mirror of
https://github.com/wassname/geopandas.git
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Martin Fleischmann
953753896a
* use pydata theme * structure * fix links * adapt colors * getting started * buttons * buttons * rename, use myst, fill stuff * myst to env * cleanup * covered_by * review comments * fix link * use api
243 lines
8.3 KiB
ReStructuredText
243 lines
8.3 KiB
ReStructuredText
.. currentmodule:: geopandas
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.. ipython:: python
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:suppress:
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import geopandas
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import matplotlib
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orig = matplotlib.rcParams['figure.figsize']
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matplotlib.rcParams['figure.figsize'] = [orig[0] * 1.5, orig[1]]
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import matplotlib.pyplot as plt
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plt.close('all')
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Mapping Tools
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=========================================
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*geopandas* provides a high-level interface to the ``matplotlib`` library for making maps. Mapping shapes is as easy as using the ``plot()`` method on a ``GeoSeries`` or ``GeoDataFrame``.
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Loading some example data:
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.. ipython:: python
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world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
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cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
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We can now plot those GeoDataFrames:
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.. ipython:: python
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# Examine country GeoDataFrame
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world.head()
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# Basic plot, random colors
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@savefig world_randomcolors.png
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world.plot();
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Note that in general, any options one can pass to `pyplot <http://matplotlib.org/api/pyplot_api.html>`_ in ``matplotlib`` (or `style options that work for lines <http://matplotlib.org/api/lines_api.html>`_) can be passed to the ``plot()`` method.
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Choropleth Maps
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-----------------
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*geopandas* makes it easy to create Choropleth maps (maps where the color of each shape is based on the value of an associated variable). Simply use the plot command with the ``column`` argument set to the column whose values you want used to assign colors.
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.. ipython:: python
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# Plot by GDP per capta
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world = world[(world.pop_est>0) & (world.name!="Antarctica")]
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world['gdp_per_cap'] = world.gdp_md_est / world.pop_est
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@savefig world_gdp_per_cap.png
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world.plot(column='gdp_per_cap');
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Creating a legend
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~~~~~~~~~~~~~~~~~
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When plotting a map, one can enable a legend using the ``legend`` argument:
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.. ipython:: python
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# Plot population estimates with an accurate legend
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots(1, 1)
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@savefig world_pop_est.png
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world.plot(column='pop_est', ax=ax, legend=True)
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However, the default appearance of the legend and plot axes may not be desirable. One can define the plot axes (with ``ax``) and the legend axes (with ``cax``) and then pass those in to the ``plot`` call. The following example uses ``mpl_toolkits`` to vertically align the plot axes and the legend axes:
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.. ipython:: python
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# Plot population estimates with an accurate legend
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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fig, ax = plt.subplots(1, 1)
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divider = make_axes_locatable(ax)
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cax = divider.append_axes("right", size="5%", pad=0.1)
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@savefig world_pop_est_fixed_legend_height.png
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world.plot(column='pop_est', ax=ax, legend=True, cax=cax)
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And the following example plots the color bar below the map and adds its label using ``legend_kwds``:
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.. ipython:: python
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# Plot population estimates with an accurate legend
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots(1, 1)
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@savefig world_pop_est_horizontal.png
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world.plot(column='pop_est',
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ax=ax,
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legend=True,
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legend_kwds={'label': "Population by Country",
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'orientation': "horizontal"})
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Choosing colors
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~~~~~~~~~~~~~~~~
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One can also modify the colors used by ``plot`` with the ``cmap`` option (for a full list of colormaps, see the `matplotlib website <http://matplotlib.org/users/colormaps.html>`_):
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.. ipython:: python
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@savefig world_gdp_per_cap_red.png
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world.plot(column='gdp_per_cap', cmap='OrRd');
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To make the color transparent for when you just want to show the boundary, you have two options. One option is to do ``world.plot(facecolor="none", edgecolor="black")``. However, this can cause a lot of confusion because ``"none"`` and ``None`` are different in the context of using ``facecolor`` and they do opposite things. ``None`` does the "default behavior" based on matplotlib, and if you use it for ``facecolor``, it actually adds a color. The second option is to use ``world.boundary.plot()``. This option is more explicit and clear.:
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.. ipython:: python
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@savefig world_gdp_per_cap_transparent.png
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world.boundary.plot();
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The way color maps are scaled can also be manipulated with the ``scheme`` option (if you have ``mapclassify`` installed, which can be accomplished via ``conda install -c conda-forge mapclassify``). The ``scheme`` option can be set to any scheme provided by mapclassify (e.g. 'box_plot', 'equal_interval',
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'fisher_jenks', 'fisher_jenks_sampled', 'headtail_breaks', 'jenks_caspall', 'jenks_caspall_forced', 'jenks_caspall_sampled', 'max_p_classifier', 'maximum_breaks', 'natural_breaks', 'quantiles', 'percentiles', 'std_mean' or 'user_defined'). Arguments can be passed in classification_kwds dict. See the `mapclassify documentation <https://pysal.org/mapclassify>`_ for further details about these map classification schemes.
