dashboard_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.16.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.16.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import chart_studio.plotly as py\n",
    "import plotly.graph_objects as go\n",
    "import cufflinks as cf\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import matplotlib as plt\n",
    "%matplotlib inline\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Type', 'Date', 'Part of a policing operation', 'Policing operation',\n",
       "       'Latitude', 'Longitude', 'Gender', 'Age range',\n",
       "       'Self-defined ethnicity', 'Officer-defined ethnicity', 'Legislation',\n",
       "       'Object of search', 'Outcome', 'Outcome linked to object of search',\n",
       "       'Removal of more than just outer clothing'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('2021-06-metropolitan-stop-and-search.csv')\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def criminality(df):\n",
    "    if df['Outcome'] == \"A no further action disposal\" or df['Outcome'] == \"Caution (simple or conditional)\":\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "df['criminality'] = df.apply(criminality, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ethnicity(df):\n",
    "    et = df['Self-defined ethnicity']\n",
    "    if \"White\" in et and (not 'Mixed' in et):\n",
    "        return 'White'\n",
    "    elif \"Black\" in et and (not 'Mixed' in et):\n",
    "        return 'Black'\n",
    "    elif \"Asian\" in et and (not 'Mixed' in et):\n",
    "        return 'Asian'\n",
    "    elif 'Mixed' in et:\n",
    "        return 'Mixed'\n",
    "    elif et == \"Other ethnic group - Any other ethnic group\":\n",
    "        return 'Other'\n",
    "    else:\n",
    "        return 'Not stated'\n",
    "\n",
    "df['Self-defined ethnicity'].fillna('Other ethnic group - Not stated', inplace=True)\n",
    "df['ethnicity'] = df.apply(ethnicity, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>5215</td>\n",
       "      <td>1559</td>\n",
       "      <td>3656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not stated</td>\n",
       "      <td>4324</td>\n",
       "      <td>1148</td>\n",
       "      <td>3496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black</td>\n",
       "      <td>4067</td>\n",
       "      <td>828</td>\n",
       "      <td>2919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2023</td>\n",
       "      <td>586</td>\n",
       "      <td>1437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mixed</td>\n",
       "      <td>580</td>\n",
       "      <td>197</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>476</td>\n",
       "      <td>144</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ethnicity  total  crime  nocrime\n",
       "0       White   5215   1559     3656\n",
       "1  Not stated   4324   1148     3496\n",
       "2       Black   4067    828     2919\n",
       "3       Asian   2023    586     1437\n",
       "4       Mixed    580    197      383\n",
       "5       Other    476    144      332"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = df['ethnicity'].value_counts()\n",
    "ethnicities = pd.DataFrame({'ethnicity':counts.index, 'total':counts.values})\n",
    "counts_nocrime =  df[df['criminality'] == 0]['ethnicity'].value_counts()\n",
    "counts_crime = df[df['criminality'] == 1]['ethnicity'].value_counts()\n",
    "ethnicities['crime'] = counts_crime.values\n",
    "ethnicities['nocrime'] = counts_nocrime.values\n",
    "ethnicities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>ethnicity</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "      <th>percentage_crime</th>\n",
       "      <th>percentage_nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White</td>\n",
       "      <td>5215</td>\n",
       "      <td>1559</td>\n",
       "      <td>3656</td>\n",
       "      <td>0.298945</td>\n",
       "      <td>0.701055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not stated</td>\n",
       "      <td>4324</td>\n",
       "      <td>1148</td>\n",
       "      <td>3496</td>\n",
       "      <td>0.265495</td>\n",
       "      <td>0.808511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Black</td>\n",
       "      <td>4067</td>\n",
       "      <td>828</td>\n",
       "      <td>2919</td>\n",
       "      <td>0.203590</td>\n",
       "      <td>0.717728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asian</td>\n",
       "      <td>2023</td>\n",
       "      <td>586</td>\n",
       "      <td>1437</td>\n",
       "      <td>0.289669</td>\n",
       "      <td>0.710331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mixed</td>\n",
       "      <td>580</td>\n",
       "      <td>197</td>\n",
       "      <td>383</td>\n",
       "      <td>0.339655</td>\n",
       "      <td>0.660345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Other</td>\n",
       "      <td>476</td>\n",
       "      <td>144</td>\n",
       "      <td>332</td>\n",
