#!/usr/bin/env python
# coding: utf-8

# 
# # 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.
# 

# In[ ]:


import pandas as pd
import geopandas
import matplotlib.pyplot as plt
from geodatasets import get_path


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

# In[ ]:


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. We use geopandas
# ``points_from_xy()`` to transform **Longitude** and **Latitude** into a list
# of ``shapely.Point`` objects and set it as a ``geometry`` while creating the
# ``GeoDataFrame``. (note that ``points_from_xy()`` is an enhanced wrapper for
# ``[Point(x, y) for x, y in zip(df.Longitude, df.Latitude)]``). The ``crs``
# value is also set to explicitly state the geometry data defines latitude/
# longitude world geodetic degree values. This is important for the correct 
# interpretation of the data, such as when plotting with data in other formats.

# In[ ]:


gdf = geopandas.GeoDataFrame(
    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude), crs="EPSG:4326"
)


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

# In[ ]:


print(gdf.head())


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

# In[ ]:


world = geopandas.read_file(get_path("naturalearth.land"))

# We restrict to South America.
ax = world.clip([-90, -55, -25, 15]).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.
# 
# 

# In[ ]:


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


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

# In[ ]:


from shapely import wkt

df["Coordinates"] = geopandas.GeoSeries.from_wkt(df["Coordinates"])


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

# In[ ]:


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

print(gdf.head())


# Again, we can plot our ``GeoDataFrame``.
# 
# 

# In[ ]:


ax = world.clip([-90, -55, -25, 15]).plot(color="white", edgecolor="black")

gdf.plot(ax=ax, color="red")

plt.show()