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<div class="section" id="introduction-to-geopandas">
<h1>Introduction to Geopandas<a class="headerlink" href="#introduction-to-geopandas" title="Permalink to this headline">¶</a></h1>
<div class="section" id="reading-a-shapefile">
<h2>Reading a Shapefile<a class="headerlink" href="#reading-a-shapefile" title="Permalink to this headline">¶</a></h2>
<p>Spatial data can be read easily with geopandas using <code class="docutils literal"><span class="pre">gpd.from_file()</span></code>
-function:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Import necessary modules</span>
<span class="gp">In [1]: </span><span class="kn">import</span> <span class="nn">geopandas</span> <span class="kn">as</span> <span class="nn">gpd</span>
<span class="go"># Set filepath (fix path relative to yours)</span>
<span class="gp">In [2]: </span><span class="n">fp</span> <span class="o">=</span> <span class="s2">"/home/geo/Data/DAMSELFISH_distributions.shp"</span>
<span class="go"># Read file using gpd.read_file()</span>
<span class="gp">In [3]: </span><span class="n">data</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">read_file</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s see what datatype is our ‘data’ variable</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [4]: </span><span class="nb">type</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="gh">Out[4]: </span><span class="go">geopandas.geodataframe.GeoDataFrame</span>
</pre></div>
</div>
<p>Okey so from the above we can see that our <code class="docutils literal"><span class="pre">data</span></code> -variable is a
<strong>GeoDataFrame</strong>. GeoDataFrame extends the functionalities of
<strong>pandas.DataFrame</strong> in a way that it is possible to use and handle
spatial data within pandas (hence the name geopandas). GeoDataFrame have
some special features and functions that are useful in GIS.</p>
<ul class="simple">
<li>Let’s take a look at our data and print the first 5 rows using the
<code class="docutils literal"><span class="pre">head()</span></code> -function prints the first 5 rows by default</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [5]: </span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="gh">Out[5]: </span><span class="go"></span>
<span class="go"> id_no binomial origin compiler year \</span>
<span class="go">0 183963.0 Stegastes leucorus 1 IUCN 2010 </span>
<span class="go">1 183963.0 Stegastes leucorus 1 IUCN 2010 </span>
<span class="go">2 183963.0 Stegastes leucorus 1 IUCN 2010 </span>
<span class="go">3 183793.0 Chromis intercrusma 1 IUCN 2010 </span>
<span class="go">4 183793.0 Chromis intercrusma 1 IUCN 2010 </span>
<span class="go"> citation source dist_comm island \</span>
<span class="go">0 International Union for Conservation of Nature... None None None </span>
<span class="go">1 International Union for Conservation of Nature... None None None </span>
<span class="go">2 International Union for Conservation of Nature... None None None </span>
<span class="go">3 International Union for Conservation of Nature... None None None </span>
<span class="go">4 International Union for Conservation of Nature... None None None </span>
<span class="go"> subspecies ... kingdom_na \</span>
<span class="go">0 None ... ANIMALIA </span>
<span class="go">1 None ... ANIMALIA </span>
<span class="go">2 None ... ANIMALIA </span>
<span class="go">3 None ... ANIMALIA </span>
<span class="go">4 None ... ANIMALIA </span>
<span class="go"> phylum_nam class_name order_name family_nam genus_name \</span>
<span class="go">0 CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE Stegastes </span>
<span class="go">1 CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE Stegastes </span>
<span class="go">2 CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE Stegastes </span>
<span class="go">3 CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE Chromis </span>
<span class="go">4 CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE Chromis </span>
<span class="go"> species_na category testCol \</span>
<span class="go">0 leucorus VU 1 </span>
<span class="go">1 leucorus VU 1 </span>
<span class="go">2 leucorus VU 1 </span>
<span class="go">3 intercrusma LC 1 </span>
<span class="go">4 intercrusma LC 1 </span>
<span class="go"> geometry </span>
<span class="go">0 POLYGON ((-115.6437454219999 29.71392059300007... </span>
<span class="go">1 POLYGON ((-105.589950704 21.89339825500002, -1... </span>
<span class="go">2 POLYGON ((-111.159618439 19.01535626700007, -1... </span>
<span class="go">3 POLYGON ((-80.86500229899997 -0.77894492099994... </span>
<span class="go">4 POLYGON ((-67.33922225599997 -55.6761029239999... </span>
<span class="go">[5 rows x 25 columns]</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s also take a look how our data looks like on a map. If you just
want to explore your data on a map, you can use <code class="docutils literal"><span class="pre">.plot()</span></code> -function
in geopandas that creates a simple map out of the data (uses