       "      <td>0.302521</td>\n",
       "      <td>0.697479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ethnicity  total  crime  nocrime  percentage_crime  percentage_nocrime\n",
       "0       White   5215   1559     3656          0.298945            0.701055\n",
       "1  Not stated   4324   1148     3496          0.265495            0.808511\n",
       "2       Black   4067    828     2919          0.203590            0.717728\n",
       "3       Asian   2023    586     1437          0.289669            0.710331\n",
       "4       Mixed    580    197      383          0.339655            0.660345\n",
       "5       Other    476    144      332          0.302521            0.697479"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ethnicities['percentage_crime'] = ethnicities['crime'] / ethnicities['total'] \n",
    "ethnicities['percentage_nocrime'] = ethnicities['nocrime'] / ethnicities['total'] \n",
    "ethnicities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>gender</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "      <th>percentage_crime</th>\n",
       "      <th>percentage_nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>15182</td>\n",
       "      <td>4048</td>\n",
       "      <td>11134</td>\n",
       "      <td>0.266632</td>\n",
       "      <td>0.733368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Female</td>\n",
       "      <td>1285</td>\n",
       "      <td>332</td>\n",
       "      <td>953</td>\n",
       "      <td>0.258366</td>\n",
       "      <td>0.741634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not stated</td>\n",
       "      <td>213</td>\n",
       "      <td>82</td>\n",
       "      <td>131</td>\n",
       "      <td>0.384977</td>\n",
       "      <td>0.615023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Other</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       gender  total  crime  nocrime  percentage_crime  percentage_nocrime\n",
       "0        Male  15182   4048    11134          0.266632            0.733368\n",
       "1      Female   1285    332      953          0.258366            0.741634\n",
       "2  Not stated    213     82      131          0.384977            0.615023\n",
       "3       Other      5      0        5          0.000000            1.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].fillna('Not stated', inplace=True)\n",
    "counts = df['Gender'].value_counts()\n",
    "genders = pd.DataFrame({'gender':counts.index, 'total':counts.values})\n",
    "counts_nocrime =  df[df['criminality'] == 0]['Gender'].value_counts()\n",
    "counts_crime = df[df['criminality'] == 1]['Gender'].value_counts().reindex(df['Gender'].unique(), fill_value=0)\n",
    "genders['crime'] = counts_crime.values\n",
    "genders['nocrime'] = counts_nocrime.values\n",
    "genders['percentage_crime'] = genders['crime'] / genders['total'] \n",
    "genders['percentage_nocrime'] = genders['nocrime'] / genders['total'] \n",
    "genders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>age</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "      <th>percentage_crime</th>\n",
       "      <th>percentage_nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-24</td>\n",
       "      <td>5282</td>\n",
       "      <td>1677</td>\n",
       "      <td>3605</td>\n",
       "      <td>0.317493</td>\n",
       "      <td>0.682507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25-34</td>\n",
       "      <td>3632</td>\n",
       "      <td>1262</td>\n",
       "      <td>2370</td>\n",
       "      <td>0.347467</td>\n",
       "      <td>0.652533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>over 34</td>\n",
       "      <td>3146</td>\n",
       "      <td>883</td>\n",
       "      <td>2263</td>\n",
       "      <td>0.280674</td>\n",
       "      <td>0.719326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10-17</td>\n",
       "      <td>2422</td>\n",
       "      <td>461</td>\n",
       "      <td>2023</td>\n",
       "      <td>0.190339</td>\n",
       "      <td>0.835260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Not stated</td>\n",
       "      <td>2201</td>\n",
       "      <td>178</td>\n",
       "      <td>1961</td>\n",
       "      <td>0.080872</td>\n",
       "      <td>0.890959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>under 10</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  total  crime  nocrime  percentage_crime  percentage_nocrime\n",
       "0       18-24   5282   1677     3605          0.317493            0.682507\n",
       "1       25-34   3632   1262     2370          0.347467            0.652533\n",
       "2     over 34   3146    883     2263          0.280674            0.719326\n",
       "3       10-17   2422    461     2023          0.190339            0.835260\n",
       "4  Not stated   2201    178     1961          0.080872            0.890959\n",
       "5    under 10      2      1        1          0.500000            0.500000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age range'].fillna('Not stated', inplace=True)\n",
