bee-walk/BeeWalkPandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f0c4b77f",
   "metadata": {},
   "source": [
    "# Exploring BeeWalk data with Python Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "320ffabb",
   "metadata": {},
   "source": [
    "In this notebook I'd like to explore a small part of the _BeeWalk_ dataset using some of my recently acquired _Python Pandas_ skills."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d0a157d",
   "metadata": {},
   "source": [
    "## About the dataset\n",
    "\n",
    "Quoting directly from the overview on the Bumblebee Conservation Trust's website:\n",
    "\n",
    "```\n",
    "BeeWalk is a standardised bumblebee-monitoring scheme which involves volunteer ‘BeeWalkers’ walking the same fixed route (transect) once a month between March and October, counting the bumblebees seen and identifying them to species and caste (queen, worker, male) where possible.\n",
    "```\n",
    "\n",
    "Read more at https://www.bumblebeeconservation.org/beewalk/ and as mentioned on the https://www.bumblebeeconservation.org/our-science-research/ page the dataset is available via https://figshare.com/articles/BeeWalk_dataset_2008-19/12280547."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "277aac48",
   "metadata": {},
   "source": [
    "## Software used\n",
    "\n",
    "This [jupyter](https://en.wikipedia.org/wiki/Project_Jupyter) notebook uses the [pandas](https://en.wikipedia.org/wiki/Pandas_(software)) and [matplotlib](https://en.wikipedia.org/wiki/Matplotlib) python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ca5b404",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecff108b",
   "metadata": {},
   "source": [
    "## Reading the data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d35ebc7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_url = \"https://figshare.com/articles/dataset/BeeWalk_dataset_2008-19/12280547\"\n",
    "file = \"../data-sets/BeeWalk data 2008-19 06022020 for sharing.xlsx\" # manually downloaded from {dataset_url}\n",
    "sheets = pd.read_excel(io=file, sheet_name=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ff81743",
   "metadata": {},
   "source": [
    "From the preview on the `figshare.com` website we know that the Excel file contains two tabs with metadata and data respectively.\n",
    "\n",
    "Let's fill`metadata` and `data` variables accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c83c750",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = sheets[0].copy()\n",
    "data = sheets[1].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4608b5",
   "metadata": {},
   "source": [
    "## Metadata overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1adcc34a",
   "metadata": {},
   "source": [
    "The raw metadata contains 50 rows and 4 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ac4abd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783b0519",
   "metadata": {},
   "source": [
    "Note how the content of third and fourth columns is tangential."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "608b9331",
   "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>Dataset header</th>\n",
       "      <th>Explanation</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Data downloaded from website on</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RecordKey</td>\n",
       "      <td>Dataset unique key</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-01-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Website ID</td>\n",
       "      <td>Website use only - record identifier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Website RecordKey</td>\n",
       "      <td>Website use only - website unique key</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SiteName</td>\n",
       "      <td>Transect name</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Site section</td>\n",
       "      <td>Section of the transect</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ViceCounty</td>\n",
       "      <td>Watsonian vice-county of the transect centre p...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>established</td>\n",
       "      <td>Year first walked</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GridReference</td>\n",
       "      <td>OS grid ref of the transect centre point</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Projection</td>\n",
       "      <td>Map projection of the transect centre point (s...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Precision</td>\n",
       "      <td>Precision in metres of the transect centre point</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Transect lat</td>\n",
       "      <td>WGS84 latitude of the transect centre point (c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Transect long</td>\n",
       "      <td>WGS84 longitude of the transect centre point (...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>transect OS1936 lat</td>\n",
       "      <td>OS1936 latitude of the transect centre point (...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Transect OS1936 long</td>\n",
       "      <td>OS1936 longitude of the transect centre point ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>transect_length</td>\n",
       "      <td>Total transect length in metres</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>section_length</td>\n",
       "      <td>Length of the section in metres</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>section_grid_ref</td>\n",
       "      <td>OS grid ref of the individual section (startin...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>H1</td>\n",
       "      <td>Primary EUNIS level 2 habitat type on the tran...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>H2</td>\n",
       "      <td>Secondary EUNIS level 2 habitat type on the tr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>H3</td>\n",
       "      <td>Tertiary EUNIS level 2 habitat type on the tra...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>H4</td>\n",
       "      <td>Quaternary EUNIS level 2 habitat type on the t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>habitat_description</td>\n",
       "      <td>Free text description of the transect section ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>L1</td>\n",
       "      <td>Primary land use type (chosen by recorder from...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>L2</td>\n",
       "      <td>Secondary land use type (chosen by recorder fr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>land_use_description</td>\n",
       "      <td>Free text description of the land use of the t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Recorder</td>\n",
       "      <td>Name of recorder, supplied with submitted data</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>start_time</td>\n",
       "      <td>Time of day (24hr clock) that the transect wal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>end_time</td>\n",
       "      <td>Time of day (24hr clock) that the transect wal...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>sunshine</td>\n",
       "      <td>Categorical - sunny, sunny/cloudy, cloudy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>wind_speed</td>\n",
       "      <td>Wind speed (Beaufort scale)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>temperature</td>\n",
       "      <td>Air temperature in degrees Celcius</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>TaxonVersionKey</td>\n",
       "      <td>Taxon version key for the species</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>species</td>\n",
       "      <td>Vernacular name of the species</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>latin</td>\n",
       "      <td>Scientific name of the species (records are en...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>queens</td>\n",
       "      <td>Number of queens of the species recorded on th...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>workers</td>\n",
       "      <td>Number of workers of the species recorded on t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>males</td>\n",
       "      <td>Number of males of the species recorded on tha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>unknown</td>\n",
       "      <td>Number of the species recorded on that section...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Comment</td>\n",
       "      <td>Free text field for any other information</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>flower_visited</td>\n",
       "      <td>The flower species visited by the bees</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>StartDate</td>\n",
       "      <td>Date that the walk began on</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>EndDate</td>\n",
       "      <td>Date that the walk ended on</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>DateType</td>\n",
       "      <td>Accuracy of date.  D=Day</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Year</td>\n",
       "      <td>Year of record</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Month</td>\n",
       "      <td>Month of record</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Day</td>\n",
       "      <td>Day of record</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Sensitive</td>\n",
       "      <td>Whether the locality is sensitive (Y/N)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SHBP locality name</td>\n",
       "      <td>Short-haired bumblebee locality name (for matc...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>SHBP grid ref</td>\n",
       "      <td>Short-haired bumblebee grid ref (for matching ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Week</td>\n",
       "      <td>Week of year of record</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Dataset header                                        Explanation  \\\n",
       "0              RecordKey                                 Dataset unique key   \n",
       "1             Website ID               Website use only - record identifier   \n",
       "2      Website RecordKey              Website use only - website unique key   \n",
       "3               SiteName                                      Transect name   \n",
       "4           Site section                            Section of the transect   \n",
       "5             ViceCounty  Watsonian vice-county of the transect centre p...   \n",
       "6            established                                  Year first walked   \n",
       "7          GridReference           OS grid ref of the transect centre point   \n",
       "8             Projection  Map projection of the transect centre point (s...   \n",
       "9              Precision   Precision in metres of the transect centre point   \n",
       "10          Transect lat  WGS84 latitude of the transect centre point (c...   \n",
       "11         Transect long  WGS84 longitude of the transect centre point (...   \n",
       "12   transect OS1936 lat  OS1936 latitude of the transect centre point (...   \n",
       "13  Transect OS1936 long  OS1936 longitude of the transect centre point ...   \n",
       "14       transect_length                    Total transect length in metres   \n",
       "15        section_length                    Length of the section in metres   \n",
       "16      section_grid_ref  OS grid ref of the individual section (startin...   \n",
       "17                    H1  Primary EUNIS level 2 habitat type on the tran...   \n",
       "18                    H2  Secondary EUNIS level 2 habitat type on the tr...   \n",
       "19                    H3  Tertiary EUNIS level 2 habitat type on the tra...   \n",
       "20                    H4  Quaternary EUNIS level 2 habitat type on the t...   \n",
       "21   habitat_description  Free text description of the transect section ...   \n",
       "22                    L1  Primary land use type (chosen by recorder from...   \n",
       "23                    L2  Secondary land use type (chosen by recorder fr...   \n",
       "24  land_use_description  Free text description of the land use of the t...   \n",
       "25              Recorder     Name of recorder, supplied with submitted data   \n",
       "26            start_time  Time of day (24hr clock) that the transect wal...   \n",
       "27              end_time  Time of day (24hr clock) that the transect wal...   \n",
       "28              sunshine          Categorical - sunny, sunny/cloudy, cloudy   \n",
       "29            wind_speed                        Wind speed (Beaufort scale)   \n",
       "30           temperature                 Air temperature in degrees Celcius   \n",
       "31       TaxonVersionKey                  Taxon version key for the species   \n",
       "32               species                     Vernacular name of the species   \n",
       "33                 latin  Scientific name of the species (records are en...   \n",
       "34                queens  Number of queens of the species recorded on th...   \n",
       "35               workers  Number of workers of the species recorded on t...   \n",
       "36                 males  Number of males of the species recorded on tha...   \n",
       "37               unknown  Number of the species recorded on that section...   \n",
       "38               Comment          Free text field for any other information   \n",
       "39        flower_visited             The flower species visited by the bees   \n",
       "40             StartDate                        Date that the walk began on   \n",
       "41               EndDate                        Date that the walk ended on   \n",
       "42              DateType                           Accuracy of date.  D=Day   \n",
       "43                  Year                                     Year of record   \n",
       "44                 Month                                    Month of record   \n",
       "45                   Day                                      Day of record   \n",
       "46             Sensitive            Whether the locality is sensitive (Y/N)   \n",