matplotlib as a backend):</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [6]: </span><span class="n">data</span><span class="o">.</span><span class="n">plot</span><span class="p">();</span>
</pre></div>
</div>
<a class="reference internal image-reference" href="_images/damselfish.png"><img alt="_images/damselfish.png" src="_images/damselfish.png" style="width: 5in;" /></a>
</div>
<div class="section" id="coordinate-reference-system-crs">
<h2>Coordinate reference system (CRS)<a class="headerlink" href="#coordinate-reference-system-crs" title="Permalink to this headline">¶</a></h2>
<p>GeoDataFrame that is read from a Shapefile contains <em>always</em> (well not
always but should) information about the coordinate system in which the
data is projected.</p>
<ul class="simple">
<li>We can see the current coordinate reference system from <code class="docutils literal"><span class="pre">.crs</span></code>
attribute:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [7]: </span><span class="n">data</span><span class="o">.</span><span class="n">crs</span>
<span class="gh">Out[7]: </span><span class="go">{'init': 'epsg:4326'}</span>
</pre></div>
</div>
<p>Okey, so from this we can see that the data is something called
<strong>epsg:4326</strong>. The EPSG number (<em>“European Petroleum Survey Group”</em>) is
a code that tells about the coordinate system of the dataset. “<a class="reference external" href="http://www.epsg.org/">EPSG
Geodetic Parameter Dataset</a> is a collection of
definitions of coordinate reference systems and coordinate
transformations which may be global, regional, national or local in
application”. EPSG-number 4326 that we have here belongs to the WGS84
coordinate system (i.e. coordinates are in decimal degrees (lat, lon)).
You can check easily different epsg-codes from <a class="reference external" href="http://spatialreference.org/ref/epsg/">this
website</a>.</p>
</div>
<div class="section" id="writing-a-shapefile">
<h2>Writing a Shapefile<a class="headerlink" href="#writing-a-shapefile" title="Permalink to this headline">¶</a></h2>
<p>Writing a new Shapefile is also something that is needed frequently.</p>
<ul class="simple">
<li>Let’s select 50 first rows of the input data and write those into a
new Shapefile by first selecting the data using index slicing and
then write the selection into a Shapefile with <code class="docutils literal"><span class="pre">gpd.to_file()</span></code>
-function:</li>
</ul>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Create a output path for the data</span>
<span class="n">out</span> <span class="o">=</span> <span class="s2">r"/home/geo/Data/DAMSELFISH_distributions_SELECTION.shp"</span>
<span class="c1"># Select first 50 rows</span>
<span class="n">selection</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">50</span><span class="p">]</span>
<span class="c1"># Write those rows into a new Shapefile (the default output file format is Shapefile)</span>
<span class="n">selection</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
</pre></div>
</div>
<p><strong>Task:</strong> Open the Shapefile now in QGIS that has been installed into
our computer instance, and see how the data looks like.</p>
</div>
<div class="section" id="geometries-in-geopandas">
<h2>Geometries in Geopandas<a class="headerlink" href="#geometries-in-geopandas" title="Permalink to this headline">¶</a></h2>
<p>Geopandas takes advantage of Shapely’s geometric objects. Geometries are
stored in a column called <em>geometry</em> that is a default column name for
storing geometric information in geopandas.</p>
<ul class="simple">
<li>Let’s print the first 5 rows of the column ‘geometry’:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># It is possible to use only specific columns by specifying the column name within square brackets []</span>
<span class="gp">In [8]: </span><span class="n">data</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
<span class="gh">Out[8]: </span><span class="go"></span>
<span class="go">0 POLYGON ((-115.6437454219999 29.71392059300007...</span>
<span class="go">1 POLYGON ((-105.589950704 21.89339825500002, -1...</span>
<span class="go">2 POLYGON ((-111.159618439 19.01535626700007, -1...</span>
<span class="go">3 POLYGON ((-80.86500229899997 -0.77894492099994...</span>
<span class="go">4 POLYGON ((-67.33922225599997 -55.6761029239999...</span>
<span class="go">Name: geometry, dtype: object</span>
</pre></div>
</div>
<p>Since spatial data is stored as Shapely objects, <strong>it is possible to use
all of the functionalities of Shapely module</strong> that we practiced
earlier.</p>
<ul class="simple">
<li>Let’s print the areas of the first 5 polygons:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Make a selection that contains only the first five rows</span>
<span class="gp">In [9]: </span><span class="n">selection</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]</span>
</pre></div>
</div>
<ul class="simple">
<li>We can iterate over the selected rows using a specific
