.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/plot_largest_cities_usa.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_gallery_plot_largest_cities_usa.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_plot_largest_cities_usa.py:


Pointplot of US cities by population
====================================

This example, taken from the User Guide, plots cities in the contiguous United States by their
population. It demonstrates some of the range of styling options available in ``geoplot``.

.. GENERATED FROM PYTHON SOURCE LINES 8-46



.. image:: /gallery/images/sphx_glr_plot_largest_cities_usa_001.png
    :alt: Large cities in the contiguous United States, 2010
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Text(0.5, 1.0, 'Large cities in the contiguous United States, 2010')





|

.. code-block:: default



    import geopandas as gpd
    import geoplot as gplt
    import geoplot.crs as gcrs
    import matplotlib.pyplot as plt
    import mapclassify as mc

    continental_usa_cities = gpd.read_file(gplt.datasets.get_path('usa_cities'))
    continental_usa_cities = continental_usa_cities.query('STATE not in ["AK", "HI", "PR"]')
    contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
    scheme = mc.Quantiles(continental_usa_cities['POP_2010'], k=5)

    ax = gplt.polyplot(
        contiguous_usa,
        zorder=-1,
        linewidth=1,
        projection=gcrs.AlbersEqualArea(),
        edgecolor='white',
        facecolor='lightgray',
        figsize=(12, 7)
    )
    gplt.pointplot(
        continental_usa_cities,
        scale='POP_2010',
        limits=(2, 30),
        hue='POP_2010',
        cmap='Blues',
        scheme=scheme,
        legend=True,
        legend_var='scale',
        legend_values=[8000000, 2000000, 1000000, 100000],
        legend_labels=['8 million', '2 million', '1 million', '100 thousand'],
        legend_kwargs={'frameon': False, 'loc': 'lower right'},
        ax=ax
    )

    plt.title("Large cities in the contiguous United States, 2010")


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

   **Total running time of the script:** ( 0 minutes  1.280 seconds)


.. _sphx_glr_download_gallery_plot_largest_cities_usa.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_largest_cities_usa.py <plot_largest_cities_usa.py>`



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

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


.. only:: html

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

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