       "47    SHBP locality name  Short-haired bumblebee locality name (for matc...   \n",
       "48         SHBP grid ref  Short-haired bumblebee grid ref (for matching ...   \n",
       "49                  Week                             Week of year of record   \n",
       "\n",
       "    Unnamed: 2 Data downloaded from website on  \n",
       "0          NaN                      2018-01-06  \n",
       "1          NaN                      2019-02-15  \n",
       "2          NaN                      2020-01-13  \n",
       "3          NaN                             NaT  \n",
       "4          NaN                             NaT  \n",
       "5          NaN                             NaT  \n",
       "6          NaN                             NaT  \n",
       "7          NaN                             NaT  \n",
       "8          NaN                             NaT  \n",
       "9          NaN                             NaT  \n",
       "10         NaN                             NaT  \n",
       "11         NaN                             NaT  \n",
       "12         NaN                             NaT  \n",
       "13         NaN                             NaT  \n",
       "14         NaN                             NaT  \n",
       "15         NaN                             NaT  \n",
       "16         NaN                             NaT  \n",
       "17         NaN                             NaT  \n",
       "18         NaN                             NaT  \n",
       "19         NaN                             NaT  \n",
       "20         NaN                             NaT  \n",
       "21         NaN                             NaT  \n",
       "22         NaN                             NaT  \n",
       "23         NaN                             NaT  \n",
       "24         NaN                             NaT  \n",
       "25         NaN                             NaT  \n",
       "26         NaN                             NaT  \n",
       "27         NaN                             NaT  \n",
       "28         NaN                             NaT  \n",
       "29         NaN                             NaT  \n",
       "30         NaN                             NaT  \n",
       "31         NaN                             NaT  \n",
       "32         NaN                             NaT  \n",
       "33         NaN                             NaT  \n",
       "34         NaN                             NaT  \n",
       "35         NaN                             NaT  \n",
       "36         NaN                             NaT  \n",
       "37         NaN                             NaT  \n",
       "38         NaN                             NaT  \n",
       "39         NaN                             NaT  \n",
       "40         NaN                             NaT  \n",
       "41         NaN                             NaT  \n",
       "42         NaN                             NaT  \n",
       "43         NaN                             NaT  \n",
       "44         NaN                             NaT  \n",
       "45         NaN                             NaT  \n",
       "46         NaN                             NaT  \n",
       "47         NaN                             NaT  \n",
       "48         NaN                             NaT  \n",
       "49         NaN                             NaT  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cde222",
   "metadata": {},
   "source": [
    "Let's remove those two columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25a560d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.drop(['Unnamed: 2', 'Data downloaded from website on'], axis=1, inplace=True)\n",
    "metadata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e60b85bf",
   "metadata": {},
   "source": [
    "And let's use the first column as the metadata index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fde00dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.set_index('Dataset header', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3352bb5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acc4777b",
   "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>Explanation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dataset header</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RecordKey</th>\n",
       "      <td>Dataset unique key</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Website ID</th>\n",
       "      <td>Website use only - record identifier</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Website RecordKey</th>\n",
       "      <td>Website use only - website unique key</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SiteName</th>\n",
       "      <td>Transect name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Site section</th>\n",
       "      <td>Section of the transect</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ViceCounty</th>\n",
       "      <td>Watsonian vice-county of the transect centre p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>established</th>\n",
       "      <td>Year first walked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GridReference</th>\n",
       "      <td>OS grid ref of the transect centre point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Projection</th>\n",
       "      <td>Map projection of the transect centre point (s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>Precision in metres of the transect centre point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transect lat</th>\n",
       "      <td>WGS84 latitude of the transect centre point (c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transect long</th>\n",
       "      <td>WGS84 longitude of the transect centre point (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transect OS1936 lat</th>\n",
       "      <td>OS1936 latitude of the transect centre point (...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transect OS1936 long</th>\n",
       "      <td>OS1936 longitude of the transect centre point ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>transect_length</th>\n",
       "      <td>Total transect length in metres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>section_length</th>\n",
       "      <td>Length of the section in metres</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>section_grid_ref</th>\n",
       "      <td>OS grid ref of the individual section (startin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H1</th>\n",
       "      <td>Primary EUNIS level 2 habitat type on the tran...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2</th>\n",
       "      <td>Secondary EUNIS level 2 habitat type on the tr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H3</th>\n",
       "      <td>Tertiary EUNIS level 2 habitat type on the tra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H4</th>\n",
       "      <td>Quaternary EUNIS level 2 habitat type on the t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>habitat_description</th>\n",
       "      <td>Free text description of the transect section ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L1</th>\n",
       "      <td>Primary land use type (chosen by recorder from...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L2</th>\n",
       "      <td>Secondary land use type (chosen by recorder fr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>land_use_description</th>\n",
       "      <td>Free text description of the land use of the t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recorder</th>\n",
       "      <td>Name of recorder, supplied with submitted data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>Time of day (24hr clock) that the transect wal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>Time of day (24hr clock) that the transect wal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunshine</th>\n",
       "      <td>Categorical - sunny, sunny/cloudy, cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wind_speed</th>\n",
       "      <td>Wind speed (Beaufort scale)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>Air temperature in degrees Celcius</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TaxonVersionKey</th>\n",
       "      <td>Taxon version key for the species</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <td>Vernacular name of the species</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latin</th>\n",
       "      <td>Scientific name of the species (records are en...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>queens</th>\n",
       "      <td>Number of queens of the species recorded on th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workers</th>\n",
       "      <td>Number of workers of the species recorded on t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>males</th>\n",
       "      <td>Number of males of the species recorded on tha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>Number of the species recorded on that section...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Comment</th>\n",
       "      <td>Free text field for any other information</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flower_visited</th>\n",
       "      <td>The flower species visited by the bees</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StartDate</th>\n",
       "      <td>Date that the walk began on</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EndDate</th>\n",
       "      <td>Date that the walk ended on</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateType</th>\n",
       "      <td>Accuracy of date.  D=Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>Year of record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>Month of record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Day</th>\n",
       "      <td>Day of record</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitive</th>\n",
       "      <td>Whether the locality is sensitive (Y/N)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHBP locality name</th>\n",
       "      <td>Short-haired bumblebee locality name (for matc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHBP grid ref</th>\n",
       "      <td>Short-haired bumblebee grid ref (for matching ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Week</th>\n",
       "      <td>Week of year of record</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            Explanation\n",
       "Dataset header                                                         \n",
       "RecordKey                                            Dataset unique key\n",
       "Website ID                         Website use only - record identifier\n",
       "Website RecordKey                 Website use only - website unique key\n",
       "SiteName                                                  Transect name\n",
       "Site section                                    Section of the transect\n",
       "ViceCounty            Watsonian vice-county of the transect centre p...\n",
       "established                                           Year first walked\n",
       "GridReference                  OS grid ref of the transect centre point\n",
       "Projection            Map projection of the transect centre point (s...\n",
       "Precision              Precision in metres of the transect centre point\n",
       "Transect lat          WGS84 latitude of the transect centre point (c...\n",
       "Transect long         WGS84 longitude of the transect centre point (...\n",
       "transect OS1936 lat   OS1936 latitude of the transect centre point (...\n",
       "Transect OS1936 long  OS1936 longitude of the transect centre point ...\n",
       "transect_length                         Total transect length in metres\n",
       "section_length                          Length of the section in metres\n",
       "section_grid_ref      OS grid ref of the individual section (startin...\n",
       "H1                    Primary EUNIS level 2 habitat type on the tran...\n",
       "H2                    Secondary EUNIS level 2 habitat type on the tr...\n",
       "H3                    Tertiary EUNIS level 2 habitat type on the tra...\n",
       "H4                    Quaternary EUNIS level 2 habitat type on the t...\n",
       "habitat_description   Free text description of the transect section ...\n",
       "L1                    Primary land use type (chosen by recorder from...\n",
       "L2                    Secondary land use type (chosen by recorder fr...\n",
       "land_use_description  Free text description of the land use of the t...\n",
       "Recorder                 Name of recorder, supplied with submitted data\n",
       "start_time            Time of day (24hr clock) that the transect wal...\n",
       "end_time              Time of day (24hr clock) that the transect wal...\n",
       "sunshine                      Categorical - sunny, sunny/cloudy, cloudy\n",
       "wind_speed                                  Wind speed (Beaufort scale)\n",
       "temperature                          Air temperature in degrees Celcius\n",
       "TaxonVersionKey                       Taxon version key for the species\n",
       "species                                  Vernacular name of the species\n",
       "latin                 Scientific name of the species (records are en...\n",
       "queens                Number of queens of the species recorded on th...\n",
       "workers               Number of workers of the species recorded on t...\n",
       "males                 Number of males of the species recorded on tha...\n",
       "unknown               Number of the species recorded on that section...\n",
       "Comment                       Free text field for any other information\n",
       "flower_visited                   The flower species visited by the bees\n",
       "StartDate                                   Date that the walk began on\n",
       "EndDate                                     Date that the walk ended on\n",
       "DateType                                       Accuracy of date.  D=Day\n",
       "Year                                                     Year of record\n",
       "Month                                                   Month of record\n",
