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<li class="toctree-l1 current"><a class="current reference internal" href="">Merging Data</a><ul>
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<div class="section" id="merging-data">
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<h1>Merging Data<a class="headerlink" href="#merging-data" title="Permalink to this headline">¶</a></h1>
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<p>There are two ways to combine datasets in <em>geopandas</em> – attribute joins and spatial joins.</p>
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<p>In an attribute join, a <code class="docutils literal"><span class="pre">GeoSeries</span></code> or <code class="docutils literal"><span class="pre">GeoDataFrame</span></code> is combined with a regular <em>pandas</em> <code class="docutils literal"><span class="pre">Series</span></code> or <code class="docutils literal"><span class="pre">DataFrame</span></code> based on a common variable. This is analogous to normal merging or joining in <em>pandas</em>.</p>
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<p>In a Spatial Join, observations from to <code class="docutils literal"><span class="pre">GeoSeries</span></code> or <code class="docutils literal"><span class="pre">GeoDataFrames</span></code> are combined based on their spatial relationship to one another.</p>
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<p>In the following examples, we use these datasets:</p>
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<div class="highlight-ipython"><div class="highlight"><pre><span class="gp">In [1]: </span><span class="n">world</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">gpd</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s">'naturalearth_lowres'</span><span class="p">))</span>
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<span class="gp">In [2]: </span><span class="n">cities</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">gpd</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">get_path</span><span class="p">(</span><span class="s">'naturalearth_cities'</span><span class="p">))</span>
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<span class="go"># For attribute join</span>
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<span class="gp">In [3]: </span><span class="n">country_shapes</span> <span class="o">=</span> <span class="n">world</span><span class="p">[[</span><span class="s">'geometry'</span><span class="p">,</span> <span class="s">'iso_a3'</span><span class="p">]]</span>
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<span class="gp">In [4]: </span><span class="n">country_names</span> <span class="o">=</span> <span class="n">world</span><span class="p">[[</span><span class="s">'name'</span><span class="p">,</span> <span class="s">'iso_a3'</span><span class="p">]]</span>
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<span class="go"># For spatial join</span>
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<span class="gp">In [5]: </span><span class="n">countries</span> <span class="o">=</span> <span class="n">world</span><span class="p">[[</span><span class="s">'geometry'</span><span class="p">,</span> <span class="s">'name'</span><span class="p">]]</span>
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<span class="gp">In [6]: </span><span class="n">countries</span> <span class="o">=</span> <span class="n">countries</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s">'name'</span><span class="p">:</span><span class="s">'country'</span><span class="p">})</span>
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</pre></div>
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</div>
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<div class="section" id="attribute-joins">
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<h2>Attribute Joins<a class="headerlink" href="#attribute-joins" title="Permalink to this headline">¶</a></h2>
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<p>Attribute joins are accomplished using the <code class="docutils literal"><span class="pre">merge</span></code> method. In general, it is recommended to use the <code class="docutils literal"><span class="pre">merge</span></code> method called from the spatial dataset. With that said, the stand-alone <code class="docutils literal"><span class="pre">merge</span></code> function will work if the GeoDataFrame is in the <code class="docutils literal"><span class="pre">left</span></code> argument; if a DataFrame is in the <code class="docutils literal"><span class="pre">left</span></code> argument and a GeoDataFrame is in the <code class="docutils literal"><span class="pre">right</span></code> position, the result will no longer be a GeoDataFrame.</p>
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<p>For example, consider the following merge that adds full names to a <code class="docutils literal"><span class="pre">GeoDataFrame</span></code> that initially has only ISO codes for each country by merging it with a <em>pandas</em> <code class="docutils literal"><span class="pre">DataFrame</span></code>.</p>
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<div class="highlight-ipython"><div class="highlight"><pre><span class="go"># `country_shapes` is GeoDataFrame with country shapes and iso codes</span>
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<span class="gp">In [7]: </span><span class="n">country_shapes</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[7]: </span><span class="go"></span>
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<span class="go"> geometry iso_a3</span>
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<span class="go">0 POLYGON ((61.21081709172574 35.65007233330923,... AFG</span>
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<span class="go">1 (POLYGON ((16.32652835456705 -5.87747039146621... AGO</span>
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<span class="go">2 POLYGON ((20.59024743010491 41.85540416113361,... ALB</span>
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<span class="go">3 POLYGON ((51.57951867046327 24.24549713795111,... ARE</span>
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<span class="go">4 (POLYGON ((-65.50000000000003 -55.199999999999... ARG</span>
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<span class="go"># `country_names` is DataFrame with country names and iso codes</span>
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<span class="gp">In [8]: </span><span class="n">country_names</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="go"> Out[8]: </span>
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<span class="go"> name iso_a3</span>
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<span class="go">0 Afghanistan AFG</span>
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<span class="go">1 Angola AGO</span>
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<span class="go">2 Albania ALB</span>
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<span class="go">3 United Arab Emirates ARE</span>
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<span class="go">4 Argentina ARG</span>
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<span class="go"># Merge with `merge` method on shared variable (iso codes):</span>
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<span class="gp">In [9]: </span><span class="n">country_shapes</span> <span class="o">=</span> <span class="n">country_shapes</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">country_names</span><span class="p">,</span> <span class="n">on</span><span class="o">=</span><span class="s">'iso_a3'</span><span class="p">)</span>
