Toronto tick data

TICKS (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import urllib\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "# Retrieve the metadata for this dataset:\n",
    "\n",
    "url = \"https://ckan0.cf.opendata.inter.prod-toronto.ca/api/3/action/package_show\"\n",
    "params = { \"id\": \"78c88292-5375-4373-a687-788a5ff19077\", \"limit\": \"1000\"}\n",
    "response = urllib.request.urlopen(url, data=bytes(json.dumps(params), encoding=\"utf-8\"))\n",
    "package = json.loads(response.read())\n",
    "\n",
    "#print(package[\"result\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#retrieve the data content for the first resource in the datastore:\n",
    "\n",
    "for idx, resource in enumerate(package[\"result\"][\"resources\"]):\n",
    "    #if resource[\"datastore_active\"]:\n",
    "        url = \"https://ckan0.cf.opendata.inter.prod-toronto.ca/api/3/action/datastore_search\"\n",
    "        p = { \"id\": resource[\"id\"], \"limit\" : \"1000\"}\n",
    "        r = urllib.request.urlopen(url, data=bytes(json.dumps(p), encoding=\"utf-8\"))\n",
    "        data = json.loads(r.read())\n",
    "        df = pd.DataFrame(data[\"result\"][\"records\"])\n",
    "    \n",
    "        break\n",
    "        \n",
    "#Here is the data        \n",
    "#df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 191 entries, 0 to 190\n",
      "Data columns (total 9 columns):\n",
      "# Positive               191 non-null int64\n",
      "BLT Adults and Nymphs    191 non-null int64\n",
      "BLT Larvae               191 non-null int64\n",
      "Latitude                 191 non-null float64\n",
      "Longitude                191 non-null float64\n",
      "Park Location            191 non-null object\n",
      "Total BLTs               191 non-null int64\n",
      "Year                     191 non-null int64\n",
      "_id                      191 non-null int64\n",
      "dtypes: float64(2), int64(6), object(1)\n",
      "memory usage: 13.5+ KB\n"
     ]
    },
    {
     "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># Positive</th>\n",
       "      <th>BLT Adults and Nymphs</th>\n",
       "      <th>BLT Larvae</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Total BLTs</th>\n",
       "      <th>Year</th>\n",
       "      <th>_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>191.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.879581</td>\n",
       "      <td>2.539267</td>\n",
       "      <td>0.115183</td>\n",
       "      <td>43.714954</td>\n",
       "      <td>-79.354375</td>\n",
       "      <td>2.539267</td>\n",
       "      <td>2017.094241</td>\n",
       "      <td>96.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.547987</td>\n",
       "      <td>11.763336</td>\n",
       "      <td>0.938829</td>\n",
       "      <td>0.059057</td>\n",
       "      <td>0.129287</td>\n",
       "      <td>11.763336</td>\n",
       "      <td>1.444233</td>\n",
       "      <td>55.2811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.602787</td>\n",
       "      <td>-79.610324</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.660574</td>\n",
       "      <td>-79.459314</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>48.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.724281</td>\n",
       "      <td>-79.355428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>96.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.756853</td>\n",
       "      <td>-79.221507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>143.5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>43.811851</td>\n",
       "      <td>-79.151299</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>191.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       # Positive  BLT Adults and Nymphs  BLT Larvae    Latitude   Longitude  \\\n",
       "count  191.000000             191.000000  191.000000  191.000000  191.000000   \n",
       "mean     0.879581               2.539267    0.115183   43.714954  -79.354375   \n",
       "std      5.547987              11.763336    0.938829    0.059057    0.129287   \n",
       "min      0.000000               0.000000    0.000000   43.602787  -79.610324   \n",
       "25%      0.000000               0.000000    0.000000   43.660574  -79.459314   \n",
       "50%      0.000000               0.000000    0.000000   43.724281  -79.355428   \n",
       "75%      0.000000               1.000000    0.000000   43.756853  -79.221507   \n",
       "max     63.000000             122.000000    9.000000   43.811851  -79.151299   \n",
       "\n",
       "       Total BLTs         Year       _id  \n",
       "count  191.000000   191.000000  191.0000  \n",
       "mean     2.539267  2017.094241   96.0000  \n",
       "std     11.763336     1.444233   55.2811  \n",
       "min      0.000000  2013.000000    1.0000  \n",
       "25%      0.000000  2016.000000   48.5000  \n",
       "50%      0.000000  2017.000000   96.0000  \n",
       "75%      1.000000  2018.000000  143.5000  \n",
       "max    122.000000  2019.000000  191.0000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2013-01-01\n",
       "1     2014-01-01\n",
       "2     2014-01-01\n",
       "3     2014-01-01\n",
       "4     2014-01-01\n",
       "5     2015-01-01\n",
       "6     2015-01-01\n",
       "7     2015-01-01\n",
       "8     2015-01-01\n",
       "9     2015-01-01\n",
       "10    2015-01-01\n",
       "11    2015-01-01\n",
       "12    2015-01-01\n",
       "13    2015-01-01\n",
       "14    2015-01-01\n",
       "15    2015-01-01\n",
       "16    2015-01-01\n",
