csv - Convert a pandas DataFrame to a GeoDataFrame

This seems like a simple enough question, but I can't figure out how to convert a pandas DataFrame to a GeoDataFrame for a spatial join.

Here is an example of what my data looks like using df.head():

    Date/Time           Lat       Lon       ID
0   4/1/2014 0:11:00    40.7690   -73.9549  140
1   4/1/2014 0:17:00    40.7267   -74.0345  NaN

In fact, this dataframe was created from a CSV so if it's easier to read the CSV in directly as a GeoDataFrame that's fine too.

Answer

Convert the DataFrame's content (e.g. Lat and Lon columns) into appropriate Shapely geometries first and then use them together with the original DataFrame to create a GeoDataFrame.

from geopandas import GeoDataFrame
from shapely.geometry import Point


geometry = [Point(xy) for xy in zip(df.Lon, df.Lat)]
df = df.drop(['Lon', 'Lat'], axis=1)
crs = {'init': 'epsg:4326'}
gdf = GeoDataFrame(df, crs=crs, geometry=geometry)

Result:

    Date/Time           ID      geometry
0   4/1/2014 0:11:00    140     POINT (-73.95489999999999 40.769)
1   4/1/2014 0:17:00    NaN     POINT (-74.03449999999999 40.7267)

---

Since the geometries often come in the WKT format, I thought I'd include an example for that case as well:

import geopandas as gpd
import shapely.wkt

geometry = df['wktcolumn'].map(shapely.wkt.loads)
df = df.drop('wktcolumn', axis=1)
crs = {'init': 'epsg:4326'}
gdf = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)