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.. ipython:: python
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@savefig world_gdp_per_cap_quantiles.png
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world.plot(column='gdp_per_cap', cmap='OrRd', scheme='quantiles');
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Missing data
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~~~~~~~~~~~~
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In some cases one may want to plot data which contains missing values - for some features one simply does not know the value. Geopandas (from the version 0.7) by defaults ignores such features.
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.. ipython:: python
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import numpy as np
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world.loc[np.random.choice(world.index, 40), 'pop_est'] = np.nan
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@savefig missing_vals.png
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world.plot(column='pop_est');
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However, passing ``missing_kwds`` one can specify the style and label of features containing None or NaN.
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.. ipython:: python
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@savefig missing_vals_grey.png
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world.plot(column='pop_est', missing_kwds={'color': 'lightgrey'});
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@savefig missing_vals_hatch.png
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world.plot(
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column="pop_est",
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legend=True,
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scheme="quantiles",
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figsize=(15, 10),
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missing_kwds={
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"color": "lightgrey",
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"edgecolor": "red",
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"hatch": "///",
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"label": "Missing values",
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},
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);
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Maps with Layers
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-----------------
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There are two strategies for making a map with multiple layers -- one more succinct, and one that is a little more flexible.
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Before combining maps, however, remember to always ensure they share a common CRS (so they will align).
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.. ipython:: python
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# Look at capitals
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# Note use of standard `pyplot` line style options
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@savefig capitals.png
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cities.plot(marker='*', color='green', markersize=5);
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# Check crs
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cities = cities.to_crs(world.crs)
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# Now we can overlay over country outlines
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# And yes, there are lots of island capitals
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# apparently in the middle of the ocean!
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**Method 1**
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.. ipython:: python
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base = world.plot(color='white', edgecolor='black')
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@savefig capitals_over_countries_1.png
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cities.plot(ax=base, marker='o', color='red', markersize=5);
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**Method 2: Using matplotlib objects**
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.. ipython:: python
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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# set aspect to equal. This is done automatically
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# when using *geopandas* plot on it's own, but not when
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# working with pyplot directly.
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ax.set_aspect('equal')
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world.plot(ax=ax, color='white', edgecolor='black')
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cities.plot(ax=ax, marker='o', color='red', markersize=5)
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@savefig capitals_over_countries_2.png
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plt.show();
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Control the order of multiple layers in a plot
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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When plotting multiple layers, use ``zorder`` to take control of the order of layers being plotted.
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The lower the ``zorder`` is, the lower the layer is on the map and vice versa.
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Without specified ``zorder``, cities (Points) gets plotted below world (Polygons), following the default order based on geometry types.
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.. ipython:: python
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ax = cities.plot(color='k')
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@savefig zorder_default.png
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world.plot(ax=ax);
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We can set the ``zorder`` for cities higher than for world to move it of top.
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.. ipython:: python
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ax = cities.plot(color='k', zorder=2)
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@savefig zorder_set.png
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world.plot(ax=ax, zorder=1);
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Other Resources
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-----------------
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Links to jupyter Notebooks for different mapping tasks:
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`Making Heat Maps <http://nbviewer.jupyter.org/gist/perrygeo/c426355e40037c452434>`_
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.. ipython:: python
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:suppress:
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matplotlib.rcParams['figure.figsize'] = orig
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.. ipython:: python
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:suppress:
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import matplotlib.pyplot as plt
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plt.close('all')
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