       "17    2015-01-01\n",
       "18    2015-01-01\n",
       "19    2015-01-01\n",
       "20    2015-01-01\n",
       "21    2015-01-01\n",
       "22    2015-01-01\n",
       "23    2015-01-01\n",
       "24    2015-01-01\n",
       "25    2015-01-01\n",
       "26    2015-01-01\n",
       "27    2015-01-01\n",
       "28    2015-01-01\n",
       "29    2015-01-01\n",
       "         ...    \n",
       "161   2019-01-01\n",
       "162   2019-01-01\n",
       "163   2019-01-01\n",
       "164   2019-01-01\n",
       "165   2019-01-01\n",
       "166   2019-01-01\n",
       "167   2019-01-01\n",
       "168   2019-01-01\n",
       "169   2019-01-01\n",
       "170   2019-01-01\n",
       "171   2019-01-01\n",
       "172   2019-01-01\n",
       "173   2019-01-01\n",
       "174   2019-01-01\n",
       "175   2019-01-01\n",
       "176   2019-01-01\n",
       "177   2019-01-01\n",
       "178   2019-01-01\n",
       "179   2019-01-01\n",
       "180   2019-01-01\n",
       "181   2019-01-01\n",
       "182   2019-01-01\n",
       "183   2019-01-01\n",
       "184   2019-01-01\n",
       "185   2019-01-01\n",
       "186   2019-01-01\n",
       "187   2019-01-01\n",
       "188   2019-01-01\n",
       "189   2019-01-01\n",
       "190   2019-01-01\n",
       "Name: Year, Length: 191, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Changing the year to date time \n",
    "pd.to_datetime(df.Year, format='%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ef8be48>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ef05588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#let's look at the overall trend in the GTA\n",
    "annual =df.groupby([\"Year\"]).agg({\"# Positive\":\"sum\", \"Total BLTs\": \"sum\"})\n",
    "\n",
    "\n",
    "\n",
    "annual.plot(kind = \"area\", stacked = False, title = 'Black Legged Ticks (BLTs) in the GTA - Positive for Lyme vs Total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th># Positive</th>\n",
       "      <th>Total BLTs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Park Location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <th>Algonquin Island</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2014</th>\n",
       "      <th>Algonquin Island</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Danforth Birchmount Parkette</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Park</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wards Island</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">2015</th>\n",
       "      <th>Algonquin Island</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bob Hunter Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brimley Woods Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cedar Ridge Park</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centre Island</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charles Sauriol Conservation Area</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cudia Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Etobicoke Creek</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenvale Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grey Abbey Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guildwood Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hanlan's Point</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humber River</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joyce Trimmer Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lavender Creek</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leslie Grove Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Milkman’s Lane</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morningside Park</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Panorama Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pine Point Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Park</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rowntree Mills Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Marine Drive Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunnybrook Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvan Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">2019</th>\n",
       "      <th>Cudia Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earl Bales Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. Ross Lord Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greenvale Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guild Park and Gardens</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High Park</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Highland Creek Park</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hinder Area</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humberwood Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lambton Park</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Magwood Park</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>McCowan Park</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moore Ravine Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morningside Park</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pine Point Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Park: Glen Eagle Vista Trail</th>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rowntree Mills Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scarborough Crescent Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sherwood Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunnybrook Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sylvan Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taylor Creek Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Islands: Algonquin Island Park</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Islands: Centre Island</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Islands: Ward's</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Upper Rouge Trail Park</th>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Warden Woods Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Deane Park (North)</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wilket Creek Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windfields Park</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>191 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             # Positive  Total BLTs\n",