    "counts = df['Age range'].value_counts()\n",
    "ages = pd.DataFrame({'age':counts.index, 'total':counts.values})\n",
    "counts_nocrime =  df[df['criminality'] == 0]['Age range'].value_counts()\n",
    "counts_crime = df[df['criminality'] == 1]['Age range'].value_counts()\n",
    "ages['crime'] = counts_crime.values\n",
    "ages['nocrime'] = counts_nocrime.values\n",
    "ages['percentage_crime'] = ages['crime'] / ages['total'] \n",
    "ages['percentage_nocrime'] = ages['nocrime'] / ages['total'] \n",
    "ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": "#E45756"
         },
         "name": "Crime",
         "text": [
          "29.89%",
          "26.55%",
          "20.36%",
          "28.97%",
          "33.97%",
          "30.25%"
         ],
         "type": "bar",
         "x": [
          "White",
          "Not stated",
          "Black",
          "Asian",
          "Mixed",
          "Other"
         ],
         "y": [
          1559,
          1148,
          828,
          586,
          197,
          144
         ]
        },
        {
         "marker": {
          "color": "#54A54B"
         },
         "name": "Non-crime",
         "text": [
          "70.11%",
          "80.85%",
          "71.77%",
          "71.03%",
          "66.03%",
          "69.75%"
         ],
         "type": "bar",
         "x": [
          "White",
          "Not stated",
          "Black",
          "Asian",
          "Mixed",
          "Other"
         ],
         "y": [
          3656,
          3496,
          2919,
          1437,
          383,
          332
         ]
        },
        {
         "marker": {
          "color": "#4C78A8"
         },
         "name": "Total",
         "type": "bar",
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   "source": [
    "trace1 = go.Bar(\n",
    "    x=ethnicities['ethnicity'],\n",
    "    y=ethnicities['crime'],\n",
    "    text=ethnicities['percentage_crime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Crime',\n",
    "    marker_color=\"#E45756\",\n",
    ")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=ethnicities['ethnicity'],\n",
    "    y=ethnicities['nocrime'],\n",
    "    text=ethnicities['percentage_nocrime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Non-crime',\n",
    "    marker_color=\"#54A54B\"\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=ethnicities['ethnicity'],\n",
    "    y=ethnicities['total'],\n",
    "    name='Total',\n",
    "    marker_color=\"#4C78A8\"\n",
    ")\n",
    "layout = go.Layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            type=\"dropdown\",\n",
    "            active=0,\n",
    "            xanchor=\"right\",\n",
    "            yanchor=\"top\",\n",
    "            direction=\"down\",\n",
    "            buttons=list([\n",
    "                dict(label=\"Summary\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, True, True]\n",
    "                     },\n",
    "                     {\"title\": \"Summary of stop and searches by ethnicity\",\n",
    "                     }]),\n",
    "                dict(label=\"Crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, False, False]},\n",
    "                           {\"title\": \"Criminal officer judgment stop and searches by ethnicity\"}]),\n",
    "                dict(label=\"Non-crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, True, False]},\n",
    "                     {\"title\": \"Non-criminal officer judgment top and searches with by ethnicity\"}]),\n",
    "                dict(label=\"Total\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, False, True]\n",
    "                     },\n",
    "                     {\"title\": \"Total stop and searches by ethnicity\",\n",
    "                     }])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Ethnicity\",\n",
    "    yaxis_title=\"Number of People\",\n",
    "    title=\"Summary of stop and searches by ethnicity\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "trace1 = go.Bar(\n",
    "    x=genders['gender'],\n",
    "    y=genders['crime'],\n",
    "    text=genders['percentage_crime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Crime',\n",
    "    marker_color=\"#E45756\",\n",
    ")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=genders['gender'],\n",
    "    y=genders['nocrime'],\n",
    "    text=genders['percentage_nocrime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Non-crime',\n",
    "    marker_color=\"#54A54B\"\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=genders['gender'],\n",
    "    y=genders['total'],\n",
    "    name='Total',\n",
    "    marker_color=\"#4C78A8\"\n",
    ")\n",
    "layout = go.Layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            type=\"dropdown\",\n",
    "            active=0,\n",
    "            xanchor=\"right\",\n",
    "            yanchor=\"top\",\n",
    "            direction=\"down\",\n",
    "            buttons=list([\n",