       "Day                                                       Day of record\n",
       "Sensitive                       Whether the locality is sensitive (Y/N)\n",
       "SHBP locality name    Short-haired bumblebee locality name (for matc...\n",
       "SHBP grid ref         Short-haired bumblebee grid ref (for matching ...\n",
       "Week                                             Week of year of record"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "947d9454",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "The raw data contains several thousand rows and 49 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94f96e4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(151233, 49)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd60495f",
   "metadata": {},
   "source": [
    "The metadata defined 50 headers but the data contains only 49 columns.\n",
    "\n",
    "Let's find the missing column and its meaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "175a1417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explanation    Name of recorder, supplied with submitted data\n",
      "Name: Recorder, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for header in metadata.index.values:\n",
    "    if header not in data.columns:\n",
    "        print(metadata.loc[header])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea5b67ca",
   "metadata": {},
   "source": [
    "Since the name of the recorder is not part of the public dataset, let's remove the corresponding metadata row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4fb312fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata.drop(['Recorder'], axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8fce38d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49, 1)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c5f23c",
   "metadata": {},
   "source": [
    "## Data previews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f92b372",
   "metadata": {},
   "source": [
    "Let's looks at the first few data rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "462fc3cb",
   "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>RecordKey</th>\n",
       "      <th>Website ID</th>\n",
       "      <th>Website RecordKey</th>\n",
       "      <th>SiteName</th>\n",
       "      <th>Site section</th>\n",
       "      <th>ViceCounty</th>\n",
       "      <th>established</th>\n",
       "      <th>GridReference</th>\n",
       "      <th>Projection</th>\n",
       "      <th>Precision</th>\n",
       "      <th>...</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>DateType</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Sensitive</th>\n",
       "      <th>SHBP locality name</th>\n",
       "      <th>SHBP grid ref</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1350305</td>\n",
       "      <td>iBRC1350305</td>\n",
       "      <td>Holyrood park - Hunter's Bog</td>\n",
       "      <td>Holyrood park - Hunter's Bog - S2</td>\n",
       "      <td>Midlothian</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NT273733</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-10-08 00:00:00</td>\n",
       "      <td>2012-10-08 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1350304</td>\n",
       "      <td>iBRC1350304</td>\n",
       "      <td>Holyrood park - Hunter's Bog</td>\n",
       "      <td>Holyrood park - Hunter's Bog - S1</td>\n",
       "      <td>Midlothian</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NT273733</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-10-08 00:00:00</td>\n",
       "      <td>2012-10-08 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1350292</td>\n",
       "      <td>iBRC1350292</td>\n",
       "      <td>Holyrood park - Hunter's Bog</td>\n",
       "      <td>Holyrood park - Hunter's Bog - S3</td>\n",
       "      <td>Midlothian</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NT273733</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-09-27 00:00:00</td>\n",
       "      <td>2012-09-27 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1349168</td>\n",
       "      <td>iBRC1349168</td>\n",
       "      <td>Holyrood park - Hunter's Bog</td>\n",
       "      <td>Holyrood park - Hunter's Bog - S4</td>\n",
       "      <td>Midlothian</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NT273733</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-07-19 00:00:00</td>\n",
       "      <td>2012-07-19 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1349167</td>\n",
       "      <td>iBRC1349167</td>\n",
       "      <td>Holyrood park - Hunter's Bog</td>\n",
       "      <td>Holyrood park - Hunter's Bog - S2</td>\n",
       "      <td>Midlothian</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>NT273733</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>2012-07-19 00:00:00</td>\n",
       "      <td>2012-07-19 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RecordKey Website ID Website RecordKey                      SiteName  \\\n",
       "0          1    1350305       iBRC1350305  Holyrood park - Hunter's Bog   \n",
       "1          2    1350304       iBRC1350304  Holyrood park - Hunter's Bog   \n",
       "2          3    1350292       iBRC1350292  Holyrood park - Hunter's Bog   \n",
       "3          4    1349168       iBRC1349168  Holyrood park - Hunter's Bog   \n",
       "4          5    1349167       iBRC1349167  Holyrood park - Hunter's Bog   \n",
       "\n",
       "                        Site section  ViceCounty  established GridReference  \\\n",
       "0  Holyrood park - Hunter's Bog - S2  Midlothian       2012.0      NT273733   \n",
       "1  Holyrood park - Hunter's Bog - S1  Midlothian       2012.0      NT273733   \n",
       "2  Holyrood park - Hunter's Bog - S3  Midlothian       2012.0      NT273733   \n",
       "3  Holyrood park - Hunter's Bog - S4  Midlothian       2012.0      NT273733   \n",
       "4  Holyrood park - Hunter's Bog - S2  Midlothian       2012.0      NT273733   \n",
       "\n",
       "  Projection  Precision  ...            StartDate              EndDate  \\\n",
       "0       OSGB        100  ...  2012-10-08 00:00:00  2012-10-08 00:00:00   \n",
       "1       OSGB        100  ...  2012-10-08 00:00:00  2012-10-08 00:00:00   \n",
       "2       OSGB        100  ...  2012-09-27 00:00:00  2012-09-27 00:00:00   \n",
       "3       OSGB        100  ...  2012-07-19 00:00:00  2012-07-19 00:00:00   \n",
       "4       OSGB        100  ...  2012-07-19 00:00:00  2012-07-19 00:00:00   \n",
       "\n",
       "   DateType  Year  Month Day Sensitive SHBP locality name SHBP grid ref Week  \n",
       "0         D  2012     10   8         F                NaN           NaN   41  \n",
       "1         D  2012     10   8         F                NaN           NaN   41  \n",
       "2         D  2012      9  27         F                NaN           NaN   39  \n",
       "3         D  2012      7  19         F                NaN           NaN   29  \n",
       "4         D  2012      7  19         F                NaN           NaN   29  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ecd742",
   "metadata": {},
   "source": [
    "Let's use the first column as the data index and let's remove the two columns with website identifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "40e1321a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_index('RecordKey', inplace=True)\n",
    "data.drop(['Website ID', 'Website RecordKey'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b50f8a55",
   "metadata": {},
   "source": [
    "Let's looks at a small sample of rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d23cc1da",
   "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>SiteName</th>\n",
       "      <th>Site section</th>\n",
       "      <th>ViceCounty</th>\n",
       "      <th>established</th>\n",
       "      <th>GridReference</th>\n",
       "      <th>Projection</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Transect lat</th>\n",
       "      <th>Transect long</th>\n",
       "      <th>transect OS1936 lat</th>\n",
       "      <th>...</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>DateType</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Sensitive</th>\n",
       "      <th>SHBP locality name</th>\n",
       "      <th>SHBP grid ref</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecordKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>71525</th>\n",
       "      <td>Lancaster Canal</td>\n",
       "      <td>Lancaster Canal - S1</td>\n",
       "      <td>West Lancashire</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>SD521713</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>54.135307</td>\n",
       "      <td>-2.734619</td>\n",
       "      <td>54.135136</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-06-24 00:00:00</td>\n",
       "      <td>2017-06-24 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38222</th>\n",
       "      <td>Sustrans - NCN77 Perth Section 3</td>\n",
       "      <td>Sustrans - NCN77 Perth Section 3 - S5</td>\n",
       "      <td>Mid Perthshire</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>NO094265</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>56.422047</td>\n",
       "      <td>-3.470346</td>\n",
       "      <td>56.422108</td>\n",
       "      <td>...</td>\n",
       "      <td>2015-07-18 00:00:00</td>\n",
       "      <td>2015-07-18 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7130</th>\n",
       "      <td>Rockside and Dreemskerry Road, Isle of Man</td>\n",
       "      <td>Rockside and Dreemskerry Road, Isle of Man - S1</td>\n",
       "      <td>Isle of Man</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>SC478908</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>54.290104</td>\n",
       "      <td>-4.339882</td>\n",
       "      <td>54.289967</td>\n",
       "      <td>...</td>\n",
       "      <td>2011-07-11 00:00:00</td>\n",
       "      <td>2011-07-11 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2011</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124906</th>\n",
       "      <td>Hunting Hall Farm</td>\n",
       "      <td>Hunting Hall Farm - S5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>NU033408</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>55.660751</td>\n",
       "      <td>-1.949104</td>\n",
       "      <td>55.660721</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-06-21 00:00:00</td>\n",
       "      <td>2019-06-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115868</th>\n",
       "      <td>Brandhill Gutter</td>\n",
       "      <td>Brandhill Gutter - S3</td>\n",
       "      <td>Shropshire</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>SO425787</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>52.403248</td>\n",
       "      <td>-2.846628</td>\n",
       "      <td>52.402912</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-06-27 00:00:00</td>\n",
       "      <td>2017-06-27 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2017</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             SiteName  \\\n",
       "RecordKey                                               \n",
       "71525                                 Lancaster Canal   \n",
       "38222                Sustrans - NCN77 Perth Section 3   \n",
       "7130       Rockside and Dreemskerry Road, Isle of Man   \n",
       "124906                              Hunting Hall Farm   \n",
       "115868                               Brandhill Gutter   \n",
       "\n",
       "                                              Site section       ViceCounty  \\\n",
       "RecordKey                                                                     \n",
       "71525                                 Lancaster Canal - S1  West Lancashire   \n",
       "38222                Sustrans - NCN77 Perth Section 3 - S5   Mid Perthshire   \n",
       "7130       Rockside and Dreemskerry Road, Isle of Man - S1      Isle of Man   \n",
       "124906                              Hunting Hall Farm - S5              NaN   \n",
       "115868                               Brandhill Gutter - S3       Shropshire   \n",
       "\n",
       "           established GridReference Projection  Precision  Transect lat  \\\n",
       "RecordKey                                                                  \n",
       "71525           2012.0      SD521713       OSGB        100     54.135307   \n",
       "38222           2015.0      NO094265       OSGB        100     56.422047   \n",
       "7130            2009.0      SC478908       OSGB        100     54.290104   \n",
       "124906          2016.0      NU033408       OSGB        100     55.660751   \n",
       "115868          2017.0      SO425787       OSGB        100     52.403248   \n",
       "\n",
       "           Transect long  transect OS1936 lat  ...            StartDate  \\\n",
       "RecordKey                                      ...                        \n",
       "71525          -2.734619            54.135136  ...  2017-06-24 00:00:00   \n",
       "38222          -3.470346            56.422108  ...  2015-07-18 00:00:00   \n",
       "7130           -4.339882            54.289967  ...  2011-07-11 00:00:00   \n",
       "124906         -1.949104            55.660721  ...  2019-06-21 00:00:00   \n",
       "115868         -2.846628            52.402912  ...  2017-06-27 00:00:00   \n",
       "\n",
       "                       EndDate DateType  Year Month Day Sensitive  \\\n",
       "RecordKey                                                           \n",
       "71525      2017-06-24 00:00:00        D  2017     6  24         F   \n",
       "38222      2015-07-18 00:00:00        D  2015     7  18         F   \n",
       "7130       2011-07-11 00:00:00        D  2011     7  11         F   \n",
       "124906     2019-06-21 00:00:00        D  2019     6  21         F   \n",
       "115868     2017-06-27 00:00:00        D  2017     6  27         F   \n",
       "\n",
       "          SHBP locality name SHBP grid ref Week  \n",
       "RecordKey                                        \n",
       "71525                    NaN           NaN   25  \n",
