stijnvanhoey/species-observations-per-year.ipynb

## species-observations-per-year.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Species observations for a country per year (using GBIF occurrence facets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import pygbif\n",
    "import requests\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define a list of species we want counts for. I'm using invasive species of EU concern."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_species_list = ['this name should be ignored', 'Acer negundo','Alopochen aegyptiaca', 'Alternanthera philoxeroides', 'Ameiurus melas', 'Asclepias syriaca', 'Bison bison', 'Elodea nuttallii', 'Gunnera manicata', 'Gunnera tinctoria', 'Heracleum mantegazzianum', 'Homarus americanus', 'Impatiens glandulifera', 'Lepomis gibbosus', 'Lupinus polyphyllus', 'Microstegium vimineum', 'Myriophyllum heterophyllum', 'Nyctereutes procyonoides', 'Ondatra zibethicus', 'Pennisetum setaceum']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we look up their GBIF `speciesKey`s. Note: these is the key for the accepted species name, so if your list contains infraspecific names or synonyms, you'll get the **accepted species** back. If you don't want this, use the `usageKey`, which is id for that specific name (not it's accepted species)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_species_key(name):\n",
    "    gbif_match = pygbif.species.name_backbone(name)\n",
    "    try:\n",
    "        return gbif_match['speciesKey'] # Found a valid match\n",
    "    except:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "my_species_dict = {}\n",
    "for species_name in my_species_list:\n",
    "    species_key = get_species_key(species_name)\n",
    "    if species_key:\n",
    "        my_species_dict[species_key] = species_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2340977: 'Ameiurus melas',\n",
       " 2394486: 'Lepomis gibbosus',\n",
       " 2434552: 'Nyctereutes procyonoides',\n",
       " 2441176: 'Bison bison',\n",
       " 2498252: 'Alopochen aegyptiaca',\n",
       " 2706134: 'Pennisetum setaceum',\n",
       " 2891770: 'Impatiens glandulifera',\n",
       " 2964355: 'Lupinus polyphyllus',\n",
       " 2984306: 'Gunnera tinctoria',\n",
       " 3034825: 'Heracleum mantegazzianum',\n",
       " 3084923: 'Alternanthera philoxeroides',\n",
       " 3170247: 'Asclepias syriaca',\n",
       " 3189866: 'Acer negundo',\n",
       " 5219858: 'Ondatra zibethicus',\n",
       " 5289808: 'Microstegium vimineum',\n",
       " 5329212: 'Elodea nuttallii',\n",
       " 5361762: 'Myriophyllum heterophyllum',\n",
       " 5972004: 'Homarus americanus',\n",
       " 8078925: 'Gunnera manicata'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_species_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we'll use GBIF occurrence facets to get `HumanObservation` counts per year for these species, for a certain country. Here I use `BE` for Belgium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_count_per_year_dict(country_code, species_key):\n",
    "    request = 'http://api.gbif.org/v1/occurrence/search?country=' + str(country_code) + '&taxon_key=' + str(species_key) + '&basis_of_record=HUMAN_OBSERVATION&facet=YEAR&YEAR.facetLimit=300&limit=0'\n",
    "    response = json.loads(requests.get(request).text)\n",
    "    return response['facets'][0]['counts'] # Counts per year (or None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def build_year_count_df(country_code, species_dict):\n",
    "    year_counts = {}\n",
    "    \n",
    "    for key, value in species_dict.items():\n",
    "        # Get counts per year\n",
    "        temp = pd.DataFrame(get_count_per_year_dict(country_code, species_key))\n",
    "        if len(temp) > 0:\n",
    "            year_counts[value] = temp.set_index('name')\n",
    "            \n",
    "    df = pd.concat(year_counts, axis=1).transpose().fillna(0.0).astype(int)\n",
    "    df.index = df.index.droplevel(1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "species_counts_df = build_year_count_df('BE', my_species_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1940</th>\n",
       "      <th>1942</th>\n",
       "      <th>1943</th>\n",
       "      <th>1944</th>\n",
       "      <th>1945</th>\n",
       "      <th>1950</th>\n",
       "      <th>1951</th>\n",
       "      <th>1952</th>\n",
       "      <th>1954</th>\n",
       "      <th>1955</th>\n",
       "      <th>...</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Acer negundo</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alopochen aegyptiaca</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>857</td>\n",
       "      <td>951</td>\n",
       "      <td>1062</td>\n",
       "      <td>1226</td>\n",
       "      <td>1227</td>\n",
       "      <td>1673</td>\n",
       "      <td>2011</td>\n",
       "      <td>1370</td>\n",
       "      <td>833</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asclepias syriaca</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elodea nuttallii</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heracleum mantegazzianum</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>36</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>65</td>\n",
       "      <td>282</td>\n",
