ArcPy vs GeoPandas: A Practitioner's Honest Comparison

The Honest Answer

We're not going to bury the lead. Here's the punchline:

Side-by-Side Benchmarks

We benchmarked both libraries against the same dataset: 1M random polygons (simulated parcels) with 12 attribute columns. Single-threaded execution to keep comparisons fair.

The headline numbers favour GeoPandas 3-4x for vector operations. But the story changes at scale: above 10M records, you need Dask-GeoPandas or Spark, adding complexity. ArcPy handles large datasets with its 64-bit background geoprocessing - slower, but without the distributed computing overhead.

Code Comparison

The same task in both libraries. Buffer all features by 500m, dissolve by region, export.

import arcpy

arcpy.env.workspace = r"C:\data\project.gdb"
arcpy.env.overwriteOutput = True

# Buffer
arcpy.analysis.Buffer(
    "parcels",
    "parcels_buffered",
    "500 Meters",
    dissolve_option="NONE"
)

# Dissolve
arcpy.management.Dissolve(
    "parcels_buffered",
    "parcels_dissolved",
    dissolve_field="region"
)

# Export to shapefile
arcpy.conversion.FeatureClassToShapefile(
    "parcels_dissolved",
    r"C:\output"
)

import geopandas as gpd

gdf = gpd.read_file("parcels.gpkg")

# Buffer (ensure projected CRS)
gdf["geometry"] = gdf.geometry.buffer(500)

# Dissolve
dissolved = gdf.dissolve(by="region")

# Export to GeoParquet (modern)
dissolved.to_parquet("output.parquet")
# or: dissolved.to_file("output.shp")

The GeoPandas version is nearly half the code, uses native Python methods, and outputs to modern formats. But fair's fair - ArcPy's geoprocessing framework handles edge cases (datum transformations, environment settings) that GeoPandas leaves to you.

For raster work, the picture flips. ArcPy's Spatial Analyst lets you compute NDVI in a few lines with arcpy.sa.Raster() algebra. The open-source equivalent (rasterio + numpy) takes considerably more code and domain knowledge to handle edge cases like NoData values, band alignment, and output metadata.

Where GeoPandas Wins

Where ArcPy Wins

This section matters. If we only talked about where GeoPandas wins, you'd rightly question our objectivity. ArcPy has genuine strengths that no open-source library matches.

The Ecosystem Factor

Choosing between ArcPy and GeoPandas isn't just about the libraries. It's about which ecosystem you're buying into.

The ecosystem question often matters more than the library question. A team running Databricks for analytics won't run ArcPy there. A team producing maps for a government utility won't replace ArcGIS Pro with matplotlib. Start with your infrastructure, then pick the library.

When to Stick with ArcPy

We'd be doing you a disservice if we implied everyone should switch to GeoPandas. Here are six scenarios where ArcPy is the right call - and switching would waste time and money.

Migration Strategy

For teams that want to adopt GeoPandas without a cliff-edge migration, here's the phased approach we recommend. For a detailed function-by-function translation guide, see our ArcPy to GeoPandas migration guide.

The goal is not to eliminate ArcPy. It's to use the right tool for each job and stop paying for ESRI licences where an open-source alternative is faster, cheaper, and more maintainable. For automating the migrated workflows end-to-end, see our guide on geospatial workflow automation.

Frequently Asked Questions