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<span class="gp">In [10]: </span><span class="n">country_shapes</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[10]: </span><span class="go"></span>
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<span class="go"> geometry iso_a3 \</span>
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<span class="go">0 POLYGON ((61.21081709172574 35.65007233330923,... AFG </span>
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<span class="go">1 (POLYGON ((16.32652835456705 -5.87747039146621... AGO </span>
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<span class="go">2 POLYGON ((20.59024743010491 41.85540416113361,... ALB </span>
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<span class="go">3 POLYGON ((51.57951867046327 24.24549713795111,... ARE </span>
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<span class="go">4 (POLYGON ((-65.50000000000003 -55.199999999999... ARG </span>
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<span class="go"> name </span>
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<span class="go">0 Afghanistan </span>
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<span class="go">1 Angola </span>
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<span class="go">2 Albania </span>
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<span class="go">3 United Arab Emirates </span>
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<span class="go">4 Argentina </span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="spatial-joins">
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<h2>Spatial Joins<a class="headerlink" href="#spatial-joins" title="Permalink to this headline">¶</a></h2>
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<p>In a Spatial Join, two geometry objects are merged based on their spatial relationship to one another.</p>
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<div class="highlight-ipython"><div class="highlight"><pre><span class="go"># One GeoDataFrame of countries, one of Cities.</span>
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<span class="go"># Want to merge so we can get each city's country.</span>
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<span class="gp">In [11]: </span><span class="n">countries</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[11]: </span><span class="go"></span>
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<span class="go"> geometry country</span>
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<span class="go">0 POLYGON ((61.21081709172574 35.65007233330923,... Afghanistan</span>
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<span class="go">1 (POLYGON ((16.32652835456705 -5.87747039146621... Angola</span>
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<span class="go">2 POLYGON ((20.59024743010491 41.85540416113361,... Albania</span>
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<span class="go">3 POLYGON ((51.57951867046327 24.24549713795111,... United Arab Emirates</span>
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<span class="go">4 (POLYGON ((-65.50000000000003 -55.199999999999... Argentina</span>
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<span class="gp">In [12]: </span><span class="n">cities</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="go"> Out[12]: </span>
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<span class="go"> geometry name</span>
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<span class="go">0 POINT (12.45338654497177 41.90328217996012) Vatican City</span>
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<span class="go">1 POINT (12.44177015780014 43.936095834768) San Marino</span>
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<span class="go">2 POINT (9.516669472907267 47.13372377429357) Vaduz</span>
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<span class="go">3 POINT (6.130002806227083 49.61166037912108) Luxembourg</span>
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<span class="go">4 POINT (158.1499743237623 6.916643696007725) Palikir</span>
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<span class="go"># Execute spatial join</span>
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<span class="gp">In [13]: </span><span class="n">cities_with_country</span> <span class="o">=</span> <span class="n">gpd</span><span class="o">.</span><span class="n">sjoin</span><span class="p">(</span><span class="n">cities</span><span class="p">,</span> <span class="n">countries</span><span class="p">,</span> <span class="n">how</span><span class="o">=</span><span class="s">"inner"</span><span class="p">,</span> <span class="n">op</span><span class="o">=</span><span class="s">'intersects'</span><span class="p">)</span>
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<span class="gp">In [14]: </span><span class="n">cities_with_country</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<span class="gh">Out[14]: </span><span class="go"></span>
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<span class="go"> geometry name index_right \</span>
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<span class="go">0 POINT (12.45338654497177 41.90328217996012) Vatican City 79 </span>
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<span class="go">1 POINT (12.44177015780014 43.936095834768) San Marino 79 </span>
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<span class="go">192 POINT (12.481312562874 41.89790148509894) Rome 79 </span>
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<span class="go">2 POINT (9.516669472907267 47.13372377429357) Vaduz 9 </span>
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<span class="go">184 POINT (16.36469309674374 48.20196113681686) Vienna 9 </span>
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<span class="go"> country </span>
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<span class="go">0 Italy </span>
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<span class="go">1 Italy </span>
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<span class="go">192 Italy </span>
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<span class="go">2 Austria </span>
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<span class="go">184 Austria </span>
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</pre></div>
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</div>
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<p>The <code class="docutils literal"><span class="pre">op</span></code> options determines the type of join operation to apply. <code class="docutils literal"><span class="pre">op</span></code> can be set to “intersects”, “within” or “contains” (these are all equivalent when joining points to polygons, but differ when joining polygons to other polygons or lines).</p>
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<p>Note more complicated spatial relationships can be studied by combining geometric operations with spatial join. To find all polygons within a given distance of a point, for example, one can first use the <code class="docutils literal"><span class="pre">buffer</span></code> method to expand each point into a circle of appropriate radius, then intersect those buffered circles with the polygons in question.</p>
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