Reading Multiple Shapefiles with Geopandas from a Zip File in Memory

What Will You Learn?

Discover how to efficiently read multiple shapefiles using Geopandas directly from a zip file stored in memory. This tutorial will equip you with the skills to handle geospatial data seamlessly.

Introduction to the Problem and Solution

Working with geospatial data often involves dealing with shapefiles, which are sometimes compressed into zip files for easier distribution. Extracting and reading multiple shapefiles from a zip file can be complex, but with Geopandas and Python, we can simplify this process.

The solution entails loading the zipped data into memory, extracting the relevant shapefiles, and then utilizing Geopandas to read them. By adopting this approach, we can effortlessly manage multiple shapefiles without manual extraction.


import zipfile
from io import BytesIO
import geopandas as gpd

# Assume 'zip_data' contains the binary data of the zip file

with zipfile.ZipFile(BytesIO(zip_data)) as z:
    # List all files in the zip archive
    files = z.namelist()

    # Filter only .shp files assuming they are part of different layers 
    shp_files = [file for file in files if file.endswith('.shp')]

    # Read each shapefile as a GeoDataFrame
    dfs = [gpd.read_file('zip://' + path) for path in shp_files]

# Concatenate all GeoDataFrames into one if needed
final_gdf = gpd.GeoDataFrame(pd.concat(dfs, ignore_index=True))

# Copyright PHD

The above code block is credited to


  • Import necessary libraries like zipfile, BytesIO from io, and geopandas.
  • Load binary zip data into memory using BytesIO.
  • Extract filenames within the zip archive and filter out only .shp files.
  • Read each .shp file as a separate GeoDataFrame using gpd.read_file().
  • Combine all GeoDataFrames into one if required.
    How do I install Geopandas?

    To install Geopandas, use pip:

    pip install geopandas  
    # Copyright PHD

    Can I read other geospatial formats apart from shapefiles with Geopandas?

    Yes, Geopandas supports various formats such as GeoJSON, CSV containing WKT (Well-Known Text), etc.

    Is it possible to perform spatial operations on these merged GeoDataFrames?

    Absolutely! Spatial operations like intersection, buffer analysis can be easily performed on merged datasets.

    What should I do if there are projection mismatches among the individual shapefiles?

    Reproject them before merging or conducting any spatial analysis for consistent results.

    Can I save this final combined dataset back to disk?

    Yes, you can save your final GeoDataFrame to various formats like Shapefile or GeoJSON using methods provided by Geopandas.


    Mastering how to read multiple shapefiles directly from a zip file in memory using Geopandas streamlines geospatial data processing. This method enhances efficiency by eliminating manual extraction steps and enables seamless integration of multiple shapefiles for analysis and visualization purposes.

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