       "38222                    NaN           NaN   29  \n",
       "7130                     NaN           NaN   29  \n",
       "124906                   NaN           NaN   25  \n",
       "115868                   NaN           NaN   26  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(n=5, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2a45194",
   "metadata": {},
   "source": [
    "## Data cleaning: established"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8960689",
   "metadata": {},
   "source": [
    "The `established` column appears to be a floating pointer number, let's use `dtype` to confirm that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8a73df3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['established'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13120cce",
   "metadata": {},
   "source": [
    "Let's double-check the meaning of `established` via the metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ded36352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Year first walked'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.loc['established', 'Explanation']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad797439",
   "metadata": {},
   "source": [
    "The `info` for the column shows that there are 151,233 entries but only 151,224 non-null values. This means we have some missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3c7cdcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 151233 entries, 1 to 154280\n",
      "Series name: established\n",
      "Non-Null Count   Dtype  \n",
      "--------------   -----  \n",
      "151224 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "data['established'].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28eea9e3",
   "metadata": {},
   "source": [
    "Let's find the rows with missing `established` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea48947f",
   "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>SiteName</th>\n",
       "      <th>Site section</th>\n",
       "      <th>ViceCounty</th>\n",
       "      <th>established</th>\n",
       "      <th>GridReference</th>\n",
       "      <th>Projection</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Transect lat</th>\n",
       "      <th>Transect long</th>\n",
       "      <th>transect OS1936 lat</th>\n",
       "      <th>...</th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>DateType</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Sensitive</th>\n",
       "      <th>SHBP locality name</th>\n",
       "      <th>SHBP grid ref</th>\n",
       "      <th>Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecordKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>139745</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139746</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139747</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139748</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>2019-08-18 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144320</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144321</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144322</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144323</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144324</th>\n",
       "      <td>Cranham Commom</td>\n",
       "      <td>Cranham Commom - S1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ST812461</td>\n",
       "      <td>OSGB</td>\n",
       "      <td>100</td>\n",
       "      <td>51.213734</td>\n",
       "      <td>-2.270558</td>\n",
       "      <td>51.213276</td>\n",
       "      <td>...</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>2019-07-21 00:00:00</td>\n",
       "      <td>D</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>F</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 SiteName         Site section ViceCounty  established  \\\n",
       "RecordKey                                                                \n",
       "139745     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "139746     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "139747     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "139748     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "144320     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "144321     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "144322     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "144323     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "144324     Cranham Commom  Cranham Commom - S1        NaN          NaN   \n",
       "\n",
       "          GridReference Projection  Precision  Transect lat  Transect long  \\\n",
       "RecordKey                                                                    \n",
       "139745         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "139746         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "139747         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "139748         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "144320         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "144321         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "144322         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "144323         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "144324         ST812461       OSGB        100     51.213734      -2.270558   \n",
       "\n",
       "           transect OS1936 lat  ...            StartDate              EndDate  \\\n",
       "RecordKey                       ...                                             \n",
       "139745               51.213276  ...  2019-08-18 00:00:00  2019-08-18 00:00:00   \n",
       "139746               51.213276  ...  2019-08-18 00:00:00  2019-08-18 00:00:00   \n",
       "139747               51.213276  ...  2019-08-18 00:00:00  2019-08-18 00:00:00   \n",
       "139748               51.213276  ...  2019-08-18 00:00:00  2019-08-18 00:00:00   \n",
       "144320               51.213276  ...  2019-07-21 00:00:00  2019-07-21 00:00:00   \n",
       "144321               51.213276  ...  2019-07-21 00:00:00  2019-07-21 00:00:00   \n",
       "144322               51.213276  ...  2019-07-21 00:00:00  2019-07-21 00:00:00   \n",
       "144323               51.213276  ...  2019-07-21 00:00:00  2019-07-21 00:00:00   \n",
       "144324               51.213276  ...  2019-07-21 00:00:00  2019-07-21 00:00:00   \n",
       "\n",
       "          DateType  Year Month Day Sensitive SHBP locality name SHBP grid ref  \\\n",
       "RecordKey                                                                       \n",
       "139745           D  2019     8  18         F                NaN           NaN   \n",
       "139746           D  2019     8  18         F                NaN           NaN   \n",
       "139747           D  2019     8  18         F                NaN           NaN   \n",
       "139748           D  2019     8  18         F                NaN           NaN   \n",
       "144320           D  2019     7  21         F                NaN           NaN   \n",
       "144321           D  2019     7  21         F                NaN           NaN   \n",
       "144322           D  2019     7  21         F                NaN           NaN   \n",
       "144323           D  2019     7  21         F                NaN           NaN   \n",
       "144324           D  2019     7  21         F                NaN           NaN   \n",
       "\n",
       "          Week  \n",
       "RecordKey       \n",
       "139745      34  \n",
       "139746      34  \n",
       "139747      34  \n",
       "139748      34  \n",
       "144320      30  \n",
       "144321      30  \n",
       "144322      30  \n",
       "144323      30  \n",
       "144324      30  \n",
       "\n",
       "[9 rows x 46 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "established_missing_data = data[data['established'].isna()]\n",
    "established_missing_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9736439",
   "metadata": {},
   "source": [
    "The rows with missing `established` data are all for the same site."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "576896f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cranham Commom'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "established_missing_data['SiteName'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c386b02",
   "metadata": {},
   "source": [
    "Furthermore all rows for that site lack the `established` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "04d800bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = (data['SiteName'] == 'Cranham Commom')\n",
    "data[mask]['established'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bfa6ebc",
   "metadata": {},
   "source": [
    "Let's use the earliest `Year` recorded for the site to fill the missing `established` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "af4d671a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[mask]['Year'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ae3cc41",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[mask, 'established'] = data[mask]['Year'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a89ffd9",
   "metadata": {},
   "source": [
    "Lastly, we can now convert from floating point to integer data type for the `established` year column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8c589ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['established'] = data['established'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7efc8cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['established'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7adc8a9c",
   "metadata": {},
   "source": [
    "The `value_counts` function shows the number of sites established in a given year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "78a021cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2008     1694\n",
       "2009     4607\n",
       "2010    15921\n",
       "2011     5739\n",
       "2012     9566\n",
       "2013    16359\n",
       "2014    22011\n",
       "2015    20423\n",
       "2016    20091\n",
       "2017    14288\n",
       "2018    12662\n",
       "2019     7872\n",
       "Name: established, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['established'].value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee4fc99",
   "metadata": {},
   "source": [
    "## Data preview: flower_visited"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f387e882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The flower species visited by the bees'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.loc['flower_visited', 'Explanation']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04b9f47",
   "metadata": {},
   "source": [
    "The flower species visited is recorded about half the time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4d8323d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     75919\n",
       "False    75314\n",
       "Name: flower_visited, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flower_visited'].notnull().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d6ed78",
   "metadata": {},
   "source": [
    "The `flower_visited` values use a mix of upper and lowercase and record one or more species for each record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "11bd7c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bramble                                            3668\n",
       "bramble                                            1729\n",
       "Red Clover                                         1255\n",
       "Knapweed                                           1221\n",
       "White Comfrey                                      1128\n",
       "                                                   ... \n",
       "White Clover + Meadow Buttercup                       1\n",
       "Meadow Buttercup + Narrow-leaved Water dropwort       1\n",
       "Meadow Butterup                                       1\n",
       "Cats-ear + Meadow Buttercup                           1\n",
       "Broad Bean                                            1\n",
       "Name: flower_visited, Length: 9520, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flower_visited'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64bf76ea",
   "metadata": {},
   "source": [
    "Let's consistently use lowercase letters for the `flower_visited` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c79aa865",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['flower_visited'] = data['flower_visited'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f2272d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bramble                            5398\n",
       "white clover                       2387\n",
       "red clover                         2086\n",
       "knapweed                           2016\n",
       "white comfrey                      1445\n",
       "                                   ... \n",
       "knapweed/eyebright                    1\n",
       "knapweed/red clover                   1\n",
       "rosebay willowherb / horse mint       1\n",
       "confrey                               1\n",
       "rape, white comfrey                   1\n",
       "Name: flower_visited, Length: 8186, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['flower_visited'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7831826",
   "metadata": {},
   "source": [
    "The `flower_visited` values also use a mix of common and latin plant names e.g. _clover_ vs. _trifolium_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "39c5c397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "white clover          2387\n",
       "red clover            2086\n",
       "trifolium pratense     476\n",
       "trifolium repens       337\n",
       "Name: flower_visited, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clover_mask = data['flower_visited'].isin(['trifolium pratense', 'trifolium repens', 'red clover', 'white clover'])\n",
    "\n",
    "data[clover_mask]['flower_visited'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cc0f5cc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clover                                  380\n",
