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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_gallery_scalar_data_geometry_data.py>`
        to download the full example code

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

.. _sphx_glr_gallery_scalar_data_geometry_data.py:


Associating data with geometries
--------------------------------

This example shows how to colour geometries based on a data array.  This
functionality is available since Cartopy 0.23.

.. GENERATED FROM PYTHON SOURCE LINES 9-49



.. image-sg:: /gallery/scalar_data/images/sphx_glr_geometry_data_001.png
   :alt: Country Population Estimates
   :srcset: /gallery/scalar_data/images/sphx_glr_geometry_data_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/circleci/project/lib/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_countries.zip
      warnings.warn(f'Downloading: {url}', DownloadWarning)






|

.. code-block:: Python

    import matplotlib.colors as mcolors
    import matplotlib.pyplot as plt

    import cartopy.crs as ccrs
    import cartopy.io.shapereader as shpreader


    def main():
        # Load Natural Earth's country shapefiles.
        shpfilename = shpreader.natural_earth(resolution='110m',
                                              category='cultural',
                                              name='admin_0_countries')
        reader = shpreader.Reader(shpfilename)
        countries = reader.records()

        # Get hold of the geometry and population estimate from each country's record.
        geometries = []
        population_estimates = []

        for country in countries:
            geometries.append(country.geometry)
            population_estimates.append(country.attributes['POP_EST'])

        # Set up a figure and an axes with the Eckert VI projection.
        fig = plt.figure()
        ax = fig.add_subplot(projection=ccrs.EckertVI())

        # Plot the geometries coloured according to population estimate.
        art = ax.add_geometries(geometries, crs=ccrs.PlateCarree(),
                                array=population_estimates, cmap='YlGnBu',
                                norm=mcolors.LogNorm(vmin=1e6))
        cbar = fig.colorbar(art, orientation='horizontal', extend='min')
        cbar.set_label('Number of people')
        fig.suptitle('Country Population Estimates', fontsize='x-large')

        plt.show()


    if __name__ == '__main__':
        main()


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

   **Total running time of the script:** (0 minutes 1.900 seconds)


.. _sphx_glr_download_gallery_scalar_data_geometry_data.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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