       "      <td>71</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Impatiens glandulifera</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>50</td>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>108</td>\n",
       "      <td>160</td>\n",
       "      <td>101</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lepomis gibbosus</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>386</td>\n",
       "      <td>749</td>\n",
       "      <td>383</td>\n",
       "      <td>254</td>\n",
       "      <td>278</td>\n",
       "      <td>231</td>\n",
       "      <td>264</td>\n",
       "      <td>128</td>\n",
       "      <td>269</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lupinus polyphyllus</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Myriophyllum heterophyllum</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            1940  1942  1943  1944  1945  1950  1951  1952  \\\n",
       "Acer negundo                   0     0     0     0     0     0     0     0   \n",
       "Alopochen aegyptiaca           0     0     0     0     0     0     0     0   \n",
       "Asclepias syriaca              0     0     0     0     0     0     0     0   \n",
       "Elodea nuttallii              26     0     0     0     0     0     1     0   \n",
       "Heracleum mantegazzianum       0     0     0     0     1     1     0     0   \n",
       "Impatiens glandulifera        45     1     1     1     0     0     1     1   \n",
       "Lepomis gibbosus               0     0     0     0     0     0     0     0   \n",
       "Lupinus polyphyllus           26     0     0     0     0     0     0     0   \n",
       "Myriophyllum heterophyllum     0     0     0     0     0     0     0     0   \n",
       "\n",
       "                            1954  1955  ...   2006  2007  2008  2009  2010  \\\n",
       "Acer negundo                   0     0  ...      8     6    11     4    11   \n",
       "Alopochen aegyptiaca           0     0  ...    857   951  1062  1226  1227   \n",
       "Asclepias syriaca              0     0  ...      0     1     0     0     0   \n",
       "Elodea nuttallii               0     1  ...      3     7    11     9     5   \n",
       "Heracleum mantegazzianum       0     0  ...     35    35    45    36    45   \n",
       "Impatiens glandulifera         0     2  ...     51    50    43    50    85   \n",
       "Lepomis gibbosus               0     0  ...    386   749   383   254   278   \n",
       "Lupinus polyphyllus            1     0  ...      2     5     6     3     6   \n",
       "Myriophyllum heterophyllum     0     0  ...      4    18     0     0     1   \n",
       "\n",
       "                            2011  2012  2013  2014  2015  \n",
       "Acer negundo                   7     7    17    16     2  \n",
       "Alopochen aegyptiaca        1673  2011  1370   833   500  \n",
       "Asclepias syriaca              0     1     0     0     0  \n",
       "Elodea nuttallii              10    10     8    11     6  \n",
       "Heracleum mantegazzianum      48    65   282    71    37  \n",
       "Impatiens glandulifera        86   108   160   101    30  \n",
       "Lepomis gibbosus             231   264   128   269     7  \n",
       "Lupinus polyphyllus            6     2     7     4     2  \n",
       "Myriophyllum heterophyllum     0     1     0     0     0  \n",
       "\n",
       "[9 rows x 67 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_counts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's now plot this data, using [@stijnvanhoey's example](https://github.com/stijnvanhoey/open_data_showcases/blob/master/gent_cijfers/wijkmigratie_in_beeld.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "species_counts_subset_df = species_counts_df.ix[:,'2000':] # ix[row_range, column_range]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PHBwdHTl69Cju7u4sX76cdu3aGS1+TEwMrq6u7N27F4C9e/cSHx//3uOn1nW1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5CGTCe/g2+AnANNQkJmJyFzqdWZnQIU5maZm4CEzM8DDOV2UN/JL0AIIYQQQug3PR8hkGcIhBBC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\nzp07KykpScnJyQoLC1O/fv00ZcoU2zrr1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DYAACdBhwAAAADmZvL7E8whAAAAAEyMCgEA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\n8NHAIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff649bd26a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "sns.heatmap(species_counts_subset_df, ax=ax, annot=True, fmt='d',\n",
    "            cmap='RdPu')\n",
    "\n",
    "# Have x axis and label on top\n",
    "ax.xaxis.tick_top()\n",
    "ax.xaxis.set_label_position('top')\n",
    "\n",
    "# Set labels (I should try to figure out how to transform the df to get this automatically)\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Species')\n",
    "\n",
    "# Add a title\n",
    "fig.suptitle('GBIF-registered invasive species observations per year for Belgium')\n",
    "\n",
    "# Add some padding on the left\n",
    "plt.gcf().subplots_adjust(left=0.3)\n",
    "\n",
    "plt.savefig('species-observations-per-year.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there you have it!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}

## species-observations-per-year.png

      
    Raw
  

              species-observations-per-year.png