<code class="docutils literal"><span class="pre">.iterrows()</span></code> -function in (geo)pandas:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [10]: </span><span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">selection</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="gp"> ....: </span> <span class="n">poly_area</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span><span class="o">.</span><span class="n">area</span>
<span class="gp"> ....: </span> <span class="k">print</span><span class="p">(</span><span class="s2">"Polygon area at index {0} is: {1:.3f}"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">,</span> <span class="n">poly_area</span><span class="p">))</span>
<span class="gp"> ....: </span>
<span class="go">Polygon area at index 0 is: 19.396</span>
<span class="go">Polygon area at index 1 is: 6.146</span>
<span class="go">Polygon area at index 2 is: 2.697</span>
<span class="go">Polygon area at index 3 is: 87.461</span>
<span class="go">Polygon area at index 4 is: 0.001</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s create a new column into our GeoDataFrame where we calculate
and store the areas individual polygons:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Empty column for area</span>
<span class="gp">In [11]: </span><span class="n">data</span><span class="p">[</span><span class="s1">'area'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">None</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s iterate over the rows and calculate the areas</li>
</ul>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Iterate rows one at the time</span>
<span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
<span class="c1"># Update the value in 'area' column with area information at index</span>
<span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="s1">'area'</span><span class="p">]</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span><span class="o">.</span><span class="n">area</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s see the first 2 rows of our ‘area’ column</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [12]: </span><span class="n">data</span><span class="p">[</span><span class="s1">'area'</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="gh">Out[12]: </span><span class="go"></span>
<span class="go">0 19.3963</span>
<span class="go">1 6.1459</span>
<span class="go">Name: area, dtype: object</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s check what is the min and the max of those areas using
familiar functions from our previous numpy lessions</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Maximum area</span>
<span class="gp">In [13]: </span><span class="n">max_area</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'area'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
<span class="go"># Minimum area</span>
<span class="gp">In [14]: </span><span class="n">min_area</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">'area'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="gp">In [15]: </span><span class="k">print</span><span class="p">(</span><span class="s2">"Max area: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">Mean area: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">max_area</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="nb">round</span><span class="p">(</span><span class="n">min_area</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span>
<span class="go">Max area: 1493.2</span>
<span class="go">Mean area: 19.96</span>
</pre></div>
</div>
</div>
<div class="section" id="creating-geometries-into-a-geodataframe">
<h2>Creating geometries into a GeoDataFrame<a class="headerlink" href="#creating-geometries-into-a-geodataframe" title="Permalink to this headline">¶</a></h2>
<p>Since geopandas takes advantage of Shapely geometric objects it is
possible to create a Shapefile from a scratch by passing Shapely’s
geometric objects into the GeoDataFrame. This is useful as it makes it
easy to convert e.g. a text file that contains coordinates into a
Shapefile.</p>
<ul class="simple">
<li>Let’s create an empty <code class="docutils literal"><span class="pre">GeoDataFrame</span></code>.</li>
</ul>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Import necessary modules first</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">geopandas</span> <span class="k">as</span> <span class="nn">gpd</span>
<span class="kn">from</span> <span class="nn">shapely.geometry</span> <span class="k">import</span> <span class="n">Point</span><span class="p">,</span> <span class="n">Polygon</span>
<span class="kn">import</span> <span class="nn">fiona</span>
<span class="c1"># Create an empty geopandas GeoDataFrame</span>
<span class="n">newdata</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">GeoDataFrame</span><span class="p">()</span>
</pre></div>
</div>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Let's see what's inside</span>
<span class="gp">In [16]: </span><span class="n">newdata</span>
<span class="gh">Out[16]: </span><span class="go"></span>
<span class="go">Empty GeoDataFrame</span>
<span class="go">Columns: []</span>
<span class="go">Index: []</span>
</pre></div>
</div>
<p>The GeoDataFrame is empty since we haven’t placed any data inside.</p>
<ul class="simple">