       "redclover                                52\n",
       "whiteclover                              26\n",
       "alsike clover                            23\n",
       "clover sp.                               20\n",
       "                                       ... \n",
       "3 knapweed / 1 red clover                 1\n",
       "clovers, birdsfoot trefoil, knapweed      1\n",
       "red clover + meadow buttercup             1\n",
       "white clover + meadow buttercup           1\n",
       "marsh thistle, red clover                 1\n",
       "Name: flower_visited, Length: 487, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contains_other_clover_mask = data['flower_visited'].notnull() \\\n",
    "                           & data['flower_visited'].str.contains('clover') \\\n",
    "                           & ~clover_mask\n",
    "\n",
    "data[contains_other_clover_mask]['flower_visited'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "057f1651",
   "metadata": {},
   "source": [
    "## Bee counting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74b8db68",
   "metadata": {},
   "source": [
    "There are three bumblebee castes but it's not always possible to know which caste a bee belongs to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1758c72e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of queens of the species recorded on that section and date\n",
      "Number of workers of the species recorded on that section and date\n",
      "Number of males of the species recorded on that section and date\n",
      "Number of the species recorded on that section and date which could not be confidently allocated to caste\n"
     ]
    }
   ],
   "source": [
    "castes = ['queens', 'workers', 'males', 'unknown']\n",
    "\n",
    "for caste in castes:\n",
    "    print(metadata.loc[caste, 'Explanation'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5376945",
   "metadata": {},
   "source": [
    "Here's some per-caste bee count statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "563c20c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>queens</th>\n",
       "      <th>workers</th>\n",
       "      <th>males</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151233.000000</td>\n",
       "      <td>151233.000000</td>\n",
       "      <td>151233.000000</td>\n",
       "      <td>151233.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.299121</td>\n",
       "      <td>2.100778</td>\n",
       "      <td>0.298202</td>\n",
       "      <td>0.493675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.175357</td>\n",
       "      <td>6.669967</td>\n",
       "      <td>1.716639</td>\n",
       "      <td>4.971128</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>0.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              queens        workers          males        unknown\n",
       "count  151233.000000  151233.000000  151233.000000  151233.000000\n",
       "mean        0.299121       2.100778       0.298202       0.493675\n",
       "std         1.175357       6.669967       1.716639       4.971128\n",
       "min         0.000000       0.000000       0.000000       0.000000\n",
       "25%         0.000000       0.000000       0.000000       0.000000\n",
       "50%         0.000000       1.000000       0.000000       0.000000\n",
       "75%         0.000000       2.000000       0.000000       0.000000\n",
       "max       200.000000    1000.000000     300.000000    1500.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[castes].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97f2eacf",
   "metadata": {},
   "source": [
    "Let's sum up the per-caste bee counts to obtain overall bee count statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3ff8d8bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['bees'] = 0\n",
    "for caste in castes:\n",
    "    data['bees'] += data[caste]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4761dede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>bees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>151233.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.191777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.503142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                bees\n",
       "count  151233.000000\n",
       "mean        3.191777\n",
       "std         8.503142\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         1.000000\n",
       "75%         3.000000\n",
       "max      1500.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['bees']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2aa745",
   "metadata": {},
   "source": [
    "Let's add additional columns simplifying from _number of bees_ integer to _bees Y/N_ boolean values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ffa9ab5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['has_bees'] = (data['bees'] != 0)\n",
    "for caste in castes:\n",
    "    data['has_' + caste] = (data[caste] != 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235638e1",
   "metadata": {},
   "source": [
    "Here's the bee count data for the _Chocolate Mining Bee_ species."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3efc3a76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bees</th>\n",
       "      <th>queens</th>\n",
       "      <th>workers</th>\n",
       "      <th>males</th>\n",
       "      <th>unknown</th>\n",
       "      <th>has_bees</th>\n",
       "      <th>has_queens</th>\n",
       "      <th>has_workers</th>\n",
       "      <th>has_males</th>\n",
       "      <th>has_unknown</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RecordKey</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89926</th>\n",
       "      <td>1500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125808</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125820</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126405</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127864</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128761</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128996</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129170</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129178</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129182</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130316</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142766</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142767</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Chocolate Mining Bee</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           bees  queens  workers  males  unknown  has_bees  has_queens  \\\n",
       "RecordKey                                                                \n",
       "89926      1500       0        0      0     1500      True       False   \n",
       "125808        1       1        0      0        0      True        True   \n",
       "125820        1       1        0      0        0      True        True   \n",
       "126405        1       0        0      0        1      True       False   \n",
       "127864        5       0        0      0        5      True       False   \n",
       "128761        1       0        1      0        0      True       False   \n",
       "128996        1       1        0      0        0      True        True   \n",
       "129170        1       0        0      0        1      True       False   \n",
       "129178        1       0        0      0        1      True       False   \n",
       "129182        1       0        0      0        1      True       False   \n",
       "130316        1       1        0      0        0      True        True   \n",
       "142766        1       0        0      0        1      True       False   \n",
       "142767        2       0        0      0        2      True       False   \n",
       "\n",
       "           has_workers  has_males  has_unknown               species  \n",
       "RecordKey                                                             \n",
       "89926            False      False         True  Chocolate Mining Bee  \n",
       "125808           False      False        False  Chocolate Mining Bee  \n",
       "125820           False      False        False  Chocolate Mining Bee  \n",
       "126405           False      False         True  Chocolate Mining Bee  \n",
       "127864           False      False         True  Chocolate Mining Bee  \n",
       "128761            True      False        False  Chocolate Mining Bee  \n",
       "128996           False      False        False  Chocolate Mining Bee  \n",
       "129170           False      False         True  Chocolate Mining Bee  \n",
       "129178           False      False         True  Chocolate Mining Bee  \n",
       "129182           False      False         True  Chocolate Mining Bee  \n",
       "130316           False      False        False  Chocolate Mining Bee  \n",
       "142766           False      False         True  Chocolate Mining Bee  \n",
       "142767           False      False         True  Chocolate Mining Bee  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bee_mask = (data['species'] == 'Chocolate Mining Bee')\n",
    "\n",
    "selected_columns = [\n",
    "    'bees', 'queens', 'workers', 'males', 'unknown',\n",
    "    'has_bees', 'has_queens', 'has_workers', 'has_males', 'has_unknown',\n",
    "    'species'\n",
    "]\n",
    "\n",
    "data[bee_mask][selected_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672a99a5",
   "metadata": {},
   "source": [
    "## Weekly summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c4a1f9ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Week of year of record'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata.loc['Week', 'Explanation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "33ea7f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "151233"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0] # number of records"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e1c5ee",
   "metadata": {},
   "source": [
    "Let's condense the records into per-week-of-year summary rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "735d9b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "week_sums = data.groupby('Week').sum()[['has_bees', 'has_queens', 'has_workers', 'has_males', 'has_unknown']]\n",
    "week_sums.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5481d0cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_sums.shape[0] # fewer than the 52 weeks in a year since bumblebees not flying and monitored all year round"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ee1b2137",
   "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>Week</th>\n",
       "      <th>has_bees</th>\n",
       "      <th>has_queens</th>\n",
       "      <th>has_workers</th>\n",
       "      <th>has_males</th>\n",
       "      <th>has_unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>103</td>\n",
       "      <td>65</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>134</td>\n",
       "      <td>77</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>354</td>\n",
       "      <td>277</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1090</td>\n",
       "      <td>761</td>\n",
       "      <td>163</td>\n",
       "      <td>7</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>2008</td>\n",
       "      <td>1459</td>\n",
       "      <td>218</td>\n",
       "      <td>23</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14</td>\n",
       "      <td>1376</td>\n",
       "      <td>966</td>\n",
       "      <td>163</td>\n",
       "      <td>19</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15</td>\n",
       "      <td>1708</td>\n",
       "      <td>1214</td>\n",
       "      <td>234</td>\n",
       "      <td>12</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>4125</td>\n",
       "      <td>2770</td>\n",
       "      <td>667</td>\n",
       "      <td>51</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17</td>\n",
       "      <td>3837</td>\n",
       "      <td>2467</td>\n",
       "      <td>778</td>\n",
       "      <td>40</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>2668</td>\n",
       "      <td>1392</td>\n",
       "      <td>831</td>\n",
       "      <td>94</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>2649</td>\n",
       "      <td>1233</td>\n",
       "      <td>956</td>\n",
       "      <td>76</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>3774</td>\n",
       "      <td>1351</td>\n",
       "      <td>1897</td>\n",
       "      <td>207</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21</td>\n",
       "      <td>5147</td>\n",
       "      <td>1408</td>\n",
       "      <td>3011</td>\n",
       "      <td>368</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>22</td>\n",
       "      <td>5557</td>\n",
       "      <td>1067</td>\n",
       "      <td>3527</td>\n",
       "      <td>440</td>\n",
       "      <td>916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>23</td>\n",
       "      <td>5296</td>\n",
       "      <td>753</td>\n",
       "      <td>3572</td>\n",
       "      <td>438</td>\n",
       "      <td>887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>24</td>\n",
       "      <td>8313</td>\n",
       "      <td>1055</td>\n",
       "      <td>5976</td>\n",
       "      <td>802</td>\n",
       "      <td>1131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>25</td>\n",
       "      <td>10991</td>\n",
       "      <td>1136</td>\n",
       "      <td>8047</td>\n",
       "      <td>1022</td>\n",
       "      <td>1561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>26</td>\n",
       "      <td>10284</td>\n",
       "      <td>906</td>\n",
       "      <td>7420</td>\n",
       "      <td>964</td>\n",
       "      <td>1664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>27</td>\n",
       "      <td>7473</td>\n",
       "      <td>553</td>\n",
       "      <td>5461</td>\n",
       "      <td>660</td>\n",
