Reading and writing files#

Reading spatial data#

GeoPandas can read almost any vector-based spatial data format including ESRI shapefile, GeoJSON files and more using the geopandas.read_file() command:


which returns a GeoDataFrame object. This is possible because GeoPandas makes use of the massive open-source program called GDAL/OGR designed to facilitate spatial data transformations, through the Python packages Fiona or pyogrio, which both provide bindings to GDAL.


GeoPandas currently defaults to use Fiona as the engine in read_file. However, GeoPandas 1.0 will switch to use pyogrio as the default engine, since pyogrio can provide a significant speedup compared to Fiona. We recommend to already install pyogrio and specify the engine by using the engine keyword (geopandas.read_file(..., engine="pyogrio")), or by setting the default for the engine keyword globally with:

geopandas.options.io_engine = "pyogrio"

Any arguments passed to geopandas.read_file() after the file name will be passed directly to or pyogrio.read_dataframe(), which does the actual data importation. In general, geopandas.read_file() is pretty smart and should do what you want without extra arguments, but for more help, type:

import fiona; help(
import pyogrio; help(pyogrio.read_dataframe)

Among other things, one can explicitly set the driver (shapefile, GeoJSON) with the driver keyword, or pick a single layer from a multi-layered file with the layer keyword:

countries_gdf = geopandas.read_file("package.gpkg", layer='countries')

GeoPandas can also load resources directly from a web URL, for example for GeoJSON files from

url = ""
df = geopandas.read_file(url)

You can also load ZIP files that contain your data:

zipfile = "zip:///Users/name/Downloads/"
states = geopandas.read_file(zipfile)

If the dataset is in a folder in the ZIP file, you have to append its name:

zipfile = "zip:///Users/name/Downloads/!data"

If there are multiple datasets in a folder in the ZIP file, you also have to specify the filename:

zipfile = "zip:///Users/name/Downloads/!data/gadm36_AFG_1.shp"

It is also possible to read any file-like objects with a read() method, such as a file handler (e.g. via built-in open() function) or StringIO:

filename = "test.geojson"
file = open(filename)
df = geopandas.read_file(file)

File-like objects from fsspec can also be used to read data, allowing for any combination of storage backends and caching supported by that project:

path = "simplecache::"
with as file:
    df = geopandas.read_file(file)

You can also read path objects:

import pathlib
path_object = pathlib.path(filename)
df = geopandas.read_file(path_object)

Reading subsets of the data#

Since geopandas is powered by GDAL, you can take advantage of pre-filtering when loading in larger datasets. This can be done geospatially with a geometry or bounding box. You can also filter rows loaded with a slice. Read more at geopandas.read_file().

Geometry filter#

The geometry filter only loads data that intersects with the geometry.

import geodatasets

gdf_mask = geopandas.read_file(
gdf = geopandas.read_file(
    geodatasets.get_path(" education"),
    mask=gdf_mask["Coney Island"],

Bounding box filter#

The bounding box filter only loads data that intersects with the bounding box.

bbox = (
    1031051.7879884212, 224272.49231459625, 1047224.3104931959, 244317.30894023244
gdf = geopandas.read_file(

Row filter#

Filter the rows loaded in from the file using an integer (for the first n rows) or a slice object.

gdf = geopandas.read_file(
gdf = geopandas.read_file(
    rows=slice(10, 20),

Field/column filters#

Load in a subset of fields from the file:


Requires Fiona 1.9+

gdf = geopandas.read_file(
    include_fields=["name", "rent2008", "kids2000"],


Requires Fiona 1.8+

gdf = geopandas.read_file(
    ignore_fields=["rent2008", "kids2000"],

Skip loading geometry from the file:


Requires Fiona 1.8+


Returns pandas.DataFrame

pdf = geopandas.read_file(

SQL WHERE filter#

Added in version 0.12.

Load in a subset of data with a SQL WHERE clause.


Requires Fiona 1.9+ or the pyogrio engine.

gdf = geopandas.read_file(
    where="subborough='Coney Island'",

Supported drivers#

Currently fiona only exposes the default drivers. To display those, type:

import fiona; fiona.supported_drivers

There is a list of available drivers which are unexposed but supported (depending on the GDAL-build). You can activate these on runtime by updating the supported_drivers dictionary like:

fiona.supported_drivers["NAS"] = "raw"

When using pyogrio, all drivers supported by the GDAL installation are enabled, and you can check those with:

import pyogrio; pyogrio.list_drivers()

Writing spatial data#

GeoDataFrames can be exported to many different standard formats using the geopandas.GeoDataFrame.to_file() method. For a full list of supported formats, type import fiona; fiona.supported_drivers.

In addition, GeoDataFrames can be uploaded to PostGIS database (starting with GeoPandas 0.8) by using the geopandas.GeoDataFrame.to_postgis() method.


GeoDataFrame can contain more field types than supported by most of the file formats. For example tuples or lists can be easily stored in the GeoDataFrame, but saving them to e.g. GeoPackage or Shapefile will raise a ValueError. Before saving to a file, they need to be converted to a format supported by a selected driver.

Writing to Shapefile:


Writing to GeoJSON:

countries_gdf.to_file("countries.geojson", driver='GeoJSON')

Writing to GeoPackage:

countries_gdf.to_file("package.gpkg", layer='countries', driver="GPKG")
cities_gdf.to_file("package.gpkg", layer='cities', driver="GPKG")

Spatial databases#

GeoPandas can also get data from a PostGIS database using the geopandas.read_postgis() command.

Writing to PostGIS:

from sqlalchemy import create_engine
db_connection_url = "postgresql://myusername:mypassword@myhost:5432/mydatabase";
engine = create_engine(db_connection_url)
countries_gdf.to_postgis("countries_table", con=engine)

Apache Parquet and Feather file formats#

Added in version 0.8.0.

GeoPandas supports writing and reading the Apache Parquet and Feather file formats.

Apache Parquet is an efficient, columnar storage format (originating from the Hadoop ecosystem). It is a widely used binary file format for tabular data. The Feather file format is the on-disk representation of the Apache Arrow memory format, an open standard for in-memory columnar data.

The geopandas.read_parquet(), geopandas.read_feather(), GeoDataFrame.to_parquet() and GeoDataFrame.to_feather() methods enable fast roundtrip from GeoPandas to those binary file formats, preserving the spatial information.


This is tracking version 1.0.0 of the GeoParquet specification at: opengeospatial/geoparquet.

Previous versions are still supported as well. By default, the latest version is used when writing files (older versions can be specified using the schema_version keyword), and GeoPandas supports reading files of any version.