<li>Let’s create a new column called <code class="docutils literal"><span class="pre">geometry</span></code> that will contain our
Shapely objects:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Create a new column called 'geometry' to the GeoDataFrame</span>
<span class="gp">In [17]: </span><span class="n">newdata</span><span class="p">[</span><span class="s1">'geometry'</span><span class="p">]</span> <span class="o">=</span> <span class="bp">None</span>
<span class="go"># Let's see what's inside</span>
<span class="gp">In [18]: </span><span class="n">newdata</span>
<span class="gh">Out[18]: </span><span class="go"></span>
<span class="go">Empty GeoDataFrame</span>
<span class="go">Columns: [geometry]</span>
<span class="go">Index: []</span>
</pre></div>
</div>
<p>Now we have a geometry column in our GeoDataFrame but we don’t have any
data yet.</p>
<ul class="simple">
<li>Let’s create a Shapely Polygon repsenting the Helsinki Senate square
that we can insert to our GeoDataFrame:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Coordinates of the Helsinki Senate square in Decimal Degrees</span>
<span class="gp">In [19]: </span><span class="n">coordinates</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">24.950899</span><span class="p">,</span> <span class="mf">60.169158</span><span class="p">),</span> <span class="p">(</span><span class="mf">24.953492</span><span class="p">,</span> <span class="mf">60.169158</span><span class="p">),</span> <span class="p">(</span><span class="mf">24.953510</span><span class="p">,</span> <span class="mf">60.170104</span><span class="p">),</span> <span class="p">(</span><span class="mf">24.950958</span><span class="p">,</span> <span class="mf">60.169990</span><span class="p">)]</span>
<span class="go"># Create a Shapely polygon from the coordinate-tuple list</span>
<span class="gp">In [20]: </span><span class="n">poly</span> <span class="o">=</span> <span class="n">Polygon</span><span class="p">(</span><span class="n">coordinates</span><span class="p">)</span>
<span class="go"># Let's see what we have</span>
<span class="gp">In [21]: </span><span class="n">poly</span>
<span class="gh">Out[21]: </span><span class="go"><shapely.geometry.polygon.Polygon at 0xf05a160></span>
</pre></div>
</div>
<p>Okey, so now we have appropriate Polygon -object.</p>
<ul class="simple">
<li>Let’s insert the polygon into our ‘geometry’ column in our
GeoDataFrame:</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Insert the polygon into 'geometry' -column at index 0</span>
<span class="gp">In [22]: </span><span class="n">newdata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'geometry'</span><span class="p">]</span> <span class="o">=</span> <span class="n">poly</span>
<span class="go"># Let's see what we have now</span>
<span class="gp">In [23]: </span><span class="n">newdata</span>
<span class="gh">Out[23]: </span><span class="go"></span>
<span class="go"> geometry</span>
<span class="go">0 POLYGON ((24.950899 60.169158, 24.953492 60.16...</span>
</pre></div>
</div>
<p>Now we have a GeoDataFrame with Polygon that we can export to a
Shapefile.</p>
<ul class="simple">
<li>Let’s add another column to our GeoDataFrame called <code class="docutils literal"><span class="pre">Location</span></code> with
text <em>Senaatintori</em>.</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Add a new column and insert data</span>
<span class="gp">In [24]: </span><span class="n">newdata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="s1">'Location'</span><span class="p">]</span> <span class="o">=</span> <span class="s1">'Senaatintori'</span>
<span class="go"># Let's check the data</span>
<span class="gp">In [25]: </span><span class="n">newdata</span>
<span class="gh">Out[25]: </span><span class="go"></span>
<span class="go"> geometry Location</span>
<span class="go">0 POLYGON ((24.950899 60.169158, 24.953492 60.16... Senaatintori</span>
</pre></div>
</div>
<p>Okey, now we have additional information that is useful to be able to
recognice what the feature represents.</p>
<p>Before exporting the data it is useful to <strong>determine the spatial
reference system for the GeoDataFrame.</strong></p>
<p>As was shown earlier, GeoDataFrame has a property called <em>.crs</em> that
shows the coordinate system of the data which is empty (None) in our
case since we are creating the data from the scratch:</p>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [26]: </span><span class="k">print</span><span class="p">(</span><span class="n">newdata</span><span class="o">.</span><span class="n">crs</span><span class="p">)</span>
<span class="go">None</span>
</pre></div>
</div>
<ul class="simple">
<li>Let’s add a crs for our GeoDataFrame. A Python module called
<strong>fiona</strong> has a nice function called <code class="docutils literal"><span class="pre">from_epsg()</span></code> for passing
coordinate system for the GeoDataFrame. Next we will use that and
determine the projection to WGS84 (epsg code: 4326):</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Import specific function 'from_epsg' from fiona module</span>
