"""
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.

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``.

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()