       "Year Park Location                                                 \n",
       "2013 Algonquin Island                                 0           1\n",
       "2014 Algonquin Island                                 0           0\n",
       "     Danforth Birchmount Parkette                     0           0\n",
       "     Rouge Park                                       0           3\n",
       "     Wards Island                                     0           0\n",
       "2015 Algonquin Island                                 0           2\n",
       "     Bob Hunter Park                                  0           0\n",
       "     Brimley Woods Park                               0           0\n",
       "     Cedar Ridge Park                                 0           1\n",
       "     Centre Island                                    0           0\n",
       "     Charles Sauriol Conservation Area                0           0\n",
       "     Cudia Park                                       0           0\n",
       "     Etobicoke Creek                                  0           0\n",
       "     Greenvale Park                                   0           0\n",
       "     Grey Abbey Park                                  0           0\n",
       "     Guildwood Park                                   0           0\n",
       "     Hanlan's Point                                   0           0\n",
       "     Humber River                                     0           0\n",
       "     Joyce Trimmer Park                               0           0\n",
       "     Lavender Creek                                   0           0\n",
       "     Leslie Grove Park                                0           0\n",
       "     Milkman’s Lane                                   0           0\n",
       "     Morningside Park                                 0           4\n",
       "     Panorama Park                                    0           0\n",
       "     Pine Point Park                                  0           0\n",
       "     Rouge Park                                       1          10\n",
       "     Rowntree Mills Park                              0           0\n",
       "     South Marine Drive Park                          0           0\n",
       "     Sunnybrook Park                                  0           0\n",
       "     Sylvan Park                                      0           0\n",
       "...                                                 ...         ...\n",
       "2019 Cudia Park                                       0           0\n",
       "     Earl Bales Park                                  0           0\n",
       "     G. Ross Lord Park                                0           0\n",
       "     Greenvale Park                                   0           0\n",
       "     Guild Park and Gardens                           0           1\n",
       "     High Park                                        0           2\n",
       "     Highland Creek Park                              0           2\n",
       "     Hinder Area                                      0           0\n",
       "     Humberwood Park                                  0           0\n",
       "     Lambton Park                                     0           1\n",
       "     Magwood Park                                     0           1\n",
       "     McCowan Park                                     0           1\n",
       "     Moore Ravine Park                                0           0\n",
       "     Morningside Park                                 1           6\n",
       "     Pine Point Park                                  0           0\n",
       "     Rouge Park: Glen Eagle Vista Trail              25          52\n",
       "     Rowntree Mills Park                              0           0\n",
       "     Scarborough Crescent Park                        0           0\n",
       "     Sherwood Park                                    0           0\n",
       "     Sunnybrook Park                                  0           0\n",
       "     Sylvan Park                                      0           0\n",
       "     Taylor Creek Park                                0           0\n",
       "     Toronto Islands: Algonquin Island Park           0           7\n",
       "     Toronto Islands: Centre Island                   0           1\n",
       "     Toronto Islands: Ward's                          0           1\n",
       "     Upper Rouge Trail Park                           9          39\n",
       "     Warden Woods Park                                0           0\n",
       "     West Deane Park (North)                          1           1\n",
       "     Wilket Creek Park                                0           0\n",
       "     Windfields Park                                  0           0\n",
