.. currentmodule:: geomappy.. note:: This tutorial was generated from an IPython notebook that can be
          downloaded `here <../../../source/notebooks/plotting_shapes.ipynb>`_.

.. _plotting_shapes:


Plotting choropleth shapes
==========================

.. code:: python

    import geopandas as gpd
    import matplotlib.pyplot as plt
    
    import os
    
    from cartopy.crs import PlateCarree
    
    import geomappy as mp
    import geomappy.plot_utils


.. code:: python

    os.chdir("../../../")

\|Loading data on river plastic mobilisation when flood events happen
(Roebroek et al., 2021).

.. code:: python

    df = gpd.read_file("data/countries/plastic_mobilisation.shp")
    df.columns




.. parsed-literal::

    Index(['featurecla', 'scalerank', 'LABELRANK', 'SOVEREIGNT', 'SOV_A3',
           'ADM0_DIF', 'LEVEL', 'TYPE', 'ADMIN', 'ADM0_A3',
           ...
           'NAME_ZH', 'e_1', 'e_10', 'e_20', 'e_50', 'e_100', 'e_200', 'e_500',
           'jump', 'geometry'],
          dtype='object', length=103)



The data shows like this when plotted within the geopandas dataframe.

.. code:: python

    df.plot()
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_6_0.png


In its most simple form, the ``plot_shapes`` function does exactly the
same (with some minor esthetic changes)

.. code:: python

    mp.plot_shapes(df=df)
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_8_0.png


Choropleth capabilities are available when selecting a column to express
the values. In this case ``e_10`` expresses plastic mobilisation when
relatively low-impact floods occur.

.. code:: python

    mp.plot_shapes(df=df, values='e_10')
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_10_0.png


Similarly to ``plot_raster`` it provides ``vmin``, ``vmax`` and ``bins``
to enhance visibility.

.. code:: python

    mp.plot_shapes(df=df, values='e_10', vmax=10000000)
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_12_0.png


.. code:: python

    bins = [0, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000]

.. code:: python

    mp.plot_shapes(df=df, values='e_10', bins=bins)
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_14_0.png


To plot a binary classification of the data, provide a single bin:

.. code:: python

    mp.plot_shapes(df=df, values='e_10', bins=[1000000])
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_16_0.png


Again a basemap is easily provided

.. code:: python

    f, ax = plt.subplots(figsize=(20, 20), subplot_kw={'projection': PlateCarree()})
    bounds = df.total_bounds
    extent = bounds[0], bounds[2], bounds[1], bounds[3]
    ax.set_extent(extent)
    geomappy.plot_utils.add_gridlines(ax, 30)
    geomappy.plot_utils.add_ticks(ax, 30)
    
    mp.plot_shapes(df=df, values='e_10', cmap="Reds", bins=bins, ax=ax)
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_18_0.png


The legend is hard to interpret this way. To solve this, the legend
items can be overloaded with their E notation. In addition, a customised
label can be placed next to the colorbar.

.. code:: python

    f, ax = plt.subplots(figsize=(20, 20), subplot_kw={'projection': PlateCarree()})
    bounds = df.total_bounds
    extent = bounds[0], bounds[2], bounds[1], bounds[3]
    ax.set_extent(extent)
    geomappy.plot_utils.add_gridlines(ax, 30)
    geomappy.plot_utils.add_ticks(ax, 30)
    
    legend_ax = mp.create_colorbar_axes(ax=ax, width=0.02, pad=0.03)
    im, l = mp.plot_shapes(df=df, values='e_10', cmap="Reds", bins=bins, ax=ax, legend_ax=legend_ax)
    l.ax.set_yticks(l.ax.get_yticks(), [0, "E2", "E3", "E4", "E5", "E6", "E7", "E8"])
    
    plt.show()



.. image:: plotting_shapes_files/plotting_shapes_20_0.png