       "      <td>1226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>28</td>\n",
       "      <td>7536</td>\n",
       "      <td>577</td>\n",
       "      <td>5311</td>\n",
       "      <td>770</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>29</td>\n",
       "      <td>9161</td>\n",
       "      <td>694</td>\n",
       "      <td>6188</td>\n",
       "      <td>1432</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>30</td>\n",
       "      <td>10085</td>\n",
       "      <td>904</td>\n",
       "      <td>6863</td>\n",
       "      <td>1734</td>\n",
       "      <td>1495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>31</td>\n",
       "      <td>8225</td>\n",
       "      <td>842</td>\n",
       "      <td>5248</td>\n",
       "      <td>1692</td>\n",
       "      <td>1279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>32</td>\n",
       "      <td>7245</td>\n",
       "      <td>709</td>\n",
       "      <td>4654</td>\n",
       "      <td>1598</td>\n",
       "      <td>1032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>33</td>\n",
       "      <td>6761</td>\n",
       "      <td>679</td>\n",
       "      <td>4286</td>\n",
       "      <td>1672</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>34</td>\n",
       "      <td>7445</td>\n",
       "      <td>719</td>\n",
       "      <td>4519</td>\n",
       "      <td>1763</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>35</td>\n",
       "      <td>5360</td>\n",
       "      <td>557</td>\n",
       "      <td>3157</td>\n",
       "      <td>1230</td>\n",
       "      <td>942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>36</td>\n",
       "      <td>2729</td>\n",
       "      <td>344</td>\n",
       "      <td>1569</td>\n",
       "      <td>673</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>37</td>\n",
       "      <td>2612</td>\n",
       "      <td>325</td>\n",
       "      <td>1593</td>\n",
       "      <td>526</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>38</td>\n",
       "      <td>2651</td>\n",
       "      <td>356</td>\n",
       "      <td>1517</td>\n",
       "      <td>536</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>39</td>\n",
       "      <td>1903</td>\n",
       "      <td>261</td>\n",
       "      <td>1004</td>\n",
       "      <td>405</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>40</td>\n",
       "      <td>904</td>\n",
       "      <td>138</td>\n",
       "      <td>500</td>\n",
       "      <td>151</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>41</td>\n",
       "      <td>645</td>\n",
       "      <td>105</td>\n",
       "      <td>319</td>\n",
       "      <td>98</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>42</td>\n",
       "      <td>445</td>\n",
       "      <td>67</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>43</td>\n",
       "      <td>339</td>\n",
       "      <td>73</td>\n",
       "      <td>164</td>\n",
       "      <td>40</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>44</td>\n",
       "      <td>156</td>\n",
       "      <td>43</td>\n",
       "      <td>58</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>47</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Week  has_bees  has_queens  has_workers  has_males  has_unknown\n",
       "0      8         7           4            3          0            0\n",
       "1      9       103          65           22          1           17\n",
       "2     10       134          77           28          0           31\n",
       "3     11       354         277           50          2           36\n",
       "4     12      1090         761          163          7          172\n",
       "5     13      2008        1459          218         23          354\n",
       "6     14      1376         966          163         19          263\n",
       "7     15      1708        1214          234         12          288\n",
       "8     16      4125        2770          667         51          739\n",
       "9     17      3837        2467          778         40          681\n",
       "10    18      2668        1392          831         94          484\n",
       "11    19      2649        1233          956         76          519\n",
       "12    20      3774        1351         1897        207          575\n",
       "13    21      5147        1408         3011        368          833\n",
       "14    22      5557        1067         3527        440          916\n",
       "15    23      5296         753         3572        438          887\n",
       "16    24      8313        1055         5976        802         1131\n",
       "17    25     10991        1136         8047       1022         1561\n",
       "18    26     10284         906         7420        964         1664\n",
       "19    27      7473         553         5461        660         1226\n",
       "20    28      7536         577         5311        770         1349\n",
       "21    29      9161         694         6188       1432         1528\n",
       "22    30     10085         904         6863       1734         1495\n",
       "23    31      8225         842         5248       1692         1279\n",
       "24    32      7245         709         4654       1598         1032\n",
       "25    33      6761         679         4286       1672          906\n",
       "26    34      7445         719         4519       1763         1225\n",
       "27    35      5360         557         3157       1230          942\n",
       "28    36      2729         344         1569        673          494\n",
       "29    37      2612         325         1593        526          457\n",
       "30    38      2651         356         1517        536          521\n",
       "31    39      1903         261         1004        405          410\n",
       "32    40       904         138          500        151          183\n",
       "33    41       645         105          319         98          164\n",
       "34    42       445          67          210         66          121\n",
       "35    43       339          73          164         40           79\n",
       "36    44       156          43           58         14           45\n",
       "37    45         6           3            1          0            2\n",
       "38    46         3           1            3          0            0\n",
       "39    47         2           0            1          1            0\n",
       "40    49         4           0            0          0            4"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "week_sums"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a68d3d",
   "metadata": {},
   "source": [
    "## Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618d8c0c",
   "metadata": {},
   "source": [
    "Here's a simple plot showing overall recordings across the weeks of the year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7d129c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Week', ylabel='has_bees'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_sums.plot(kind='scatter', x='Week', y='has_bees', xlim=[0, 52])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d359d828",
   "metadata": {},
   "source": [
    "Per-caste recordings show distinct seasonal differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "02db4022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='[Week, Week, Week]', ylabel='[has_queens, has_workers, has_males]'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "week_sums.plot(kind='scatter', \\\n",
    "               x=['Week', 'Week', 'Week'], \\\n",
    "               y=['has_queens', 'has_workers', 'has_males'], \\\n",
    "               c=['red', 'pink', 'blue'] * week_sums.shape[0], \\\n",
    "               xlim=[0, 52])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "362f52ca",
   "metadata": {},
   "source": [
    "## Bumblebee sightings by caste\n",
    "\n",
    "Let's complete the scatter plot by adding a title, axis and series labels and, of course, attribution info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4448febf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qsLG05eRxnFZAQ1p6x6vqeuCrwBJgV+BnmSQUkc4i8pyIzBSRGSJysIh0FZE3RWRO+O0SiX+diMwVkVkickIkfJiITA3n7hTxAQwni2lfBDkJf4HiopaRxXFaCQ1Reu3C78nAk6q6ugFp/wq8rqq7A/sCM4BfAKNVdTAwOhwjIkOA84A9gROBu0UkN+TzD+BSYHDYTmyADI7T+KzbaF2K8xabQUlzslNvW0EhJ8fWyivMh137N68MjtPKaMiI98siMhPYDPxIRHoAZfUlEpFOwBHARQCqWgFUiMjpwIgQ7RFgDHAtcDrwlKqWA/NFZC5woIgsADqp6gch30eBM4DXGnAPjtN4fLkWPv3MxtQAvlgFw4dAYUHzXD8nxwxXNm22bs6YAnQcJyUZ/0NU9RfAwcBwVa0ESjEFVR87AyuBh0RkoojcLyLtgV6qujTkvRToGeL3ARZH0i8JYX3CfmK447QM8z6PKzyA6mpYsqJ5ZRAxZdepgys8x8mAjFt6InJWZD+2u05EalQ13T89D9gf+LGqfiQifyV0Zaa6VJIwTROeKOelWBcoO+20U5rLOM42EjUiiVFd3fxyOI6TMQ35NLwEuB+4IGz3AVcD74nIt9KkWwIsUdWPwvFzmBJcLiK9AcLvikj8fpH0fYEvQnjfJOG1UNV7VXW4qg7v0aNHA27PcRpIr661W1c5OdCza8vJ4zhOvTRE6dUAe6jq11T1a8AQoBz4CjYWlxRVXQYsFpHdQtAxwKfASODCEHYh8O+wPxI4T0QKRGQgZrAyLnSBbhCRg4LV5rcjaRyn+em/I/TtBQXtoKgAdh8IXTq1tFSO46ShIYYsA1R1eeR4BbCrqq4Wkcp60v4YeFxE8oF5wHcwhfuMiFwCLALOAVDV6SLyDKYYq4DLVDXWZ/RD4GGgCDNgcSMWp+UQgYF9bHMcp1XQEKU3VkReAZ4Nx18D3glGKWvTJVTVScDwJKeOSRH/d8DvkoSPB/bKXGTHcRzHidMQpXcZpugOxYxKHgWeV1UFjmoC2RzHcRynUclY6QXl9lzYHMdxHKfV0RDfm2cFl2HrRGS9iGwQkfVNKZzjOI7jNCYN6d78A3Cqqs5oKmEcJ6upqYHV622uX0mH5vPs4jhZREOU3nJXeI7TRNTUwMSZsDl49lNgn8FQ0rFFxXKctkZDlN54EXkaeAmbnweAqr7Q2EI5TtaxbBWUbq7t1mzmAvjK3i0mkuO0RRqi9Dph/jaPj4Qp4ErPcbaV8sraCg+gMjL9VRUqKiEvz1ZUcBxnq2iI9eZ3mlIQx8lqSoLD6Jg/T8GcSIMtDDtlto31KbZ80A7dW0pSx2nV1Kv0ROTnqvoHEbmLJA6eVfWKJpHMcbKJriUwYEeY/7m16jq0hz12tv0ps6GyKh53zkLo2N4Wkd0aVqyGz5ebR5mdetu1HSdLyKSlFzNeGd+UgjhO1tNvB/PlWaPxLszKKqhKWLlBxNbQ2xqlt/xLmL0w3qKcPhf2Guw+Q52soV6lp6ovh91SVX02ek5EzmkSqRwnWxGB3MgqWnm5FqaRThYFCvK3Lv/Pl9deEqlG4fMVrvScrKEhI+LXZRjmOE5jIQJ7DLTxvtwc+92hm40Bbm1+mYQ5ThslkzG9k4CTgT4icmfkVCdsFQTHcZqS7l3ggGLYuNlaeB2Ltz6vnXrDp/Pirb2cHOjXq3HkdJxWQCZjel9g43mnARMi4RuAnzSFUI7jJFBY0DgeWrp1hj0HWZemiI0jdtrKVqPjtEIyGdObDEwWkSdUtb518xzH2d7pWuIWm07W0pDJ6QeKyI1A/5BOsMUXdm4KwRzHcRynsWmI0nsA686cAFTXE9dxHMdxtjsaovTWqeprTSaJ4ziO4zQxmVhv7h923xKR2zFfm1GH0580kWyO4ziO06hk0tL7U8Lx8Mi+Akc3njiO4ziO03RkYr15VHMI4jiO4zhNTcZjeiJydZLgdcAEVZ3UaBI5jtMylJbB7AVQVgGd2ttqDnkNGfZ3nO2fhrghGw78AOgTtkuBEcB9IvLzxhfNcZxmo7IKJs6AdRuhvAJWrYUpc2r7/HScNkBDlF43YH9V/amq/hRTgj2AI4CLmkA2x3Gai/UbE5xaq63jV+meBp22RUP6LnYCKiLHlUB/Vd0sIuUp0jiO0xrISfH9myo8xpr1tlRRVZWt1LDbAMjNbXTxHKexaIjSewL4UET+HY5PBZ4UkfbAp40umeM4zUdJBygqgE1l1srLyYGeXW1po1SUboZpc+POq1ethep5sPfgZhHZcbaGjJWeqt4sIv8BDsNckP1AVWMLy17QFMI5jtNM5OTA0N1hyQrYXGZKcIfu6dOsWY/NWgqohjDH2X7JZHJ6J1VdLyJdgflhi53rqqqrm1JAx3Gaidxc6N+7YfHNBW88LMfX5nO2bzJp6T0BfBXzuRk15Yq97e5w2nGykR5dYNFSs/asCV2iO/dtaakcJy2ZTE7/avgd2PTiOI6z3aEKCz6HFautdbdzX1uaKDcXhg2BpSuhohK6lJgxi+Nsx2Q8ZUFEDg1GK4jIN0XkDhHZqelEcxxnu2DeEhvrK6uATZth+mewYZOdy82FvjvAzv1c4TmtgobM0/sHUCoi+wI/BxYC/2oSqRzH2X5Y/mXcQhNsf6UP5Tutk4YovSpVVeB04K+q+legY9OI5TjOdkOyuXo5PhfPaZ00ROltEJHrgG8Cr4pILtCuacRyHGe7YWCf2laZebnQu57pDI6zndKQyennAt8ALlHVZWE87/amEctxnO2GXt2gXR6sXGMKr08vKMhvaakcZ6vIuKWnqstU9Q5VHRuOF6nqo5mkFZFcEZkoIq+E464i8qaIzAm/XSJxrxORuSIyS0ROiIQPE5Gp4dydIuITghynuehaYi7GBvWDQld4TuulId2b28KVwIzI8S+A0ao6GBgdjhGRIcB5wJ7AicDdoRsVzJDmUmBw2E5sHtEdx3GctkKTKz0R6QucAtwfCT4deCTsPwKcEQl/SlXLVXU+MBc4UER6A51U9YNgTPNoJI3jOI7jZES9Sk9ERoff27byGn/BpjhEbJ7ppapLAcJvzxDeB1gcibeE+Pp9S5KEJ5P3UhEZLyLjV65cuZUiO47jOG2RTFp6vUXkSOA0EdlPRPaPbukSishXgRWqOiFDeZKN02ma8LqBqveq6nBVHd6jR48ML+s4juNkA5lYb16Pjbn1Be5IOKfA0WnSHoopy5OBQqCTiDwGLBeR3qq6NHRdrgjxlwD9Iun7Al+E8L5Jwh3HcRwnY+pt6anqc6p6EvAHVT0qYUun8FDV61S1r6oOwAxU/qeq3wRGAheGaBcCsTX6RgLniUiBiAzEDFbGhS7QDSJyULDa/HYkjeM4juNkREPX0zsNOCIEjVHVV7byurcCz4jIJcAi4Jxwjeki8gy2KG0VcJmqVoc0PwQeBoqA18LmOI7jOBkjZgyZQUSR3wMHAo+HoPOB8ap6XRPJts0MHz5cx48fX39Ex3EcZwsiMkFVh7e0HE1BQzyynAIMVdUaABF5BJgIbLdKz3EajU2boawciguhqLClpXEcZytpiNID6AzE3KuXNK4ojtPCVFfD2o2AQueOYWVwYOEXsGhZWDZZYXB/2MF9TzpOa6QhSu/3wEQReQv7+x+Bt/KctkJFJXwyA6qq7DgvD/bfA6qqbXXwmsgwwJyF0L2zxXEcp1XREEOWJ0VkDHAApvSuVdVlTSWY4zQr8z+Hior47M/qCls8tVc3EKHWtFARU5Ku9Byn1dGgf22YOjCyiWRxnJajtKyuu4PSMhvDS2brVVDQHFI5jtPINJfDaact8bvfwW67wbHHwvnnwx//CCNGQMxSdtUqGDDA9qur4Wc/gwMOgH32gX/+M57P7bfHw2+4wcIWLIA99oDvfQ/23BOOPx42b7Zzd94JQ4ZY/PPOa9x76tyh9mKpOWLjegX5sPtAO87JsXG+vQZDrv91HKc14v0zTsOYMAGeegomTrTxr/33h2HDUsd/4AEoKYGPP4bycjj0UFNkc+bYNm6cGYecdhq88w7stJOFP/kk3HcffP3r8Pzz8M1vwq23wvz51spau7Zx76v/jrCpDFavs+POnWDAjrbfowt028+6NPPbJV9J3HGcVkFGSk9EcoApqrpXE8vjbO+MHQtnngnFxXZ82mnp448aBVOmwHPP2fG6dabURo2ybb/9LHzjRgvfaScYOBCGDrXwYcOs9QfWwrvgAjjjDNsak5wc2GsXU+SKLZqaeL7QuzQdp7WTkdJT1RoRmSwiO6nqoqYWytnOSbZ+b14e1ISFNMrK4uGqcNddcMIJteO/8QZcdx18//u1wxcsqD1elpsb79589VVrDY4cCTffDNOnN74xiRunOE6bpiH9NL2B6SIyWkRGxramEszZTjniCHjxRVNEGzbAyy9b+IAB1vUJ8VYdmLL7xz+gstKOZ8+GTZss/MEHrYUH8PnnsGIFKampgcWL4aij4A9/sO7NWFpn+6Si0rqLN5a2tCSOs4WGfNb+psmkcFoP++8P555r3Y/9+8Phh1v4NdfY+Nu//gVHR/yQf/e71nrbf39r9fXoAS+9ZON6M2bAwQdbvA4d4LHH4hPCE6mutnG9dessn5/8BDp3brr7dLaNdRtg6hxAwnPvArsNSN5L4DjNSMa+NwFEpD8wWFX/KyLFQK6qbmgy6bYR973ZDNx4oymsa65paUmc7Yn3J0FlVfw4Jwf2HARd3ZFTa6At+97MuHtTRL4HPAfEbM77AC81gUyO07SoQnlFfAzSaVxUays8C4TN5S0ijuNEaUj35mXYKgsfAajqHBHp2SRSOa2HG29saQkaxvqN1u1WUwOIzcHr0aWlpWpbiJila1lUyQl0KGoxkRwnRkMMWcpVtSJ2ICJ5JPdV4TjbJzU1pvCqqs2XZk0NzJxvrT6ncdlrF5v2kZNjSnCn3lDSsaWlcpwGtfTeFpH/A4pE5DjgR8DLTSOW4zQB5RW1HUeDVcibNpvnFafxaF8EB+1jZd4uz6eCONsNDWnp/QJYCUwFvg/8B/hVUwjlOE1Cfjsbb4qiNa7wmoqcHFt70BWesx3RkFUWasLCsR9h3ZqztCGmn47T0uTmwq79bWkgEXuL+/ayVonjOFlBQ6w3TwE+A+4E/gbMFZGTmkowp40ydSoMHw477GCuxFavrjdJo7JDdxi2pxmw7Lc7DOzTvNd3HKdFaUi/w5+Ao1R1LoCIDAJeBV5rCsGcNsjKlebRJTbB/LXX4MQT4aOPmnfScnGhbY7jZB0NGdNbEVN4gXlAGr9RjpPAu++axWSsV7yiAiZPhjVrWlYux3GyhnpbeiJyVtidLiL/AZ7BRkPOAT5uQtmctkaHDnUnhNfUQKG3uhzHaR4y6d48NbK/HDgy7K8EfFavkzkjRsDuu8O0abYSQ3Ex/PCH8WWKHMdxmph6lZ6qfqc5BHGygHbtbD2+u++GefPMWfXXv97SUjmOk0VkbMgiIgOBHwMDoulUtZ5VRB0nQmEhXH118nPr1tnyRJ062eKx7pHfcZxGpiHWmy8BD2BeWNxTr9O4fPqptfyqqmwZocMPt7X6fGJzq2DMGPjjH+GVV1paEsdJT0NqlDJVvbPJJHGym29+06w4Y5ad77wDjzwCl1zSsnI5jtOmaMiUhb+KyA0icrCI7B/bmkwyJ7tYsKC2i7DSUpgzp8XEyUYWLDA7o+9+F/baCy64AP77Xzj0UBg8GMaNs+2QQ2C//ex31qy6+WzaBBdfDAccYPH+/W8Lnz4dDjzQ1h/eZx9/vE7L0JCW3t7At4CjiXdvajh2nG1j773hvfesaxOgfXtbbd1pVubOhWefhXvvNaX1xBM2vXLkSLjlFnj0UWuE5+WZQvy//4Pnn6+dx+9+B0cfDQ8+CGvXmqI79li45x648kpTphUV8UftOM1JQ5TemcDO0eWFHKfReOIJm9LwxRc2rvftb8M557S0VFnHwIH2/QGw555wzDFmT7T33tYSXLcOLrzQWmkiUFlZN49Ro0xJ/vGPdlxWBosWwcEHm0JcsgTOOstaj47T3DRE6U0GOuNeWJymoE8fmDkTFi6Ejh2hR4+WligrKSiI7+fkxI9zcuxb5Ne/hqOOghdfNCU4YkTdPFSt9bfbbrXD99gDvvIVePVVOOEEuP9+axE6TnPSkDG9XsBMEXlDREbGtqYSzMlCcnNh551d4W3HrFtn3ycADz+cPM4JJ8Bdd8WHaCdOtN958+zxXnEFnHYaTJnS5OI6Th0a0tK7ocmkcBynVfDzn1v35h13pG6l/frXcNVVZqyiCgMG2FSGp5+Gxx4zHwU77ADXX9+ckjuOIW15Sbzhw4fr+PHjW1oMx3GcVoWITFDV4S0tR1PQkPX0NojI+rCViUi1iKzPIF0/EXlLRGaIyHQRuTKEdxWRN0VkTvjtEklznYjMFZFZInJCJHyYiEwN5+4UcZcdjuM4TuZkrPRUtaOqdgpbIfA1bDHZ+qgCfqqqewAHAZeJyBDgF8BoVR0MjA7HhHPnAXsCJwJ3i0huyOsfwKXA4LCdmKn8juM4jtMQQ5ZaqOpLZDBHT1WXquonYX8DMAPoA5wOPBKiPQKcEfZPB55S1XJVnQ/MBQ4Ukd5AJ1X9QK1P9tFIGsdxHMepl4Y4nD4rcpgDDMcmp2eMiAwA9gM+Anqp6lIwxSgiPUO0PsCHkWRLQlhl2E8Md5y6LP8S5n9u6/X17AaD+roDa8dxGmS9GV1XrwpYgLXKMkJEOgDPA1ep6vo0w3HJTmia8MTrXIp1gbLTTjtlKp7TllizHmYvgJrweixdYW/PoH4tKZXjONsBGSu9bVlXT0TaYQrvcVV9IQQvF5HeoZXXm/ik9yVAtHbqC3wRwvsmCU+U817gXjDrza2V2WnFrFwTV3hg+yvXuNJzHKd+pSci6WbTqKreXE96wZYkmqGqd0ROjQQuBG4Nv/+OhD8hIncAO2IGK+NUtTpYkB6EdY9+G7irPvmdLCQvt25Y7lYPX7dapk41Jze77gr77ts013jxRbj0Upu0fsQRNhevW7emuZbjNAaZ1ASbkmwAlwDXZpD+UIKjahGZFLaTMWV3nIjMAY4Lx6jqdOAZ4FPgdeAyVY25pv0hcD9m3PIZ8FoG13eyjT49TfHFOsRzcrKulXfHHXDQQbZiwiGHmM/LGEuXmguwhx6C1aszzzNxSu/kybYi1KpV5oPznXfcXaqz/dOgyeki0hG4ElN4zwB/UtXt1henT07PYsorYOkqqKmG7l2hU/uWlqjZWL7cvKCUlcXDCgttGaDNm83/ZcxRdIcOMGkS9O6dOr+NG+Eb34DXXjNfnLfeCpdfDnfeaR5aysvjcfPykjuhdloXbXlyekZjeiLSFbgauACbXrC/qq5pSsEcZ5soyIcBO7a0FC3CF19Afn5tpZefb+E33QTr18dbbRUV5g7svvssbMwY8/m9//7mRgys+/LNN83hdFUVXHutrZDQrZspuajS69hxG4Uvr4DSMnt+xYXbmJnj1CWTMb3bgbMw45C9VXVjk0vlOM5Ws8sudbsia2psbG/p0trnqqrg888t7OKLbS09sOO//AW+9z1TeFEFWlpqa+n99rfWjTprlrXucnPhH//IQMB1G2HB51BVDTt0gx172nSSlath5gLb1xrYqTf0z84PF6fpyKSl91OgHPgV8MvIVAPBDFk6NZFsjuNsBR072vI9p51m3Zn5+WZw0rUrnHwyzJ5tiguguBi++lX4+GNTeJs2xfP58Y/hW9+C7t1t3C5GQQH06mW/779vxiurVsGRR8KwYfUIt7EUpsw2LQwwr8z2d+wFM+fXtrpdtAy6d4H2RZbu8zCS0rs7dOqwzeXkZCf1Kj1VzT6zN6c2paX2uV9Zaa71u3ZtaYmcejj8cFNEX35p3ZC5waD1xhutm/Oxx8y+54c/tO3f/47HiSECa9bYiucnn2y6KTfXxv++/32LU1Bg6/1mzPIv4woPbP/zFdCjK+E7urYAZeUWZ9KseLoVq2HvwdB5W/tSnWykIZPTnWxk9WoYPjz+qV9QAOPG2RLbrZVly6y507+/ba2YN94wRVZRAT/6EVxySfxcbi707Fk7frt2ZrX54IN2HOu42X9/6+qM0rWrteh69zZjl1GjrBX5ta9B+621C0rqk0Igv11djzlaA8VF8Nniuopy4RfQOWGVWsfJAG/FOem5+WYb9NmwwbbVq810rzkZNQoGDbJa+LzzzJwwE1Shurp22IsvWl6nnQa7726DUq2Ut9+Gs86CDz+ETz6xxVnvvz+ztCK1dcxOO1n3ZseOpiz797dxu5xQQwweDJddZq26rVZ4ADv0qD1nMifHxu5ycmCvXexcTg7kCAzuD0UFtRVejGRhjpMBrvSc9MybZ82IGDU1sGhR811/+nQ480yTY80aeOklmxxWH7/7HRQVWcv09NOti7a01NKWltps6rIy+NWvYO7cJr+NpuC+++Jjc2D7d22Du4aTT7ZiWb8eFiyAPfbYZhHrUlwI++0BPbpA1xLYbQDs2MPOde4IBw+FYUPsd4fuFr5jD1OCMXJyoHePJhDOyQZc6TnpOfpos3aIUVgII0Y03/VHjard71ZeDq+/nj7NCy/ALbdY3Opqy+Pyy810MbELLT8fPvus8eVuBvLz64blbeOAhUjtx90ktC+CIYNsXK5nwvhwbo4pxqhXne5dYNcBFl5cCLv0iytEx2kgrvSc9Pz4xzYzOTfXatSjj4Y//CGztJWVdbsXG0qnTnVr8qKi+P748fDXv8JTT8WV4+uv124ClZWZIU6fPnWVXkUF7NY6x4auuKJ2V2Nxsc25216YPRseecQexzb3RvbqBgfsZZu38pxtwJWek56cHOtH27jR+r5efbW20klGeTl8/esWr7AQrryy7sSxTDnvPNhxR8sHrGb/059s/9FHzU7+2mvN39ZRR5ni69u3bjOoVy/L46WXbOCqY0c7vvtuc1/SChk6FN59175JvvY1u7XTM173pGl5+WXYbz9rYJ9zDpxyig/DOdsHDXJD1tpwN2QtxE9/arOUN2+24+JiuP12My/cGjZsgAcegJUr4bjjrHtV1XxoRVt0HTqYIjz6aDNHXL7c4uXkwFtvmRUqmAJfsMBafl26bMudpkbVJrEtW2aT11qpYt1aunSBtWvjxx06wL/+BWec0VISOQ0h692QOU6DGDUqrvDAFNMbb2y90uvYEa66qnZYVVVtNyFgTYlVq6CkxLwh//vfJsdxx9WemtChA+y119bJkgmqZjATm/xWVQXPPGPNnSygpsaMYaJUV9v8QMdpabx702l8+vSJ27qDTQ5r7AV927Wz1lzieN9hh9lvhw5wwQXW7ZnpXLwHHjBfXYMHwz//mVmaDz6w7tvrrjOnlQCjR5vC27TJav/SUjj//K3v4m1l5OTA3nvXnuwuYqs+OE5L40rPaXzuvNMMUNq3N+XTq1fTWFi88goccIDVrt26mTHL1trZP/mkWYbMmWNTGK6+2rpK0/Haa3DMMXa/t99ui9YtWACLFvFJ9b7szwR25HO+wWNs2Ci1PTO3cV5+2aZB5ubGh073378JLqQKq9bAoqWwel0TXMBpa/iYnrP1lJdbZf/JJ1aj/exnNi8ObDxt1ChriZ1yiinBpmBjKazfaC2/7p3rWmdmyjHHwP/+Vzvs8MNtkbhU7LOPrdQaIycHrrqKJSdcwh4n9GMj7YEcCijj8KLxvFlqrdCRI60h2KOHDX/2aMPGiKWlpvRymuLzWtX8da5aa32qOTk2py/L1k5sCnxMz3ESqamBE0+Ejz6ycbPXX7duvdGjrfLp1cu8FTcly7+E2QvCgdiaefvsunWKL5mbkQ71ODVO9AxTUwMbNvC/ZUOgoBLKraYvp5C3yg+losJ6TX9xbQ2lm3PIy63hkUeE6dOladyZfvaZzRvYZRfrsm0BmnTOX2lZXOFB3I9nvx3MrZnjJMG7N52tY+ZM88EZM1jZvNmOZ85snuurwuyF5pW/RoP1xCb4cu3W5XfDDXUnvd1wQ/o0F15Yu1YvLobzz7egdrUr3ZxcIS8Prv+/Kko329+uqjqHdSsqeOqJJrDlv/deG1g7/3zrdr3zzsa/RktTWVXXl2eOWLjjpMCVnrN1VFbW7bPKyWmaZbM3l8Onn8GkmbBkmSk81SQTvxQqtrLCGzbMphj8+MfmZPK992yJ8XT8+tdwzTVs7LMb5bvsaWOARx3FKafYVMFYT29xMfzyl1Y8FZsqamVRVQObP5i0dTKnYuVKM67ZvNnmVm7ebHMZP//czCivvtq6mzt3NndtrXWIo0NR3VZ9To7563ScFHj3phNn0ybzcdm7ty2ilo4hQ8xKc948U3Tt2tkk8iFDtv76azdYSy0v17xu5LezlbQ/+dQWHAXYUAoVlbBzP+hQbGN6UUq2YZ21ffZJ3yKKjRsFNmzK4dS3f8N7y38DwGVj4c9n2Zz8jz+Gv/3N3JQec4xNHgc4V57lKT2HzVgLMZ8qvrrDeCC9lcf06eZ8pm9fm4aYtgd3yZK6S6cXFJgwDz9sfayx+Y233GLP7TvfSXv97ZK8PNh3N5j+mb0nRQWw5y5NNIDotBX87XCM996zyu/QQ61mrc9zcbt25g7kzDPNjdeZZ9pxu60cS1mxGqbOhiXLYeFSGD/dlNuqtVCduP7aStvfaxdTfGBmgrsPNL+Ojc3779uHQF6erdAwfTpg0w4//NCm4VVVmeOaRx6xJB06wC9+YVaLMYUHcM+w+/gu99OfBezHBEYVnMpup+yS9vKPPQYHHmjeTc44w6YApm2c7bxz3VZwZaWN6z3/fF0v1c8/Hz9Wrb2SbH2owpQpZgT05ZeZp2ssOhTDV/aGI4aZi7LiwuaXwWlVuNJzrII89VSbU7Zhg1ll/uIXWyr3lHTvbstmz5xpv5maIVZXW5dltGKetyS+araqteyWrUqePkZBvnnkP2IYHLZfWIi0kVm1ygx2loVu1XnzrKlVUcHYsbVnIZSWwpgx6bPLf+Ep7tz9Hywo2J1P2h3MITceb/lFiCq0qir43vcs740bbfv3v2Hs2BBh7Fg4/nhzx/bMMxZWUmJOtzt0sHHK9u3t+XTvXrcFn5MTf26vvmppS0psbuOnn8bjLVxorcTnnou3IFWthXjwwbbG0c4727huPdTUWEv47bftdXOc5sS7Nx2r2EsTugnz8qzS23PPxr3W0pUwZ5H1z+WIWVt2bF/XMbWqtfB6dYP5n8fPJ1tWZmunKWTClCl1u8tKS2H+fPr1241Fi+JKqqDA6v209O1r5frll6aUCuMtkwcegJ/8xIbgjj3W9FR1dd1GW05O8G7y4YemkGPPbvx4a9FdcIF5oVm50iL27h33l3r77TaBv7zcyq19ezPYWbTI/KXG8lq0yIRYssQsdI87zsJFTCGOG2etu8SW4znnxCfpJ6GyEk44wZLH5vC9/741oB2nOfCWnmMTuxMdNFdVNb6Ze2kZzF0UN0Kpqoapc+y4R5eENdMEupVYa27/PaBbZ5uSMKA3DOrbuHKlo2fP2usJgtXc3btz773WKIr5rx40yGxE6kXEWlwRhff22zY3fsMGK/q33oKLLjJbk969QSTe/KuuNrsb7r67blfl7bfHjwsLTQtHHYTvu6+5aLvlFrj1VmvNDxhgcy0TvdusWWPzLb/3Pevy3LTJmpqffWbjgnPn1jVc+vzztLf+z3+aro45q1m1yoxgHae5cKXn2Cf3c8/ZV39JiVWW115rbvwbk02ldVtlVdVmYr7LTtCrO7TLg8ICW2+tUzBKKS608bv99oB+vZu2ZZfIXnvZfMP27U15FBfbwrPdurHHHjYN7sEHzaHLJ5/UP7UvFaNHw+bNccVWXm5hIjDqD5MYyAJyqaKYTfzr6yPteyRZOWRSNjvvDNdcY83K3r0tbMcda69bCPYx0qWLKb4oZWWm3Pbdt7aiFIl/KFVVwY03msec0083TzdYIzfqlrWmpvYavqqmaxNbt+Xl9mEwZkzt9I7TYFS1zW7Dhg1TpwGsWKH6zjuqn33WNPmv36j6zgTVMR/Ht3cmqNbUNM31GouaGtU33lD9+99Vx45tkkvcedNqLWKTxudjqA7ostau3bWrKuhGirUGVIuLVSdNUv3oI9uPJSguVn3yya0ToKZG9eKLVdu3V+3QQbWoSPW+++zc2Wer5ufXvs4rr9i5X/9ataDA0uywg+qMGRZ+8cVx2XJyVDt3Vl22TB98sLbIeXmqJ5xgSSZOVO3Vyy7Vvn38EmvWqO62m2rHjrYNHGivar1sLFVd8LnqoqWq5RVbVy5ZCjBet4M6vCm2FhegKTdXetshcxeZohs7wX5Xrm5pibZQXa366aeq06erVlXVPrd+verMmaqbNjXNtTfe9CfdnU+1PRu0kFItZqOOKjlb9csvayscsJr/iScs4fvvq371q6rHHaf64ovbJkRNjepbb6k+8ojq5Mnx8LVrVY8+2pRXfr7qbbfVTrdqlers2arl5XZcXW3aLCpzcbHqffdpdbXqt75lerK42JTZ0qWqFRWqPXrUTbJ4sepll9UugnbtLI+0rF0f/8B6e7zquxNVy8q3rXyyiLas9NyQxWleBvWDHbrbvKr2RTZmtx2waZMZUcYMVvfYw+w0OnY0P9YXXxxfNeCll2zuXWPSXkr5JOcAnq05i3WUcAyjGZK73Ab1CgtrjytWV8e7EQ8+2Lw7NwYitlZhIiUl1tdaWWndmYldqN262RbNJzGOCOTmkpNjc/hvu82GIAcMsHJduLDuTIl27cyOaMaM2rdfWRl3/KNqfr/nzDEHNFsMYecujveRqlp36+Jl1o3uZDU+puc0P+2LoGtJiyi8qipbeP1rXzOHKrGK9pe/tAo2Zq8xdaqtFrRkiSm8zZvjUwbOPLOuses2c845FBXBt/kXP+ZvDCleaAYkOTnx6QclJTau+NOfxhfEbU7atctszFDEJjHGXLTl5dmYaGRZ997F6xjUfhm5OTaO2b17XQPeykrzf3DIIVBUGB/vLCxUDj7Y9i+91IxOr70WTjvNniMQd2ZQK0N3T+bg3ZtOdnHWWfExpcJC1f33V62sVD388Npda6B60EGq//2vaklJ7fAOHeJDV43Kxx+rjhihuu++qrfcYt2EMVautPHWuXOb4MJNQHW16l//qnr88arf+Y7qkiUWXlNj/ZXt2lkf59Ch1j2qqvfeXaHFeeXaMXejFueV6RWXblZV1bIFS/Wkgv9qAZu1kFI9st27umnmIv30Uxt6jD6bggLVZctU9bPFqu+Mrz12HOtKr6pSXfiF6qwFqiu+3P7HlFsAvHvTaTN89pn5W1y9Gr7xDftMzhKWLbP517EJ5WVlZn350UdmiDhuXPxcQYEZrw4YUHfGQnW1GTs2OsOH21yFZHTvbksdtRZycmwOxhVX1A5/4gl46KH4VIfp022C+8iRfO/l0zkkdzmTy3djYN4SDv7fUiibSsHNv+LVqkdYRncUoXf1CuTXZ7Hy8mfIz69tzZmfb1Mge+3Rxx7U8tU2/WXAjtC9i3V5fjIDysrNGcLyL6FvKQxsxmkwToviSi+bWLTI1r3bsME+jN98E1asMP9W6aiuhtXrgwl7x8xcja1cbcu85OTATjtA5yZaTy8Nq1ZZhRhbyL2yMrl/4ooKm7b23numBEXMqv/WW61H8eabrSs0P9/iPvBA0y0P2OZ5773afcOVleaeZcUK+N//2LO8nD35BKqA5R1t5vqCBUh1Fb1ZZmlqgAUL2HtvoLwMsPmOQg1FlDNoUHBEPbi/bVFWr7Px5Jj3n5oaG+sb0MfSVNfA5jLz/1rojqvbIj6ml008+qhVOBr+8KWlVtvHqK6GTZtrj31UVsHH02HWfFu7btw0qxRiVFVZmugcrxVfwswFsG4jrFlvE9DXNZ+/qZoaG4fr08fcgu63nynAvn3N2CG2+kFurimvAw80g5WPPzb3oWPHwoQJpvDAhtCmTrWpjLNn22o9zlYyeHDtyfIxDy+qyeOrmjeY6BJORUVw3HF0WTufN/U4dmIhOVSzC3N5q/wQCjatTn396hpIvJRiSrC0DD6aYqt5fDwNZi2oLVdZuTk4r7O6h9OacKWXTVRW1v3DxpTVuo3wwWSYOMN+l4QJyQu/MMfP1TW2VVWbVxWAlWvggynxNCvXWPji5bWvU6PW6msmHn7YXHhVVFhLb8YM+O53rX59800491xThiefDB98EF9GLzfXujSHDq3rnGTQIPPK1dd7wbaNH/3IVrPo0MG+OLp0se7Onj2t+zbmpaZdO+vSPeQQm0h/7rn2gHJzzWIluE47oHAqCxlAFXnMZjeGFHwGS5dSVQVXnrmIrvkb2aF4Hf+84QvLt3PH2mvwiUBJR8jNseWrKqvsPa9Rc4L+5VpTfLMWmCKcNBM+nGIK0mmVuNJr7dTUJDclXL7cBrA++CD+tXruubW/souL4fvft/PT5sQVmyrMX2ItuLKKul/hZRVWOcycZ9ePVRIz5zWJhdzCheaS66ST4J57aouzfDm88QZMmhQPe//9uj1o48fbfkmJrYQwcyaMHOlKrNkpKLDm9Msv2/jenDk2P0TEHsiPfmTrGJ5/vg22FhWZonvwQXuomzbZHJL8fNh99y1jg7V6rQcM4PpzZ3H/S91YU9mB5ZtLuPqmEkb+fTEU5DPvC+XUq3oz9KJdufo3BZR37WLpNtuA7vpNOVRWib3bmzbDqjWmAGuCP9jKKpjxWfOWm9No+Jhea+axx2w8rqAQuneD//zHuorefx/OPAsG7gxr18Luu5rZ+5Ah8OZomDIzmJ/XwLfPj3/dRhGB0s02hrdmPSu+tIpgx55VSJdOVkGIoKqUluVQXFiDiFjXZ78d7Ms41trLEejTCzDvVVdfbfY0Rx5pNjWxj/sJE+wWSkrg29+2KWrLl9sw5Lp11vs6dqzlcfPN5pLq1FOtTqystHryvvusDi0sjC8GkJOTgSNop/nIy0s+H7CoyOaTpCLRP2yvXrayxLnn2pdQXp4pzvbtefaVQkppvyVqKe157s5pHHqYcOC5A1hDCTXkMXvBziwY+hEvrO7J8vX5nHjlQKYtKEIEbrl4Mdf8thA2bWb9+hoeGdWTNRtyOeGA9XxlSJjrUlEJYz4AcqG6CobtBT274WzHtLT5aEM34ERgFjAX+EW6uG1lysJ//u89PfeA+fq9w2bprGeCp4zJk3X5qT/QH536hZ560Cq9+7tTtGbEMaqqWnPI4frY1dP0nMOW6+Wnfq5fXPEn1WefVd20WWc+Pk0vOHaVnnTgGn30l/PMdLumRjeOmqiXfnW57r5TqZ544Fqd99RU1Q2btLKiRs86bqPmt6vWwvxqPXjfUl2/tlq1vELfv3uG9iip0NycGu1RYsdaXqHV1aq/urpUe3at1N49KvXvfzTT8/XrVXv3jjvrKCpSPeUUu52XX7apBDk5NpWgXz9zP/X3v9c1S2/f3tJ07143fNQo1bIy1YMPtqkFnTqZp485c5r5oTnNR1mZ6qJF5tYlMLxgSq13I5cKvWrIG/rU5WO1A+sTzlVq2epNemT3qZqXWx33CNOuXP9723hd98TrOrBXqRbmV2uO1GhRQZU+e8WHqqq67OH/6ZH7rNXigirt36tM3759suq6DaqqOu3xKXrxwTP1/OGf6X9v/nCLbBVrS/X350zSs4Yv1hvPmKKbV6zfcm7MnVP1nOEL9PwD5+mHD3ya0e2/8ptP9NwD5umlR8zSOa83jgtB2vCUhRYXoEHCQi7wGbAzkA9MBoakit8WlN6Tl7+vxQVVCqpCjXYsqtQ5L03Xtf94Snt3LdvyJy0urNKfnDhbtaJCf/f1aVpcaGnycqu1Z0m5rvrz4zp/7FLtWFylIjVb0vz5isWqqnr04ZVamF8dXCVWa49uVbpmjervf1/bV2JBgeoll6iuW6faqWN1rcqjU8dqXbeubpriYtVnnlEdOdI8aEXTtGtnXq4GDKgdXlCgevvtqnfeaUoweq6oyOo3kdrhxcWq99xj5VZVpfruu6YE161ruefntAxjr3hGi9mouVRqPmXajVW65PkP9dlrP06q9MpLq7SYjbXCc6jU3x77lt518n+0KK+i1rkdcperbt6s++2yoZaibF9YpQseekenPzVN2xfG/2tFBVX60s8/1Jqqaj1pny+0KPynC/Or9ODBq7SqrFLfvH3ylnBQLS6o0g/um572Ph++bHy8fpAa7VRcqfNGz9/m8mvLSq+1jekdCMxV1XmqWgE8BZxeT5pWzfXP7U1pufm/UoSNZbncc3cNr8zfk/WleVRV2yMsLcvlrjcHUSO53Pry7pSWWZqq6hw2bM7l2YUH8q8XiiktF1RlS5rbHuvF2rUw9sM8yiosr5qaHMorc3n7bRsSjI6PlZfb0jCzZwOS8PpIDrNm2YoDiSvePPlk3WXpYuTk2DIzUSoqzNv+GWdYr1ZsqkFxMVxyifXODhhQN6/YwhC5ubYI/HHH+fSCbOSwv5zNuOte4saed/O7vvcw7fHJ9DnrK5x47b50zd9EO2zyZTGbuOQr08gvyqVXXu2V34sop8+Adqzt1I+KqtpzXTZqezauqWLq/PZb/oMAOaK8N7mYO+/Oo7Q8Z8t/bXN5Ljc8tRsLxixkzMyebA7/6bKKXKYsLmHKs7P57f07bAkHKC3P5Q93d0x7n795do94/aDCprJcHvizr8ybjtam9PoAiyPHS0LYFkTkUhEZLyLjV65c2azCNQWV1bUfkapQVplHZb+dSbS9VhVUcqiqrv0HrVGo6NmH6qKiLX/CGNWSQ7t2dW1VVOO2AgWR6Up5ebDrrjackjhpu6ICdtghbuofIyfHjPSOOsoM8mLT/IqLbcHtjh1tbC6yvByFhWZd2a+fKdmTTrI15K67Dv7yF4vzyismR/v2JutNN5kNhOMgwp63XMCvll/BNYuvZIdvmFPODl3aMWFeF74/YjanDJ7FLT9YzD/eHwrAo3/bQAc20pH1dGAD+5fM5YI7v8IJP9mTgty4W7NCNnPyCdUU9uiAaO2xcK1ROu8/gLKqvDr/tYqqXCo219T5+MsRqChXKqrrVscVCf/lOucTlHF1DZRXtrZqvZlp6aZmQzbgHOD+yPG3gLtSxW8L3Zu/P2/qlu6LWJfHh4/O1mVLa7Rzx2j3SbWed5Z5kb/kwspImhrtUFyt8+aZ66z2xTVqalC1uKhGb7jBXDB95zvxLsn8fNVdd1XdvNnG4fbe27olO3VS7ds37lHqhhssTfv29nv99Rb+wQfx8bm8PHPjFfOetXKl6ve/r3rssao332wuwFRVS0vNc36nTjbu99RTmZVPRYWthLR2beOUt5PdLBq3VJ+6+kN9/daJWlURdwP3wguqfXpVaqf2VXru2ZW6caOF/+l3pVqcV645Uq3t88r0sKEbtLJS9e27P63TVfnX70zWqrJK3avPGs3Pq94y/DCwx0bdvHqzPnnVuDr/9dduGp9W3htPn1wnzYRH03eJZgJtuHuzxQVokLBwMPBG5Pg64LpU8duC0quprtHbvz1N9+yzVg8YuErf/NusLedmzazRE4+t1L33qNJrflK1ZWWXigrVn/+sRnfftVoPP7RaJ0yI5zd+vK1Cc8ABqnfcEXc7WF2t+pe/qJ55purPflZbiZSXq44ZY34oY3/2GB9+qPrQQ6bookybZkut3XST6sKFjVcejrO9MXq06m9/q/rgg7VsafTV26brsAGrda++a/XO707Vmmr7s305Z7WeO+wz3bXXOj1j3/m6dPLyLWke//FHOmzAl3rgzqv0pes+rvfa1RVV+vtzJuqQPuv0wJ1X6Vt/nlxvmkxoy0pP7P5aByKSB8wGjgE+Bz4GvqGq05PFHz58uI6PTdByHMdxMkJEJqhqCyzl0fS0qnl6qlolIpcDb2CWnA+mUniO4ziOk0irUnoAqvof4D8tLYfjOI7T+nAzH8dxHCdrcKXnOI7jZA2u9BzHcZyswZWe4ziOkzW40nMcx3GyBld6juM4TtbgSs9xHMfJGlzpOY7jOFmDKz3HcRwna3Cl5ziO42QNrvQcx3GcrMGVnuM4jpM1uNJzHMdxsgZXeo7jOE7W4ErPcRzHyRpc6TmO4zhZg6hqS8vQZIjIBmBWS8vRhugOrGppIdoQXp6Nh5dl47KbqnZsaSGagla3cnoDmaWqw1taiLaCiIz38mw8vDwbDy/LxkVExre0DE2Fd286juM4WYMrPcdxHCdraOtK796WFqCN4eXZuHh5Nh5elo1Lmy3PNm3I4jiO4zhR2npLz3Ecx3G24ErPcRzHyRrarNITkRNFZJaIzBWRX7S0PK0NEXlQRFaIyLRI2M0iMkVEJonIKBHZsSVlbC2ISD8ReUtEZojIdBG5MnLux+E9nS4if2hJOVsLIlIoIuNEZHIot9+E8H1F5AMRmSoiL4tIp5aWtbUgIgtCuU2KTVcQkdtFZGb4z78oIp1bWMxGoU2O6YlILjAbOA5YAnwMnK+qn7aoYK0IETkC2Ag8qqp7hbBOqro+7F8BDFHVH7SgmK0CEekN9FbVT0SkIzABOAPoBfwSOEVVy0Wkp6quaEFRWwUiIkB7Vd0oIu2Ad4ErgbuAa1T1bRG5GBioqr9uSVlbCyKyABiuqqsiYccD/1PVKhG5DUBVr20hERuNttrSOxCYq6rzVLUCeAo4vYVlalWo6jvA6oSw9ZHD9kDb+2JqAlR1qap+EvY3ADOAPsAPgVtVtTycc4WXAWpsDIftwqbAbsA7IfxN4GstIF6bQVVHqWpVOPwQ6NuS8jQWbVXp9QEWR46XhDBnGxGR34nIYuAC4PqWlqe1ISIDgP2Aj4BdgcNF5CMReVtEDmhR4VoRIpIrIpOAFcCbqvoRMA04LUQ5B+jXQuK1RhQYJSITROTSJOcvBl5rZpmahLaq9CRJmLdKGgFV/aWq9gMeBy5vaXlaEyLSAXgeuCq0mvOALsBBwM+AZ0LXnVMPqlqtqkOx1seBIrIXVjFfJiITgI5ARQuK2No4VFX3B07CyvCI2AkR+SVQhf3nWz1tVektofZXXl/gixaSpa3yBN59lDFh7Ol54HFVfSEELwFeCN1144AazHGykyGquhYYA5yoqjNV9XhVHQY8CXzWkrK1JlT1i/C7AngRGyJCRC4EvgpcoG3EAKStKr2PgcEiMlBE8oHzgJEtLFOrR0QGRw5PA2a2lCytidB6ewCYoap3RE69BBwd4uwK5OMrBdSLiPSIWRKKSBFwLDBTRHqGsBzgV8A9LSZkK0JE2gcDK0SkPXA8ME1ETgSuBU5T1dKWlLExaZOrLARro8uBN4Bc4EFVnd7CYrUqRORJYATQXUSWADcAJ4vIbliLZCHglpuZcSjwLWBqGIcC+D/gQeDBMC2kAriwrXxNNzG9gUeClXYO8IyqviIiV4rIZSHOC8BDLSZh66IX8GLoWc8DnlDV10VkLlAAvBnOfdgWrLXb5JQFx3Ecx0lGW+3edBzHcZw6uNJzHMdxsgZXeo7jOE7W4ErPcRzHyRpc6TmO4zhZgys9Z7tERP4sIldFjt8Qkfsjx38Skau3It+HReTsrZRp9+CFfqKIDNqaPJqCMG/toyDX4duBPGNEZHhLy+E4yXCl52yvvA8cAlsmG3cH9oycPwR4r5llOgP4t6rup6pN7u0jzEPLhGOAmUGusY2QX8aISJuc6+u0XVzpOdsr7xGUHqbspgEbRKSLiBQAewATRWRYcNY8IbQGewOIyCAReT2EjxWR3RMvILY+4MNBqUbDh4rIh5F1xLqIyMnAVcB3ReSthPiXiMifI8ffE5E7wv43xdZ+myQi/4wpHhH5h4iMl8h6cCF8gYhcLyLvYk6To9fpLyKjg1yjRWQnERkK/AFzHDApeCghVX4icrzYmnOfiMizwR8oInKAiLwvtkbdOBHpKLZu3UNi66xNFJGjQtyLQtqXMSfFRSLyVJDraaCWDI6zXaGqvvm2XW7AAmAn4PuY95ebgZMxDyfvYEvKvA/0CPHPxbzvAIwGBof9r2DrggE8DJyNKYp/Ehw0JFx3CnBk2L8J+EvYvxFbry0xfnvMz2O7cPw+sDemmF+OhN8NfDvsdw2/uZjvyH0i9/zzFOXxMua1Bcy58kth/yLgb2nK8Odhv3sot/bh+FpspYx8YB5wQAjvhHnm+CnwUAjbHVgEFIbrLYncw9WRct8Hc048vKXfH998S7Z514SzPRNr7R0C3IEtD3UIsA5TLLsBexF3k5QLLA2tl0OAZyW+aEFBJN9fAx+pap0lVESkBOisqm+HoEeAZ9MJqaqbROR/wFdFZAam5KYGV3jDgI+DHEXYUjgAXxdbwiUPc6s1BFO2AE+nuNTBwFlh/1+Y4s6EWH4Hheu8F+TJBz7AynGpqn4c7ie2UPBh2MKsqOpMEVmILYcEtpxPbL3FI4A7