       "\n",
       "[191 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now lets zoom in to see what's up in each park\n",
    "\n",
    "by_park= df.groupby([\"Year\",\"Park Location\"]).agg({\"# Positive\":\"sum\", \"Total BLTs\": \"sum\"})\n",
    "by_park"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Positive</th>\n",
       "      <th>BLT Adults and Nymphs</th>\n",
       "      <th>BLT Larvae</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Park Location</th>\n",
       "      <th>Total BLTs</th>\n",
       "      <th>Year</th>\n",
       "      <th>_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>43.811851</td>\n",
       "      <td>-79.151299</td>\n",
       "      <td>Rouge Park</td>\n",
       "      <td>10</td>\n",
       "      <td>2015</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>43.626120</td>\n",
       "      <td>-79.360567</td>\n",
       "      <td>Algonquin Island</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>43.780349</td>\n",
       "      <td>-79.178148</td>\n",
       "      <td>Colonel Danforth Trail</td>\n",
       "      <td>8</td>\n",
       "      <td>2016</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>43.776814</td>\n",
       "      <td>-79.199891</td>\n",
       "      <td>Morningside Park</td>\n",
       "      <td>14</td>\n",
       "      <td>2016</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>29</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>43.811851</td>\n",
       "      <td>-79.151299</td>\n",
       "      <td>Rouge Park</td>\n",
       "      <td>63</td>\n",
       "      <td>2016</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>43.776814</td>\n",
       "      <td>-79.199891</td>\n",
       "      <td>Morningside Park</td>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>63</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>43.811851</td>\n",
       "      <td>-79.151299</td>\n",
       "      <td>Rouge Park</td>\n",
       "      <td>122</td>\n",
       "      <td>2017</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>18</td>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>43.780349</td>\n",
       "      <td>-79.178148</td>\n",
       "      <td>Colonel Danforth Trail</td>\n",
       "      <td>56</td>\n",
       "      <td>2018</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>43.811851</td>\n",
       "      <td>-79.151299</td>\n",
       "      <td>Rouge Park</td>\n",
       "      <td>11</td>\n",
       "      <td>2018</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>10</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>43.809749</td>\n",
       "      <td>-79.187157</td>\n",
       "      <td>Upper Rouge Trail Park</td>\n",
       "      <td>19</td>\n",
       "      <td>2018</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>43.776814</td>\n",
       "      <td>-79.199891</td>\n",
       "      <td>Morningside Park</td>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>43.808778</td>\n",
       "      <td>-79.158593</td>\n",
       "      <td>Rouge Park: Glen Eagle Vista Trail</td>\n",
       "      <td>52</td>\n",
       "      <td>2019</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>43.621925</td>\n",
       "      <td>-79.363238</td>\n",
       "      <td>Toronto Islands: Algonquin Island Park</td>\n",
       "      <td>7</td>\n",
       "      <td>2019</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>43.810163</td>\n",
       "      <td>-79.187138</td>\n",
       "      <td>Upper Rouge Trail Park</td>\n",
       "      <td>39</td>\n",
       "      <td>2019</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     # Positive  BLT Adults and Nymphs  BLT Larvae   Latitude  Longitude  \\\n",
       "25            1                     10           0  43.811851 -79.151299   \n",
       "32            0                      8           0  43.626120 -79.360567   \n",
       "39            1                      8           0  43.780349 -79.178148   \n",
       "54            0                     14           0  43.776814 -79.199891   \n",
       "59           29                     63           0  43.811851 -79.151299   \n",
       "92            1                     14           0  43.776814 -79.199891   \n",
       "97           63                    122           0  43.811851 -79.151299   \n",
       "117          18                     56           0  43.780349 -79.178148   \n",
       "139           7                     11           0  43.811851 -79.151299   \n",
       "146          10                     19           0  43.809749 -79.187157   \n",
       "174           1                      6           0  43.776814 -79.199891   \n",
       "176          25                     52           0  43.808778 -79.158593   \n",
       "183           0                      7           0  43.621925 -79.363238   \n",
       "186           9                     39           0  43.810163 -79.187138   \n",
       "\n",
       "                              Park Location  Total BLTs  Year  _id  \n",
       "25                               Rouge Park          10  2015   26  \n",
       "32                         Algonquin Island           8  2016   33  \n",
       "39                   Colonel Danforth Trail           8  2016   40  \n",
       "54                         Morningside Park          14  2016   55  \n",
       "59                               Rouge Park          63  2016   60  \n",
       "92                         Morningside Park          14  2017   93  \n",
       "97                               Rouge Park         122  2017   98  \n",