    "                dict(label=\"Summary\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, True, True]\n",
    "                     },\n",
    "                     {\"title\": \"Summary of stop and searches by gender\",\n",
    "                     }]),\n",
    "                dict(label=\"Crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, False, False]},\n",
    "                           {\"title\": \"Criminal officer judgment stop and searches by gender\"}]),\n",
    "                dict(label=\"Non-crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, True, False]},\n",
    "                     {\"title\": \"Non-criminal officer judgment top and searches with by gender\"}]),\n",
    "                dict(label=\"Total\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, False, True]\n",
    "                     },\n",
    "                     {\"title\": \"Total stop and searches by gender\",\n",
    "                     }])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Gender\",\n",
    "    yaxis_title=\"Number of People\",\n",
    "    title=\"Summary of stop and searches by gender\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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   "outputs": [
    {
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=ages['age'],\n",
    "    y=ages['crime'],\n",
    "    text=ages['percentage_crime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Crime',\n",
    "    marker_color=\"#E45756\",\n",
    ")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=ages['age'],\n",
    "    y=ages['nocrime'],\n",
    "    text=ages['percentage_nocrime'].apply(lambda x: '{percent:.2%}'.format(percent=x)),\n",
    "    name='Non-crime',\n",
    "    marker_color=\"#54A54B\"\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=ages['age'],\n",
    "    y=ages['total'],\n",
    "    name='Total',\n",
    "    marker_color=\"#4C78A8\"\n",
    ")\n",
    "layout = go.Layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            type=\"dropdown\",\n",
    "            active=0,\n",
    "            xanchor=\"right\",\n",
    "            yanchor=\"top\",\n",
    "            direction=\"down\",\n",
    "            buttons=list([\n",
    "                dict(label=\"Summary\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, True, True]\n",
    "                     },\n",
    "                     {\"title\": \"Summary of stop and searches by age\",\n",
    "                     }]),\n",
    "                dict(label=\"Crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, False, False]},\n",
    "                           {\"title\": \"Criminal officer judgment stop and searches by age\"}]),\n",
    "                dict(label=\"Non-crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, True, False]},\n",
    "                     {\"title\": \"Non-criminal officer judgment top and searches with by age\"}]),\n",
    "                dict(label=\"Total\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, False, True]\n",
    "                     },\n",
    "                     {\"title\": \"Total stop and searches by age\",\n",
    "                     }])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Age range\",\n",
    "    yaxis_title=\"Number of People\",\n",
    "    title=\"Summary of stop and searches by age\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "df[\"weekday\"] = df[\"Date\"].dt.weekday\n",
    "df['date'] = df['Date'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>734</td>\n",
       "      <td>206</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>679</td>\n",
       "      <td>197</td>\n",
       "      <td>504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>666</td>\n",
       "      <td>195</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>662</td>\n",
       "      <td>193</td>\n",
       "      <td>478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>648</td>\n",
       "      <td>174</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>634</td>\n",
       "      <td>171</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-06-09</td>\n",
       "      <td>626</td>\n",
       "      <td>171</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-06-03</td>\n",
       "      <td>623</td>\n",
       "      <td>165</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>620</td>\n",
       "      <td>158</td>\n",
       "      <td>455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>614</td>\n",
       "      <td>157</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>596</td>\n",
       "      <td>156</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>584</td>\n",
       "      <td>153</td>\n",
       "      <td>429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>583</td>\n",
       "      <td>152</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>582</td>\n",
       "      <td>150</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>554</td>\n",
       "      <td>150</td>\n",