Q7wpIhK7D8fZ7nCl52zPxMb19sa6NxdjrY/1mN9KAaar6sHRRCLSCVirtvRMMj4GholI10jFva3cj/nTnEnc56MAj6jqdQnyDQSuwVpWa0TkYawFFWNThtfM1IdgLD/BlNX5CfLskyKvdMscJcro/gydVoGP6TnbM+9hy5qsVls/bTXQGWvxfADMAnqIyMFgy/eIyJ6hpTJfRM4J4SIi+0byfR24FXhVgnf5GKq6DlgTsYL8FvA29aC2iGk/4BvYsjZgXaxnS9z7f1cR6Y91H24C1olIL2wNs0x4H1sxBGwR33czTBfjQ+BQEdklyFMstrrDTGBHCYvYhvG8PKwr9IIQtivW1TwrSb7ReHthXZyOs13iLT1ne2YqNg71REJYB1VdBSA2/eDO0C2ZB/wFmI5Vwv8QkV9hY39PAZNjmajqs0HhjRSRk1V1c+QaFwL3iEgxNtb1nQzlfQYYqqprwjU+DdcfFYxlKoHLVPVDEZkY5JxH5laoV2CrMvwMWNkAuQjyrBSRi4AngzEQwK9UdbaInAvcFQxhNmPL9dyNlcNUbJzuIlUtl7rr3P4DeCh0a04CxjVELsdpTnyVBcdpJETkFeDPqjq6pWVxHCc53r3pONuIiHQWkdnAZld4jrN94y09x3EcJ2vwlp7jOI6TNbjScxzHcbIGV3qO4zhO1uBKz3Ecx8kaXOk5juM4WYMrPcdxHCdrcKXnOI7jZA2u9BzHcZyswZWe4ziOkzW40nMcx3GyBld6juM4TtbgSs9xHMfJGlzpOY7jOFmDKz3HcRwna3Cl5ziO42QNrvQcx3GcrMGVnuM4jpM1uNJzHMdxsgZXeo7jOE7W4ErPcRzHyRpc6TmO4zhZQ71KT0R6icgTIjJPRCaIyAcicmZzCJdCnhEicshWpFsgIt2ThG/cBlkeFpH5IjJJRGaKyA1bmU9nEflSRCQcHywiKiJ9w3GJiKwWkaTPS0QGiMi0sH+RiPwtSZwbReTzIOs0ETlta2RNku9Wl18kjx+IyLcbQ55IniNE5JVtSH+RiOyY4twYERmeJHyoiJycIEOD39U0Mp0vIr8M++1EZEI98W8SkWPD/lUiUhw516Dntq3lua2ISG8RGdVS18+E8B+7Jkn4lv9nA/LqLSKjmrLcReT9DOIcLiLTQ71R1BRyJLlmrf9NY9cPaZVeqIRfAt5R1Z1VdRhwHtC3sQRIcd28NKdHAI1WkTQCP1PVocBQ4EIRGdjQDFR1LbAM2CMEHQJMJH6fBwEfqWrNNsr65yDrOcCDqZRoc6Oq96jqoy0tRwIXAUmVXhqGAidHjkfQuO/qicDrYf8wIG2lparXq+p/w+FVQHGa6Ns7JwJvNOcF66mHmpomv19VzeTdvAD4o6oOVdXN9UUWkdxtl6z2/6ax64f6Kr2jgQpVvSciwEJVvQvsBkXkdhH5WESmiMj3Q/iI8DX8XGgBPR5pxQwTkbdDq/ENEekdwseIyC0i8jZwpYicKiIfichEEflvaHEOAH4A/CR8eRwuIj1E5Pkgw8cicmjIr1v4UpooIv8EJNVNisifROQTERkd8hskIp9Ezg+u76saKAy/m+q5z0Ei8noIHysiu4d07xF/0IcAf044fj98MY4Nsn5SXytCRE4Ra5nXauGq6gygCuguIi8FWaaLyKWRtBtF5HciMllEPhSRXiF8YMjzYxG5ORK/Qyi/T0RkqoicHsLbi8irIZ9pInJuEjm3fCGH9+AvIvJ+iH+giOSIyBwR6RHi5IjIXBHpLtbavjPEnyciZ0ey7iQiL4rIpyJyTzIlLyLXh3uZJiL3inE2MBx4XFJ/4Z4jIuNEZHZ4D/OBm4BzQ5prqfuuPhzkGBvSfTXIsGfIa5LY/2hwEjkFU6qx9/JE4LVw7tdi/7M3ReTJSFk+LCJni8gVmAJ/S0TeiuSZ7Pk+HC1Dqd0izKQ8dxH7v04O78KgUKa3hzKeGnsHxOqJt0XkmVAet4rIBaEsporIoEjW0fv9eTg/WURuDWF13psQ3lXsHZ8S7nOfEN5eRB4Mz35i5H29SESeFZGXgVFiLa53JN5DcniId2K4v8kiMjoi55Agy7xQ7onls3O43gGSui6odb+pyl1Ejhf7L34SZO4QwpPWPUlk2Rh5DnXqaxH5LvB14PpIWKrn+JaIPAFMzfS5SuZ1fLR+SPZ+JX1GKVHVlBtwBdY6SHX+UuBXYb8AGA8MxDT1OqxFmAN8gH2ZtsO+TnuENOcCD4b9McDdkby7ABL2vwv8KezfCFwTifcEcFjY3wmYEfbvBK4P+6cACnRPcg8KXBD2rwf+FvbfAoaG/VuAHydJ+zAwH5gEbARuCeHp7nM0MDjsfwX4X9i/KBJnIqZE3w3Hb2IfIMVAYQgbDIwP+wOAaZF8/gacCYwFuiSWW7juF9iHQNcQVgRMA7pFyuXUsP+HyHMeCXw77F8GbAz7eUCnsN8dmBvy/xpwX6TMSpKUY1S2MbH4wBGR+7oBuCrsHw88H3kGz2Lv2RBgbggfAZQBOwO5oQzPTnLtrpH9f0XueQwwPMV7P4b4+3gy8N9o2Se7r4isrwdZBwNLwnO+i/g7mA8Uhf3/ADuG/f2BRyN5jcPeh+HY+1cEdATmRMry4dg9AwuIvP9pnu+WNOF4YwPL8yPgzLBfGGT8WoifC/QCFgG9Q55rw34B8Dnwm5D2SuAvYT8XmBT2T8L+W8XR50fq9+Yu4Iawf3Qkn1uAb4b9zsBsoH14hksi+f4U+GVEjo5AD2AxMDBBhhuDbAXYf+BLrC4YgP23dsP+27F6JVVdEL3fpOUe8n8HaB/iXYvVXynrniTPKvps69TXSd6hdM9xU6Q8Mn2umdbxW45J/n7VeUbJ7je2Naj5LiJ/x5RXhaoegFU++0j8y7AE+zNXAONUdUlINwl78GuBvYA3xRp+ucDSyCWejuz3BZ4OXyn5mHJJxrHY11XsuJOIdMRe/LMAVPVVEVmTIn1N5LqPAS+E/fuB74jI1diLc2CK9D9T1efCV9ZosdbX+mT3GeIcAjwbkbcg/L4H/EKse3SBqpaFL6sOwDCskmsH/E1EhgLVwK4pZDoKqwyPV9X1kfCfiMg3gQ3AuaqqInKFxMdo+2HP70vsGcbGEiYAx4X9Q7GXH0xJ3Bb2BbhFRI7AyrQP9seYCvxRRG4DXlHVsSlkjvIkgKq+IyKdRKQz8CDwb+AvwMXAQ5H4L6l1/X4qocUSGKeq8wBE5Ens3X0u4VpHicjPsT9PV2A68HIGMsbekwnYu50pzwRZ54jIPGB3rJL5pdgY7guqOgdAVaNdpdGWzo7AalUtFZHDgH9r6HoSa6FkQqrnm4605Rn+d31U9cUgf1kIPwx4UlWrgeVivTkHYP+Tj1V1aYj3GRAbt5uKvcdgCuGjsH8s8JCqloZrrI7Il+y9OYzwvqrq/8R6gEqwuus0iY/BFWIfzQBvRvL9GBsKaIe9Z5NEZAQ25DM/iQyvqmo5UC4iK7D/AJii/DfwNVWdXk9dEL3fVOVehn3kvRfS52Pv0W6kr2NTkay+fjchTrrnOC5WHoFMnmumdTwhn1TvV51nlC6f+pTedOIVHKp6mVhX2fiYHFgLqFbfc3gpyiNB1eFaAkxX1YNTXG9TZP8u4A5VHRnyuzFFmhzgYE3obw4PXFOkSUcszfNY6+J/wARV/TJtItWNIjIGezFeI8l9ikgnYK3auFpi+jki0gU4FXt5wSqj7wDzQ/43AsuBfbH7Lkshzjzsy3BX4s8KrNX+x4g8I7BK5OBQgY4h3k1bqeHTifjz2yJukmtegP2xh6lqpYgswFqls0VkGNYi+r2IjFLVm1LInSp/VdXFIrJcRI7GKoULIuej71q0G7tOPtEDESkE7sZadItD+RaSGbFrJpZNfSS7tydE5COsR+INEfmuqv4vId7xxP+LJxEf70nZbV8PqZ5vFWHYQ+xPlJ9O9oTjVLKkkzH67GoixzURmU4iPpYpSa6bTr5k146Ff01VZ9USVOQrROqhoECPwJ7Nv0TkduzjPZUMyeo9sJbUYuyjcTpWxknrAmrfb7r7elNVz0+Qf2+S1z39iH/M3aORIat65K6VTZKwGJsSjjN5rpnW8Wmvn+wZaZoxwPrG9P4HFIrIDyNh0cHwN4AfBg2LiOwqIu3T5DcL6CEiB4f47URkzxRxS7BmMcCFkfANWBdDjFHA5bGD0AoCa/pfEMJOwprSycjBugsAvkH4uglfEW8A/6B2qyIpYoPeXwE+I8V9hlbXfBE5J4SLiOwbyeYDrPn/QeT4KuIGCyXA0tBS+Bb2FZeMhVgr99E05RvLb01QeLtjBjP18R5mzAS1FU8JsCIovKOA/rClVVKqqo8Bf8S66eojNlZwGLBOVdeF8Pux1vgz4WuzPg4UG4PMCXkmfrnGFNyq8OUdHQ9MfM8yITFNsjzOERuTHIR9mMwSkZ2Beap6J9Z9vE80QWiZ5EU+vKLjPe8Cp4pIYbiHUzKULRULsJ4FgNOx3oUYacszvN9LROSMIHeBmMXoO9hYZ67YuOwRWM9FphyDdQWC/d8vDvkiIl0j8ZK9N9F6YASwKsj5BvDjoNgRkf2SXVhE+mPv9X3AA9j7+wFwpASjtQQZUlEBnAF8W0S+UU9dEL1fSF7uHwKHisguIX2xiOxK6rpnsZoxytAkCi9TtvU5JpJpHQ+kfr9SPKOUpFV64UvwDOwBzxeRccAjWP8xWCX0KfCJmEnuP0nz1auqFVjFcpuITMbGIlIZY9yINf3HAqsi4S8DZ0oY5MTGHYeLDVR/ig2CAvwGOELMIOV4rP85GZuAPcUMVY7GjBFiPI59VaUzlb49dAdMwZruL9RznxcAl4Tw6VjFEuM9rIsx1jr7AKsYY0rvbsxC9EOsFZf4dbWF8AV7AVaGg1JEex3IE5EpwM3YH6k+rgQuC10KJZHwx7HnMD5cd2YI3xsYF8rol8BvYYs5fappE2vEzKnvAS6JhI8EOpDBR0jgA+BWbDxlPvBi9KSa1ex92HN7CevKivEwcI80zFT7LayrfZLYIH/iuwpWKb2NKa0fhI+rc4FpoYx2Bx4FEJH/hI+G44D/hrBcbBxoZriHj7FymYx1uY7HWhWJ3Au8JhFDlhTch/3fx2EfcdF3LGl5isj9Ep/C8S3givBOvQ/sEOJNCTL+D/i5qi6rRw5C3j2Aslg3vaq+Hu53fCiv6BSBZO/NjYT6Icgeq1xvxhT6lFB33UxyRgCTRGQi1tL+q6quxOwZXgj/46dTpK2Fqm4CvooNM5xOkrog8X4Ddco9yHAR8GS4tw+B3RtYxzaUrX6OKbiRzOr4KMnerxEkPKN0F40NIjpJEOvvL1HVX7e0LNmCWBfrNao6Psm54VgXbXrrrO0UEXkYG9dMHFesL939wP2q+mFoxXxTVX8QOd8hdH/HWlWXquonqfJrTYiNQfdV1VvriTeGFO9NayLT+3W2npach7JdIyIvAoOw1p/TwojIL4AfUrtLNStQ1e9G9t+lbjftvSIyBOuufaStKDyA0C2eNWTb/bYE3tJzHMdxsobtwiOHs/0hNpVhhog83tKypEMSXH81IN2TYRz4JwnhZ4RWU+x4jCRxOdaA61SHsYnJkoFDgTT5XCkif4kc/1NE/hs5/rGI3JkmfXSCb4PuSTJwhbW1zyGDa//fVqS5SiIu1xwniis9JxU/Ak5W1VrdidKyrplqEWQZSm3XX5mk2wE4RFX3UdU/J5w+A5v/1FhsDhZz+wLXAb/fynzep7ZBwlCgROJunw7BDKFaiqE08DlkSIOVHq3f5ZrThLjSc+ogIvdgVqMjReQnoZVwr5jD30dFpL+Yy7Ep4XenkO5hEfmHmEuieSJypJirpxnBiCPZtRaIyG1iLorGSdwEu46LohBeSxZqu/46NyHvQhF5SMz10USxqRRg1rg9E63DQivsNIJFrsStXmu5HAtxk7rgq4dOwBYnCSLys0j630TCvylxt2T/DIptIrCriBSJTWEoxSzz9g7JYq7qvhfynCzmni9l5S82deIREfltknMnirmkepfg5CGEHyjm6mti+N1N6rpgOzdZvJA+qcu1ZPcs5mKsKITV6XEI79p4MRd6vwlhSV2uOc4WNI27Ft+ydyPitgozLZ5A3D3Wy8CFYf9izAsCmJn/U9gk0tMxTw17Yx9XEwjul5JcJ+ZC6NuYdSOkd1EUleUiIq6/EvL+Kea9A2wqwCLM2GMAwU1VkjQPU9sN1xiSuxxL6oIvSX7VmHKaiU0lGBbCj8emEUgon1eweU97hPJtF+LdTdzt25gQ5wTMhP0SrEW+I7AoxOkWufZvCe7zqOvq7SDMg8kvk8hciE2kHhzkeybyXDphcwbBHBvE3MHVeg5p4tVxuVbPPW9M847G3H/lhnvaJ/Hd9c23xG276apytntGatzrzcHEv/7/hflujPGyqqqITAWWq+pUABGZjimbSUnyfjLyG+tuTOeiKCpLOg7DKllUdaaILMTmN65Pm6ouyVyOpXLBl+hKabMGrxtiE4YfFZG9QvrjsRYc2PzDwdjE9GHAx2LzpouAFSFOzCl5ETZ3aw7W/beS+FzOvULLrXPIM5Wn/n9ik/x/l+Tc7pgXoDlB7scwJR+7z0dCC02pPXk9Sqp4dVyuicgxae45HV8Xc5Keh/l5HILNI3OclLjSczIl5UR4artJirobSnRFlOp90yT76VwUpZMlyta66EokmcuxpC740qGqsRUveoT0v1fVf0bjiMiPsWkH1yXJ4n3g+1hL7O+YshsSfmPjeQ8DZ6jqZBG5CJu4m4z3Mb+jf9LgwzBR3BTpbgbeUtUzxTzij2lIPE3icg0ri1T3nBQxbyjXAAeo6prQfZ6pCzkni/ExPWdreJ/arsgS5401lHMjvzEXbKlcFCWSzr1W1AXVrphD4Vkp4maSX5SGuuBDzNVbLubQ+w3MnVZsOZg+ItITcz91dthHbGmc/iGL97FuyR6qukJVFVN4pxNv6XXEnJu3I/2cxgewVRyelbrGSTOBgZExzah/x+hzuSgSnlhuSeNJcpdr6e65MlbGCXTCPn7WiY33npRGFsfZgis9Z2u4AluBYgrmFujKbcyvIHz9XwnEphDcSHIXRYkkuv6KcjeQG7panwYuUvOAn46ngJ8FA4xU7tsgcxd8MUOMSUGGC1W1WlVHYctifRDkew5bEuVT4FfYWm5TsKVcegOo6hpMyU2P5P8B0BNzDQXwa8xD/5vEXcElRVXvwNbn+5dE1sYLLb9LgVeDIcvCSLI/YI7D36O279fE55AqXh2Xa+nuGRv3nJJoyKKqk7Gu4enYChxRy9VMXa45WYhPTndaFLHVGIarajrF5jiO0yh4S89xHMfJGryl5ziO42QN3tJzHMdxsgZXeo7jOE7WsF0qPREZECziEsMvEltUM3bcqI5lReR1EekT9s8XkV+Krc7734h7pfsl4pC4AXlfJCJ/ayxZmxuxFZgnNDBNrecjtihq5zTxtzhF3koZJ4i5xIqG1VvuYg6VG2uhzVienUXkR0nCXw/TE8aIyKzwXs0Ik6y35jr7BkvI2PH5IlIamUqxd7CITJV+izPprS3/VP/XJHG+0dC8M7h22jpAbGXtV8Vcqk0Xc20WO1cgIk+LyFwxl3cDIucuFJE5YbswEn6MmOPwSSLyrgS3eUmue3nIV8XmZsbCu4jIi2Iu2MaJOSqIpvuniBwqIucEeWsk4hxcRI4L7/nU8Ht0Bve5k5hrwInhuidHzqW6z4fFFg6fFLahCXIeIOZM/exwvFsk7iQRWS8iV6V6Li1KS7uESbaRwk0UNsF1eOR4AY3kbgjzAjEucvwI5iXiIODtRsj/IlK4y8owfW4LP5OjgLsaIm9Dnw8RV1lb+c6M3Jpy35br1iPPtISwLe9Y9F0GumI+OfO34jo5IW3HcHwXNg3hwHD8feAfadKPIO5ibKvKIdX/NdV1Grmc075jmOPpo8J+PjAWOCkc/wi4J+yfBzwdeR7zwm+XsN8lnJsN7BFJ/3CK6+4XyqWWfMDtwA1hf3dgdEK6SeG/swewG3XrvP2AHcP+XsDnGdznvcAPw/4QYEEG9/kwEXd8CTLmYiun/ydZnHB+GdC/sZ93Y2zbZUsvkCsi94WvllEi8i1gOPB4+JK4kgTHsiKyUUT+FL7ERotIjxB+hYh8Gr5ynkpxvREErxEiIpjX+CXAY8DQcM1BElmWRUQuEXNCPCbI+rcQfo6ITBNz+vtO5Bo7hi/9OSKyxXWXJHGcG8IXiMj1YnOlzhGR40Xkg3B/z0qY2BxFRHqFL8nJYTskhF8dZJoW+wIT+/qeKdZ6nSYij4vIsSLyXpDxwEjWJwKvhXQvha/M6RJpoYTyv0lszt0vkzyfBbGvXhH5dngek0XkX0nuY1AoqwkiMlZsYne6sj0JeD3E+U54Lm8Dh0byrOPEWuzr/gfAT8IzPjxZvJD+yMiX7EQR6RjCkzmOvhUYFOLeHsJGkNyDSQdsonV1yC/pcxaRYSLydiiTN0Skt6rWAB8DXwl5DcO8tcRarjFH1EkdQKdCzHH1ayJSlOL8sPAMPgAui4QPCM/rE6m9lNKtwOGhPH6SKp6I9BaRd0K8aRJ38F2nTCQD59KqWqqqb4X9CuyDoG84fTr2cQs2T/IYERHMt+mbqrpabW7km9j7D+applPYLwG+SHHdiaq6IMmpIdhkfFR1JjAg8n7tAcxWm8c5Q1XrOFII+cauOR0oFJGCeu4zlczp7jMdPwaeJ7WruGOAz1R1YYrzLUtLa90UXxIDgCqCg2LM4e03qaelhz3cmDPb6wlf+NhDLgj7ncPvcOD+SNo7gaPD/v7YpFlI+EKNyYD92RZgX0ntsC+r2PWmAn0SrncR9iVVgrlLWgj0i31xRb6QxlDbce7Pw353zMNI+3B8LXB9krJ7Grgqkl8JVhFOBdpjFex04l+iVdR2Cv0gcYfRL0XyHQcUJ8hbBEwjODkO5f/1NM9nQbiPPTHPKN0T8ruRuFPk0cDgsP8V4H+pyjbs/xtbGaI35li6B/bF+17kuaRzYn1NJK9U8V4GDg37HbDJ6KkcRw+gbksv+o6NCWUwBdgMfD/dc8besfcxbyxgk7wfjMh/fXi+7wKDML+aYP45dya1A+gRJLT0gMsxbykFaf6jU4Ajw/7tsXvFWhyFYX8wMD7F/yhVvJ8Sd0Cei3lWSfnu04DeBMwf6Txg53A8DegbOf9ZuNY1BGfiIfzXxN/LwzGPOksw5wSd6rlmLfmAWzD3egAHYv+/mBPyq4GLE9KPIVLnJZw7m+AAvZ777I39b5ZgvQKx66W7z4eJv59/Jl5/9gHeDs/mYZK39B4ELs/kmbTEtj373pyvqpPCftTRbzpqsEofrIUWcxQ8BWshvgS8BKCq47EKLcah2EsAkVZNGg7Euj1XA4jIs5gzY7CK9mEReSYiA1hXxroQ/1OgP+bNPp3j3Nj9HBTC37OPUfKJu+yKcjS2WgGqWo25aToMeFFVN4Vrv4D9eUdi5Rx1Cj1adYvD6AEhfEdgtaqWhmtcISJnhv1+WKX1JdZSeb6ecovJ+JyGCemxMowRWjaHYB5ZYsEF4bdO2YqN4/VV1XkicgYwRlVXhnNPE38u6ZxYR0kV7z3gDjHvIC+o6hIRSeU4elGSfKPvGNgH2nixHon3ReR17AMk2XPeDevOejOE5wJLI3L9FPvw+lhVPxORXUK+HUK59CMzR9HfwirHM1S1MlkEsaWNOqvq2yHoX8TdgLUD/iY2BlRNvOwTSRXvY+BBsTHJl1R1kogcmaJMMkbM1dqTwJ2qOi8WnCSqpgkH8xh0sqp+JCI/A+6gdj1SH7cCfxUbh52KvTdV4dwJwHcyyURE9gRuw969aHiy+zwf64b9k5jT83+JjSWmu8/rsC7KfOyj7lps+ai/ANeqanXkvxm9fj62PFfGflSbm+1Z6UXdRVVjrYqGEnuAp2Bf36cBvxaRPVU19qIh5g9wsVq3ANiL9LV68k7pzFhVfyAiXwnXjQ4CJ95TntTvODfmXFmwroioH8RMSed4OdEpdNRhdOz9OIngrV/M+fOxwMGqWioiYyLylgVFm4k8muZ8DrBWw+oEUVKU7VBq+/9MlXc6J9b1xlPVW0XkVWyJoQ9F5FhSO44ekHCc+I5F72mliHyCtWg3k+Q5i8jewHRVPTiJvB8CB2CrSsSUwRJsnCrmkzNTR9HTsPLsS+qPgnTP7yfAcmBf7Dkmc2adMp6qviMiR2DP91+ha3gNW//ux7gXmKOqf4mELcE+2pYEZVECrA7hIyLx+gJjwkfEvqr6UQh/mniX+htAL6zFmlIJqup6gmILXanzgflixjidNd51mRKxFSpexJZf+iyD+7yE0G2p5vS8EGvRJr3PEC/2QVUuIg8R/1gbDjwVFF534GQRqVLVl8L5k4BPVHV5fffRUmzPY3rJSHQkm3icgzX5Ab4BvCvmU7CfWn/3z4kvuRIlOh5UgnUDfVmPLOOAI8WssfKIKEkRGaSqH6nq9ZjfyH5p8knnODfKh8ChEl9ktVjMiXIio4Efhji5ItIJ6xo6I6RpD5yJtQoyJdryLQHWBIW3O9YCTUUqx7+jsdZttyBn1+jJUDHMF5FzwnkRkX3DfrKyjcr3ETBCRLqF1sI5kaxLSO7EOp2z5C3xwrWnqupt2Pp5u5PacXRinlvesURChbcf1r2W6jnPAnqEL3XErGn3DOW1AesxuIi40vsAW0E8pvSi93RRMjkCEzHjl5ESsZSOoqprifcgQG3H1iXAUrWxxm8R97uZrIzrxBNzNL1CVe/DnGLvn6ZMkuVbB7Gllkqw8ogykvjzPRvrQlfsmR4f/ttdsI/gNzDlWxK59nHAjFAmJ6jq0HQKL8jSWeIWxt8F3gnv+1GY/9K0iFk/vwpcp6rvJZxLdZ+LsHG22LhhIebDNdV9Eno5Yor5DOxjCFUdqKoDVHUANg76o4jCA2tVPsl2TGtTeg8D94gNchdR17HsJmBPMdP6o7HmeC7wmFh33UTgz6q6VkSGi8j9Id2JxCuk44D/1ieIqn6O9c9/FOJ/ii0SCrby9lQxM+53iDsDTpZPOse50XgrscrqSTET9A+xShcx45HTQtQrgaPC/U4A9lTVT7CyGxfkvV9VJ5IBYqt2D1YbdAcrp7wgw81BjlQkdfyrqtOB3wFvi8hkrIsokQuAS8L56dgYIyQv2xHYOEPsC/VGrNL/LzagH+NGkjuxfhk4U+IrqaeKd5UEIxqsRfaapnYc/SXWHTcttFai71iMx8W6uSZg3U8TUj3n0EI8G7gtXH8ScWMVsPemQFUXh+MPsLG8mNJL5QC6Dqr6LvZl/6pEzO0T+A7wdzFDlujahncDF4rIh1iXZaynYgpQJWb88pM08UZgLfiJ2IfkX9O9+9TjXDq0in6JdY/GphrEFNMDQDcRmYuNp/0i3P9q7N3+OGw3qRl7VAHfA54Pz+BbwM9SXPcKEVmCtZ6mROqaPYDpIjIT+xC6MoTX+igSkTND+oOx5xBbwupyYBesxypmVNWznvv8KfC9IPOTmON1TXWfIc3j4X2eirXofpvsPhPuuRirP1+oL25L0qbckInIRlWtY9FYT5oC4D1VjVlk3o8phXSVeSxtB1XdKNbSexEzLHhxa2TfXglf899U1R+0tCzJCH/2+1Q1VQu5xUl8xxwnEQnd26nGUZ3GI+uV3jZe74/Y+FYhMAq4UttSgTqO47Qx2pTScxyncRGRvxOZ6xj4q6o+1BLypENsfmhBQvC3YtbJjgOu9BzHcZwsorUZsjiO4zjOVuNKz3Ecx8kaXOk5juM4WYMrPcdxHCdr+H+dlc1OvSnuDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "\n",
    "columns = ['has_queens', 'has_workers', 'has_males']\n",
    "colours = ['red', 'pink', 'blue']\n",
    "            \n",
    "week_sums.plot(kind='scatter', \\\n",
    "               x=['Week'] * len(columns), \\\n",
    "               y=columns, \\\n",
    "               c=colours * week_sums.shape[0], \\\n",
    "               ax=ax, \\\n",
    "               title='Bumblebee sightings by caste', \\\n",
    "               xlabel=metadata.loc['Week', 'Explanation'], \\\n",
    "               ylabel='Number of sightings', \\\n",
    "               xticks=[0, 13, 26, 39, 52], \\\n",
    "               xlim=[0, 52], \\\n",
    "               ylim=[-1000, 9000])\n",
    "\n",
    "for idx in range(0,len(columns)):\n",
    "    # shorten 'has_xyz' to 'xyz'\n",
    "    series_label = columns[idx][4:]\n",
    "\n",
    "    # place the series label near the series' data values\n",
    "    y_values = week_sums[columns[idx]]\n",
    "    \n",
    "    # place the series label vertically slightly above the series maximum\n",
    "    text_y = y_values.max() + 500\n",
    "    \n",
    "    # place the series label horizontally near the middle of the maximums\n",
    "    text_x = week_sums[ y_values == y_values.max() ]['Week'].median() - len(series_label)/2\n",
    "\n",
    "    # show the label\n",
    "    ax.text(x=text_x, y=text_y, s=series_label, color=colours[idx])\n",
    "\n",
    "notebook = \"BeeWalkPandas.ipynb\"\n",
    "repo_url = \"https://github.com/cpoerschke/bee-informatics\"\n",
    "\n",
    "attribution = f'Generated by {notebook} at {repo_url}\\nfrom part of the BeeWalk dataset at\\n{dataset_url}'\n",
    "_ = ax.text(x=26, y=-5000, s=attribution, horizontalalignment='center', verticalalignment='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d7d295",
   "metadata": {},
   "source": [
    "## Further reading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d5986a",
   "metadata": {},
   "source": [
    "So, how come there's the seasonal differences between female (queen) and male bumblebee sightings?\n",
    "\n",
    "Well, I'll leave that question deliberately unanswered here and instead would like to encourage you to read more about bumblebees e.g. at https://www.bumblebeeconservation.org/about-bumblebees/ and/or on [Wikipedia](https://en.wikipedia.org/wiki/Bumblebee)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d0d6cf2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}