<span class="gp">In [27]: </span><span class="kn">from</span> <span class="nn">fiona.crs</span> <span class="kn">import</span> <span class="n">from_epsg</span>
<span class="go"># Set the GeoDataFrame's coordinate system to WGS84</span>
<span class="gp">In [28]: </span><span class="n">newdata</span><span class="o">.</span><span class="n">crs</span> <span class="o">=</span> <span class="n">from_epsg</span><span class="p">(</span><span class="mi">4326</span><span class="p">)</span>
<span class="go"># Let's see how the crs definition looks like</span>
<span class="gp">In [29]: </span><span class="n">newdata</span><span class="o">.</span><span class="n">crs</span>
<span class="gh">Out[29]: </span><span class="go">{'init': 'epsg:4326', 'no_defs': True}</span>
</pre></div>
</div>
<ul class="simple">
<li>Finally, we can export the data using GeoDataFrames <code class="docutils literal"><span class="pre">.to_file()</span></code>
-function. The function works similarly as numpy or pandas, but here
we only need to provide the output path for the Shapefile. Easy isn’t
it!:</li>
</ul>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Determine the output path for the Shapefile</span>
<span class="n">outfp</span> <span class="o">=</span> <span class="s2">r"/home/geo/Data/Senaatintori.shp"</span>
<span class="c1"># Write the data into that Shapefile</span>
<span class="n">newdata</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
</pre></div>
</div>
<p>Now we have successfully created a Shapefile from the scratch using only
Python programming. Similar approach can be used to for example to read
coordinates from a text file (e.g. points) and create Shapefiles from
those automatically.</p>
<p><strong>Task:</strong> check the output Shapefile in QGIS and make sure that the
attribute table seems correct.</p>
</div>
<div class="section" id="pro-tips-optional-but-recommended">
<h2>Pro -tips (optional but recommended)<a class="headerlink" href="#pro-tips-optional-but-recommended" title="Permalink to this headline">¶</a></h2>
<div class="section" id="grouping-data">
<h3>Grouping data<a class="headerlink" href="#grouping-data" title="Permalink to this headline">¶</a></h3>
<p>One really useful function that can be used in Pandas/Geopandas is <a class="reference external" href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html">.groupby()</a>.
This function groups data based on values on selected column(s).</p>
<ul class="simple">
<li>Let’s group individual fishes in <code class="docutils literal"><span class="pre">DAMSELFISH_distribution.shp</span></code> and export the species to individual Shapefiles.<ul>
<li><em>Note: If your `data` -variable doesn’t contain the Damselfish data anymore, read the Shapefile again into memory using `gpd.read_file()` -function</em></li>
</ul>
</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Group the data by column 'binomial'</span>
<span class="gp">In [30]: </span><span class="n">grouped</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'binomial'</span><span class="p">)</span>
<span class="go"># Let's see what we got</span>
<span class="gp">In [31]: </span><span class="n">grouped</span>
<span class="gh">Out[31]: </span><span class="go"><pandas.core.groupby.DataFrameGroupBy object at 0x000000000BCF4828></span>
</pre></div>
</div>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">groupby</span></code> -function gives us an object called <code class="docutils literal"><span class="pre">DataFrameGroupBy</span></code> which is similar to list of keys and values (in a dictionary) that we can iterate over.</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="go"># Iterate over the group object</span>
<span class="gp">In [32]: </span><span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">grouped</span><span class="p">:</span>
<span class="gp"> ....: </span> <span class="n">individual_fish</span> <span class="o">=</span> <span class="n">values</span>
<span class="gp"> ....: </span>
<span class="go"># Let's see what is the LAST item that we iterated</span>
<span class="gp">In [33]: </span><span class="n">individual_fish</span>
<span class="gh">Out[33]: </span><span class="go"></span>
<span class="go"> id_no binomial origin compiler year \</span>
<span class="go">27 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">28 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">29 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">30 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">31 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">32 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go">33 154915.0 Teixeirichthys jordani 1 None 2012 </span>
<span class="go"> citation source dist_comm island \</span>
<span class="go">27 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">28 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">29 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">30 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">31 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">32 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go">33 Red List Index (Sampled Approach), Zoological ... None None None </span>
<span class="go"> subspecies ... phylum_nam class_name order_name family_nam \</span>
