.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_plot_choropleth_map_population.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_choropleth_map_population.py:


Population by macroregion
=========================

In this example we want to visualize the most recent data on
brazilian population by macroregion.

Let's first retrieve the data with the `seriesbr <seriesbr.readthedocs.io>`_ package.


.. code-block:: default


    from seriesbr import ibge

    population = ibge.get_series(6462, 606, macroregions=True, last_n=1)








Then let's convert this ``DataFrame`` into a ``GeoDataFrame`` by assigning
to a column named "geometry" the macroregions' geometric objects.

You can get a geometric object for a given location with the function
:py:func:`mapsbr.ibgemaps.geocode`. It can either be a location code or name. If
it's a name, you'll need to pass which is its geographic level to avoid ambiguity.

For example, ``ibgemaps.geocode("Rio de Janeiro", geolevel="state")`` if you want Rio
de Janeiro state map or ``ibgemaps.geocode("Rio de Janeiro", geolevel="municipality")`` if
the map for Rio de Janeiro city.

Notice that the column with the geometric objects **has** to be named geometry. Otherwise
you'll need to call the ``set_geometry("geometries_column")`` method on the
``GeoDataFrame``.


.. code-block:: default


    import geopandas as gpd
    from mapsbr import ibgemaps

    gdf = gpd.GeoDataFrame(population)
    gdf["geometry"] = ibgemaps.geocode(population["Grande Região"], geolevel="macroregion")
    gdf.plot(column="Valor", legend=True, edgecolor="w")

    import matplotlib.pyplot as plt

    plt.gca().axis("off")
    plt.show()



.. image:: /auto_examples/images/sphx_glr_plot_choropleth_map_population_001.png
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  6.479 seconds)

**Estimated memory usage:**  21 MB


.. _sphx_glr_download_auto_examples_plot_choropleth_map_population.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_choropleth_map_population.py <plot_choropleth_map_population.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_choropleth_map_population.ipynb <plot_choropleth_map_population.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_