create_geopandas_from_pandas.py

"""
Creating a GeoDataFrame from a DataFrame with coordinates
---------------------------------------------------------

This example shows how to create a ``GeoDataFrame`` when starting from
a *regular* ``DataFrame`` that has coordinates either WKT
(`well-known text <https://en.wikipedia.org/wiki/Well-known_text>`_)
format, or in
two columns.

"""
import pandas as pd
import geopandas
from shapely.geometry import Point
import matplotlib.pyplot as plt

###############################################################################
# From longitudes and latitudes
# =============================
#
# First, let's consider a ``DataFrame`` containing cities and their respective
# longitudes and latitudes.

df = pd.DataFrame(
    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],
     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})

###############################################################################
# A ``GeoDataFrame`` needs a ``shapely`` object, so we create a new column
# **Coordinates** as a tuple of **Longitude** and **Latitude** :

df['Coordinates'] = list(zip(df.Longitude, df.Latitude))

###############################################################################
# Then, we transform tuples to ``Point`` :

df['Coordinates'] = df['Coordinates'].apply(Point)

###############################################################################
# Now, we can create the ``GeoDataFrame`` by setting ``geometry`` with the
# coordinates created previously.

gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')

###############################################################################
# ``gdf`` looks like this :

print(gdf.head())

###############################################################################
# Finally, we plot the coordinates over a country-level map.

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

# We restrict to South America.
ax = world[world.continent == 'South America'].plot(
    color='white', edgecolor='black')

# We can now plot our GeoDataFrame.
gdf.plot(ax=ax, color='red')

plt.show()

###############################################################################
# From WKT format
# ===============
# Here, we consider a ``DataFrame`` having coordinates in WKT format.

df = pd.DataFrame(
    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],
     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],
     'Coordinates': ['POINT(-34.58 -58.66)', 'POINT(-15.78 -47.91)',
                     'POINT(-33.45 -70.66)', 'POINT(4.60 -74.08)',
                     'POINT(10.48 -66.86)']})

###############################################################################
# We use ``shapely.wkt`` sub-module to parse wkt format:
from shapely import wkt

df['Coordinates'] = df['Coordinates'].apply(wkt.loads)

###############################################################################
#  The ``GeoDataFrame`` is constructed as follows :

gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')

print(gdf.head())