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 command:

geopandas.read_file()

which returns a GeoDataFrame object. This is possible because geopandas makes use of the great fiona library, which in turn makes use of a massive open-source program called GDAL/OGR designed to facilitate spatial data transformations.

Any arguments passed to geopandas.read_file() after the file name will be passed directly to fiona.open(), 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(fiona.open)

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')

Where supported in fiona, geopandas can also load resources directly from a web URL, for example for GeoJSON files from geojson.xyz:

url = "http://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_110m_land.geojson"
df = geopandas.read_file(url)

You can also load ZIP files that contain your data:

zipfile = "zip:///Users/name/Downloads/cb_2017_us_state_500k.zip"
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/gadm36_AFG_shp.zip!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/gadm36_AFG_shp.zip!data/gadm36_AFG_1.shp"

It is also possible to read any file-like objects with a os.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::http://download.geofabrik.de/antarctica-latest-free.shp.zip"
with fsspec.open(path) 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 Fiona, which 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

New in version 0.7.0.

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

gdf_mask = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_lowres")
)
gdf = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_cities"),
    mask=gdf_mask[gdf_mask.continent=="Africa"],
)

Bounding Box Filter

New in version 0.1.0.

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(
    geopandas.datasets.get_path("nybb"),
    bbox=bbox,
)

Row Filter

New in version 0.7.0.

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

gdf = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_lowres"),
    rows=10,
)
gdf = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_lowres"),
    rows=slice(10, 20),
)

Field/Column Filters

Load in a subset of fields from the file:

Note

Requires Fiona 1.8+

gdf = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_lowres"),
    ignore_fields=["iso_a3", "gdp_md_est"],
)

Skip loading geometry from the file:

Note

Requires Fiona 1.8+

Note

Returns pandas.DataFrame

pdf = geopandas.read_file(
    geopandas.datasets.get_path("naturalearth_lowres"),
    ignore_geometry=True,
)

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.

Note

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:

countries_gdf.to_file("countries.shp")

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

New 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.

Warning

This is an initial implementation of Parquet file support and associated metadata. This is tracking version 0.1.0 of the metadata specification at: https://github.com/geopandas/geo-arrow-spec

This metadata specification does not yet make stability promises. As such, we do not yet recommend using this in a production setting unless you are able to rewrite your Parquet or Feather files.

Data Structures Indexing and Selecting Data