       "      <td>416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>552</td>\n",
       "      <td>147</td>\n",
       "      <td>413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>550</td>\n",
       "      <td>146</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>546</td>\n",
       "      <td>142</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>532</td>\n",
       "      <td>141</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>531</td>\n",
       "      <td>138</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-06-26</td>\n",
       "      <td>530</td>\n",
       "      <td>135</td>\n",
       "      <td>382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>516</td>\n",
       "      <td>134</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>505</td>\n",
       "      <td>134</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>494</td>\n",
       "      <td>127</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>457</td>\n",
       "      <td>124</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>438</td>\n",
       "      <td>122</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>431</td>\n",
       "      <td>121</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>426</td>\n",
       "      <td>105</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>394</td>\n",
       "      <td>104</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>337</td>\n",
       "      <td>82</td>\n",
       "      <td>255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2021-05-31</td>\n",
       "      <td>41</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  total  crime  nocrime\n",
       "0   2021-06-11    734    206      541\n",
       "1   2021-06-05    679    197      504\n",
       "2   2021-06-25    666    195      483\n",
       "3   2021-06-24    662    193      478\n",
       "4   2021-06-04    648    174      473\n",
       "5   2021-06-19    634    171      470\n",
       "6   2021-06-09    626    171      469\n",
       "7   2021-06-03    623    165      455\n",
       "8   2021-06-10    620    158      455\n",
       "9   2021-06-23    614    157      449\n",
       "10  2021-06-07    596    156      436\n",
       "11  2021-06-17    584    153      429\n",
       "12  2021-06-02    583    152      422\n",
       "13  2021-06-22    582    150      419\n",
       "14  2021-06-06    554    150      416\n",
       "15  2021-06-12    552    147      413\n",
       "16  2021-06-01    550    146      398\n",
       "17  2021-06-18    546    142      397\n",
       "18  2021-06-08    532    141      395\n",
       "19  2021-06-16    531    138      384\n",
       "20  2021-06-26    530    135      382\n",
       "21  2021-06-30    516    134      374\n",
       "22  2021-06-15    505    134      370\n",
       "23  2021-06-28    494    127      360\n",
       "24  2021-06-21    457    124      353\n",
       "25  2021-06-20    438    122      311\n",
       "26  2021-06-29    431    121      309\n",
       "27  2021-06-14    426    105      305\n",
       "28  2021-06-13    394    104      289\n",
       "29  2021-06-27    337     82      255\n",
       "30  2021-05-31     41     12       29"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_counts = df['date'].value_counts()\n",
    "dates = pd.DataFrame({'date':date_counts.index, 'total':date_counts.values})\n",
    "counts_nocrime =  df[df['criminality'] == 0]['date'].value_counts()\n",
    "counts_crime = df[df['criminality'] == 1]['date'].value_counts()\n",
    "dates['crime'] = counts_crime.values\n",
    "dates['nocrime'] = counts_nocrime.values\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     },
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   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=dates['date'],\n",
    "    y=dates['crime'],\n",
    "    name='Crime',\n",
    "    marker_color=\"#E45756\",\n",
    ")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=dates['date'],\n",
    "    y=dates['nocrime'],\n",
    "    name='Non-crime',\n",
    "    marker_color=\"#54A54B\"\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=dates['date'],\n",
    "    y=dates['total'],\n",
    "    name='Total',\n",
    "    marker_color=\"#4C78A8\"\n",
    ")\n",
    "layout = go.Layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            type=\"dropdown\",\n",
    "            active=0,\n",
    "            xanchor=\"right\",\n",
    "            yanchor=\"top\",\n",
    "            direction=\"down\",\n",
    "            buttons=list([\n",
    "                dict(label=\"Summary\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, True, True]\n",
    "                     },\n",
    "                     {\"title\": \"Summary of stop and searches through time\",\n",
    "                     }]),\n",
    "                dict(label=\"Crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, False, False]},\n",
    "                           {\"title\": \"Criminal officer judgment stop and searches through time\"}]),\n",
