Is there a good way to read CSVs that contain a WKT column straight into a GeoPandas GeoDataFrame?

I've seen several solutions that take a two-step approach:

  1. Read the CSV into a Pandas DataFrame
  2. Pass the DataFrame to GeoPandas and use shapely.wkt.loads() to generate the actual geometries

I'm trying to avoid this two-step process and find a way to do this in one single step. However, the approach I use feels a little dirty and I just wanted to see if there's a better/more efficient way to do it. Here is a reproducible example where both approaches are showcased:

Reproducible example

# Loading libraries needed
import shapely
import pandas as pd
import geopandas as gpd

# Making mock CSV file
csv_filename = 'temp.csv'

                    "wkt_geom":['POINT (1 1)',
                                'POINT (2 2)',
                                'POINT (3 3)']})
 .to_csv(csv_filename, index=False))

# Two-step approach
df = pd.read_csv(csv_filename)

gdf = gpd.GeoDataFrame(data=df, 

# One-step approach
gdf = (gpd.read_file(csv_filename)
       .assign(geometry=lambda _df: shapely.wkt.loads(_df['wkt_geom']))

Streamlining the one-step approach

The one-step approach I showed above feels a little janky: it relies on the assign command and then requires me to use the set_geometry command.

Is there a more direct way we can read a CSV file containing a WKT column straight into a GeoPandas GeoDataFrame in such a way that the WKT column gets automatically parsed?

1 Answer 1


You can also use geopandas.GeoSeries.from_wkt:

df = pd.read_csv(csv_filename)
    ids     wkt_geom
0    1  POINT (1 1)
1    2  POINT (2 2)
2    3  POINT (3 3)

s = gpd.GeoSeries.from_wkt(df.wkt_geom)
gdf = gpd.GeoDataFrame(data=df, geometry=s)
     ids     wkt_geom          geometry
 0    1  POINT (1 1)  POINT (1.00000 1.00000)
 1    2  POINT (2 2)  POINT (2.00000 2.00000)
 2    3  POINT (3 3)  POINT (3.00000 3.00000)
  • Oh yeah, I forgot about the from_wkt method! This is great, thank you!!! I see that your solution takes the multi-step approach here of loading the CSV into a DF and then using that DF to make a GDF. Do you see a way of using a one-shot approach for that? The only solution I could think of also used the assign and set_geometry methods: gpd.read_file(csv_filename).assign(geom=lambda _df: gpd.GeoSeries.from_wkt(_df['wkt_geom'])).set_geometry('geom')
    – Felipe D.
    Commented Jul 14, 2023 at 19:13
  • Why do you want to use .assign
    – gene
    Commented Jul 15, 2023 at 13:19
  • Oh, sorry, I wasn't clear with my comment. I don't want to use assign. I just don't know how to perform a one-line process without using it. And my question in my last comment was geared towards: is it possible to do this in one step without the assign command? Sorry for the lack of clarity =)
    – Felipe D.
    Commented Jul 16, 2023 at 16:05

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