       "117                  Colonel Danforth Trail          56  2018  118  \n",
       "139                              Rouge Park          11  2018  140  \n",
       "146                  Upper Rouge Trail Park          19  2018  147  \n",
       "174                        Morningside Park           6  2019  175  \n",
       "176      Rouge Park: Glen Eagle Vista Trail          52  2019  177  \n",
       "183  Toronto Islands: Algonquin Island Park           7  2019  184  \n",
       "186                  Upper Rouge Trail Park          39  2019  187  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Looks like there are a lot of counts of less than 5 total BLTs in a park \n",
    "#This will make the per-park trend hard to visualize against the much higher counts in some parks. \n",
    "#Let's clean those out and only look at Park/Years with counts over five\n",
    "df_nozero=df[df[\"Total BLTs\"]>5]\n",
    "df_nozero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Total BLTs</th>\n",
       "      <th># Positive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Park Location</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algonquin Island</th>\n",
       "      <th>2016</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Colonel Danforth Trail</th>\n",
       "      <th>2016</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>56</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Morningside Park</th>\n",
       "      <th>2016</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Rouge Park</th>\n",
       "      <th>2015</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>63</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>122</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rouge Park: Glen Eagle Vista Trail</th>\n",
       "      <th>2019</th>\n",
       "      <td>52</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto Islands: Algonquin Island Park</th>\n",
       "      <th>2019</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Upper Rouge Trail Park</th>\n",
       "      <th>2018</th>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>39</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Total BLTs  # Positive\n",
       "Park Location                          Year                        \n",
       "Algonquin Island                       2016           8           0\n",
       "Colonel Danforth Trail                 2016           8           1\n",
       "                                       2018          56          18\n",
       "Morningside Park                       2016          14           0\n",
       "                                       2017          14           1\n",
       "                                       2019           6           1\n",
       "Rouge Park                             2015          10           1\n",
       "                                       2016          63          29\n",
       "                                       2017         122          63\n",
       "                                       2018          11           7\n",
       "Rouge Park: Glen Eagle Vista Trail     2019          52          25\n",
       "Toronto Islands: Algonquin Island Park 2019           7           0\n",
       "Upper Rouge Trail Park                 2018          19          10\n",
       "                                       2019          39           9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now it's going to be easier to visualize because it only inludes park-year pairs with over 5 ticks counted\n",
    "df_nozero.groupby([\"Park Location\",\"Year\"]).agg({\"Total BLTs\":\"sum\", \"# Positive\":\"sum\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Making a new df that only has the columns we need\n",
    "df_clean = df_nozero[[\"Park Location\",\"Year\",\"# Positive\", \"Total BLTs\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping by year and park location\n",
    "df_cleangrouped = df_clean.groupby([\"Year\",'Park Location'])\n",
    "totals = df_cleangrouped.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Creating a pivot to make the results easier to plot\n",
    "\n",
    "result_BLTs = pd.pivot_table(totals, values=[ 'Total BLTs'], index=['Year'],\n",
    "                    columns=['Park Location'], aggfunc=sum)\n",
    "result_positives = pd.pivot_table(totals, values=[ \"# Positive\"], index=['Year'],\n",
    "                    columns=['Park Location'], aggfunc=sum)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x10fc037f0>,\n",
       "  <matplotlib.axis.XTick at 0x10fc03390>,\n",
       "  <matplotlib.axis.XTick at 0x10fd352e8>,\n",
       "  <matplotlib.axis.XTick at 0x10fd35a90>,\n",
       "  <matplotlib.axis.XTick at 0x10fd3c160>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee238d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "my_plot = result_positives.plot(kind='area', stacked = False, legend=True, title= \"Lyme disease Carriers per park\", sort_columns=True).legend(bbox_to_anchor=(1,1))\n",
    "from matplotlib import pyplot as plt\n",
    "plt.xticks([2015,2016,2017, 2018, 2019])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 125)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f042048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = result_BLTs.plot(kind='area', stacked = False, legend=True, title= \"Count of Black Leg Ticks per park\", sort_columns=True).legend(bbox_to_anchor=(1,1))\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "plt.xticks([2015,2016,2017, 2018, 2019])\n",
    "plt.ylim([5, 125])\n",
    "        \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}