    "                dict(label=\"Non-crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, True, False]},\n",
    "                     {\"title\": \"Non-criminal officer judgment top and searches through time\"}]),\n",
    "                dict(label=\"Total\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, False, True]\n",
    "                     },\n",
    "                     {\"title\": \"Total stop and searches through time\",\n",
    "                     }])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Date\",\n",
    "    yaxis_title=\"Number of Stop and Searches\",\n",
    "    title=\"Summary of stop and searches through time\"\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weekday</th>\n",
       "      <th>total</th>\n",
       "      <th>crime</th>\n",
       "      <th>nocrime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2870</td>\n",
       "      <td>789</td>\n",
       "      <td>2081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2600</td>\n",
       "      <td>712</td>\n",
       "      <td>1915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2594</td>\n",
       "      <td>679</td>\n",
       "      <td>1888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2489</td>\n",
       "      <td>665</td>\n",
       "      <td>1836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2395</td>\n",
       "      <td>653</td>\n",
       "      <td>1730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2014</td>\n",
       "      <td>512</td>\n",
       "      <td>1502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1723</td>\n",
       "      <td>452</td>\n",
       "      <td>1271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   weekday  total  crime  nocrime\n",
       "0        2   2870    789     2081\n",
       "1        1   2600    712     1915\n",
       "2        4   2594    679     1888\n",
       "3        3   2489    665     1836\n",
       "4        5   2395    653     1730\n",
       "5        0   2014    512     1502\n",
       "6        6   1723    452     1271"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "weekday_counts = df['weekday'].value_counts()\n",
    "weekdays = pd.DataFrame({'weekday':weekday_counts.index, 'total':weekday_counts.values})\n",
    "counts_nocrime =  df[df['criminality'] == 0]['weekday'].value_counts()\n",
    "counts_crime = df[df['criminality'] == 1]['weekday'].value_counts()\n",
    "weekdays['crime'] = counts_crime.values\n",
    "weekdays['nocrime'] = counts_nocrime.values\n",
    "weekdays"
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  },
  {
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   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=weekdays['weekday'],\n",
    "    y=weekdays['crime'],\n",
    "    name='Crime',\n",
    "    marker_color=\"#E45756\",\n",
    ")\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x=weekdays['weekday'],\n",
    "    y=weekdays['nocrime'],\n",
    "    name='Non-crime',\n",
    "    marker_color=\"#54A54B\"\n",
    ")\n",
    "\n",
    "trace3 = go.Bar(\n",
    "    x=weekdays['weekday'],\n",
    "    y=weekdays['total'],\n",
    "    name='Total',\n",
    "    marker_color=\"#4C78A8\"\n",
    ")\n",
    "layout = go.Layout(\n",
    "    updatemenus=[\n",
    "        dict(\n",
    "            type=\"dropdown\",\n",
    "            active=0,\n",
    "            xanchor=\"right\",\n",
    "            yanchor=\"top\",\n",
    "            direction=\"down\",\n",
    "            buttons=list([\n",
    "                dict(label=\"Summary\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, True, True]\n",
    "                     },\n",
    "                     {\"title\": \"Summary of stop and searches through time\",\n",
    "                     }]),\n",
    "                dict(label=\"Crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [True, False, False]},\n",
    "                           {\"title\": \"Criminal officer judgment stop and searches through time\"}]),\n",
    "                dict(label=\"Non-crime\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, True, False]},\n",
    "                     {\"title\": \"Non-criminal officer judgment top and searches through time\"}]),\n",
    "                dict(label=\"Total\",\n",
    "                     method=\"update\",\n",
    "                     args=[{\"visible\": [False, False, True]\n",
    "                     },\n",
    "                     {\"title\": \"Total stop and searches through time\",\n",
    "                     }])\n",
    "            ]),\n",
    "        )\n",
    "    ])\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
    "\n",
    "fig.update_layout(\n",
    "    xaxis_title=\"Weekday\",\n",
    "    yaxis_title=\"Number of Stop and Searches\",\n",
    "    title=\"Summary of stop and searches through time\",   \n",
    ")\n",
    "fig.update_xaxes(categoryorder='array', categoryarray= ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])\n",
    "fig.show()"
   ]
  }
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