<span class="go">27 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">28 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">29 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">30 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">31 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">32 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go">33 None ... CHORDATA ACTINOPTERYGII PERCIFORMES POMACENTRIDAE </span>
<span class="go"> genus_name species_na category testCol \</span>
<span class="go">27 Teixeirichthys jordani LC 1 </span>
<span class="go">28 Teixeirichthys jordani LC 1 </span>
<span class="go">29 Teixeirichthys jordani LC 1 </span>
<span class="go">30 Teixeirichthys jordani LC 1 </span>
<span class="go">31 Teixeirichthys jordani LC 1 </span>
<span class="go">32 Teixeirichthys jordani LC 1 </span>
<span class="go">33 Teixeirichthys jordani LC 1 </span>
<span class="go"> geometry area </span>
<span class="go">27 POLYGON ((121.6300326400001 33.04248618400004,... 38.6712 </span>
<span class="go">28 POLYGON ((32.56219482400007 29.97488975500005,... 37.4457 </span>
<span class="go">29 POLYGON ((130.9052090560001 34.02498196400006,... 16.9395 </span>
<span class="go">30 POLYGON ((56.32233070000007 -3.707270205999976... 10.127 </span>
<span class="go">31 POLYGON ((40.64476131800006 -10.85502363999996... 7.7603 </span>
<span class="go">32 POLYGON ((48.11258402900006 -9.335103113999935... 3.43424 </span>
<span class="go">33 POLYGON ((51.75403543100003 -9.21679305899994,... 2.40862 </span>
<span class="go">[7 rows x 26 columns]</span>
</pre></div>
</div>
<p>From here we can see that an individual_fish variable now contains all the rows that belongs to a fish called <code class="docutils literal"><span class="pre">Teixeirichthys</span> <span class="pre">jordani</span></code>. Notice that the index numbers refer to the row numbers in the
original data -GeoDataFrame.</p>
<ul class="simple">
<li>Let’s check the datatype of the grouped object and what does the <code class="docutils literal"><span class="pre">key</span></code> variable contain</li>
</ul>
<div class="highlight-ipython"><div class="highlight"><pre><span></span><span class="gp">In [34]: </span><span class="nb">type</span><span class="p">(</span><span class="n">individual_fish</span><span class="p">)</span>
<span class="gh">Out[34]: </span><span class="go">geopandas.geodataframe.GeoDataFrame</span>
<span class="gp">In [35]: </span><span class="k">print</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
<span class="go"> Teixeirichthys jordani</span>
</pre></div>
</div>
<p>As can be seen from the example above, each set of data are now grouped into separate GeoDataFrames that we can export into Shapefiles using the variable <code class="docutils literal"><span class="pre">key</span></code>
for creating the output filepath names. Let’s now export those species into individual Shapefiles.</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="c1"># Determine outputpath</span>
<span class="n">outFolder</span> <span class="o">=</span> <span class="s2">r"/home/geo/Data"</span>
<span class="c1"># Create a new folder called 'Results' (if does not exist) to that folder using os.makedirs() function</span>
<span class="n">resultFolder</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">outFolder</span><span class="p">,</span> <span class="s1">'Results'</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">resultFolder</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">resultFolder</span><span class="p">)</span>
<span class="c1"># Iterate over the</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">grouped</span><span class="p">:</span>
<span class="c1"># Format the filename (replace spaces with underscores)</span>
<span class="n">outName</span> <span class="o">=</span> <span class="s2">"</span><span class="si">%s</span><span class="s2">.shp"</span> <span class="o">%</span> <span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">" "</span><span class="p">,</span> <span class="s2">"_"</span><span class="p">)</span>
<span class="c1"># Print some information for the user</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Processing: </span><span class="si">%s</span><span class="s2">"</span> <span class="o">%</span> <span class="n">key</span><span class="p">)</span>
<span class="c1"># Create an output path</span>
<span class="n">outpath</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">resultFolder</span><span class="p">,</span> <span class="n">outName</span><span class="p">)</span>
<span class="c1"># Export the data</span>
<span class="n">values</span><span class="o">.</span><span class="n">to_file</span><span class="p">(</span><span class="n">outpath</span><span class="p">)</span>
</pre></div>
</div>
<p>Now we have saved those individual fishes into separate Shapefiles and named the file according to the species name. These kind of grouping operations can be really
handy when dealing with Shapefiles. Doing similar process manually would be really laborious and error-prone.</p>
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