      <td>1001~2020-08-17~10:03:11~27</td>\n",
       "      <td>122807.846|484956.974</td>\n",
       "      <td>52.35159</td>\n",
       "      <td>4.91487</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>10:03:11</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>225.0</td>\n",
       "      <td>DX13</td>\n",
       "      <td>Oud-Oost</td>\n",
       "      <td>Hofmeyrstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349237</th>\n",
       "      <td>1001~2020-08-17~10:58:33~28</td>\n",
       "      <td>122558.468|485631.864</td>\n",
       "      <td>52.35764</td>\n",
       "      <td>4.91084</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>10:58:33</td>\n",
       "      <td>Mixed waste</td>\n",
       "      <td>130.0</td>\n",
       "      <td>DX13</td>\n",
       "      <td>Oud-Oost</td>\n",
       "      <td>Ruyschstraat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1349238</th>\n",
       "      <td>1001~2020-09-22~07:51:52~170</td>\n",
       "      <td>124087.635|486365.736</td>\n",
       "      <td>52.36438</td>\n",
       "      <td>4.93356</td>\n",
       "      <td>2020-09-22</td>\n",
       "      <td>07:51:52</td>\n",
       "      <td>Paper</td>\n",
       "      <td>160.0</td>\n",
       "      <td>DX14</td>\n",
       "      <td>Indische Buurt, Oostelijk Havengebied</td>\n",
       "      <td>Madurastraat</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1228430 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              waste_id             cluster_id  cluster_lat  \\\n",
       "0         10000~2017-02-17~09:12:03~26  128920.051|484940.826     52.35202   \n",
       "1         10000~2017-03-30~07:39:31~21  120064.880|486202.591     52.36260   \n",
       "2          10000~2017-05-08~07:48:44~1  126423.325|481859.655     52.32406   \n",
       "3        10000~2017-12-01~13:20:22~221  121060.889|482000.344     52.32489   \n",
       "4         10000~2017-12-22~08:20:49~37  123789.260|485255.579     52.35434   \n",
       "...                                ...                    ...          ...   \n",
       "1349233       9~2020-01-24~08:19:15~30  124324.612|485506.262     52.35671   \n",
       "1349234    1001~2020-06-30~09:12:52~17  119059.610|485875.456     52.35950   \n",
       "1349236    1001~2020-08-17~10:03:11~27  122807.846|484956.974     52.35159   \n",
       "1349237    1001~2020-08-17~10:58:33~28  122558.468|485631.864     52.35764   \n",
       "1349238   1001~2020-09-22~07:51:52~170  124087.635|486365.736     52.36438   \n",
       "\n",
       "         cluster_lon waste_date waste_time   waste_type  waste_weight  \\\n",
       "0            5.00440 2017-02-17   09:12:03  Mixed waste          75.0   \n",
       "1            4.87426 2017-03-30   07:39:31  Mixed waste         100.0   \n",
       "2            4.96816 2017-05-08   07:48:44  Mixed waste         490.0   \n",
       "3            4.88950 2017-12-01   13:20:22  Mixed waste         260.0   \n",
       "4            4.92904 2017-12-22   08:20:49  Mixed waste         160.0   \n",
       "...              ...        ...        ...          ...           ...   \n",
       "1349233      4.93702 2020-01-24   08:19:15  Mixed waste         150.0   \n",
       "1349234      4.85941 2020-06-30   09:12:52      Plastic          70.0   \n",
       "1349236      4.91487 2020-08-17   10:03:11  Mixed waste         225.0   \n",
       "1349237      4.91084 2020-08-17   10:58:33  Mixed waste         130.0   \n",
       "1349238      4.93356 2020-09-22   07:51:52        Paper         160.0   \n",
       "\n",
       "        district_id                          district_name  \\\n",
       "0              DX16                IJburg, Zeeburgereiland   \n",
       "1              DX05                  Oud-West, De Baarsjes   \n",
       "2              DX21                           Bijlmer-Oost   \n",
       "3              DX11                  Buitenveldert, Zuidas   \n",
       "4              DX15                        Watergraafsmeer   \n",
       "...             ...                                    ...   \n",
       "1349233        DX15                        Watergraafsmeer   \n",
       "1349234        DX05                  Oud-West, De Baarsjes   \n",
       "1349236        DX13                               Oud-Oost   \n",
       "1349237        DX13                               Oud-Oost   \n",
       "1349238        DX14  Indische Buurt, Oostelijk Havengebied   \n",
       "\n",
       "                 street_name  \n",
       "0           Eva Besnyöstraat  \n",
       "1                   Overtoom  \n",
       "2                 Gravestein  \n",
       "3        Van Boshuizenstraat  \n",
       "4               Wakkerstraat  \n",
       "...                      ...  \n",
       "1349233         Newtonstraat  \n",
       "1349234     J.J. Cremerplein  \n",
       "1349236        Hofmeyrstraat  \n",
       "1349237         Ruyschstraat  \n",
       "1349238         Madurastraat  \n",
       "\n",
       "[1228430 rows x 11 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "11283116-3dad-497f-bc0f-1b7333066a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ready to roll\n",
    "data.reset_index(drop=True, inplace=True)\n",
    "data.to_csv('data_clean.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ec092ed-90a2-4978-a889-4c792740a8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tableau Visualization of the above data\n",
    "# https://public.tableau.com/views/WhatDoRichDitch/WasteTypebyDistrict?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link"
   ]
  }
 ],
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