Post #2: Reading and Writing Shapefiles using GeoPandas

 

Title: How to Read and Write Shapefiles with GeoPandas (Python GIS Tutorial)

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📍Introduction

In GIS, shapefiles are one of the most common formats you'll work with. With Python and GeoPandas, you can read, edit, and write shapefiles with just a few lines of code. It’s a powerful way to automate tasks and reduce your dependency on desktop tools.

Let’s look at how to load shapefiles, view their structure, filter features, and save new versions.

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📂 Step 1: Read a Shapefile

python
import geopandas as gpd

# Load shapefile
roads = gpd.read_file("data/roads.shp")

# Preview the data
print(roads.head())

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🧠 What’s Inside a GeoDataFrame?

You can check out the column names and geometry type like this:

python
print(roads.columns)
print(roads.geom_type.unique())
print(roads.crs)  # Coordinate reference system

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🔍 Step 2: Filter Features

Let’s say we only want to keep major roads:

python
major_roads = roads[roads["road_type"] == "Major"]
print(f"Total major roads: {len(major_roads)}")

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📈 Optional: Plot the Filtered Data

python
import matplotlib.pyplot as plt

major_roads.plot(color="red")
plt.title("Major Roads Only")
plt.show()

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💾 Step 3: Write a New Shapefile

Save the filtered features to a new shapefile:

python
major_roads.to_file("output/major_roads.shp")

Or export it as GeoJSON:

python
major_roads.to_file("output/major_roads.geojson", driver="GeoJSON")

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🔁 Bonus: Reproject the Data

Need your data in WGS84 (EPSG:4326)? Use:

python
roads_4326 = roads.to_crs("EPSG:4326")
roads_4326.to_file("output/roads_wgs84.shp")

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🧹 Tips for Working with Shapefiles

• Always keep all .shp, .shx, .dbf, .prj files together.

• For non-English column names or long field names, consider simplifying them before exporting.

• Use .to_crs() before exporting if your tools expect a specific projection (e.g., WGS84 for web maps).

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🎯 Conclusion

In just a few lines of Python, you can read, filter, and save shapefiles—perfect for automating spatial workflows or cleaning data before analysis.

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📌 Next Up:

➡️ Spatial Joins and Attribute Queries in Python GIS