Introduction to GeoPandas¶
This quick tutorial introduces the key concepts and basic features of GeoPandas to help you get started with your projects.
GeoPandas, as the name suggests, extends the popular data science library pandas by adding support for geospatial data. If you are not familiar with
pandas, we recommend taking a quick look at its Getting started documentation before proceeding.
The core data structure in GeoPandas is the
geopandas.GeoDataFrame, a subclass of
pandas.DataFrame, that can store geometry columns and perform spatial operations. The
geopandas.GeoSeries, a subclass of
pandas.Series, handles the geometries. Therefore, your
GeoDataFrame is a combination of
pandas.Series, with traditional data (numerical, boolean, text etc.), and
geopandas.GeoSeries, with geometries (points, polygons etc.). You can have as many columns with geometries
as you wish; there’s no limit typical for desktop GIS software.
GeoSeries can contain any geometry type (you can even mix them within a single array) and has a
GeoSeries.crs attribute, which stores information about the projection (CRS stands for Coordinate Reference System). Therefore, each
GeoSeries in a
GeoDataFrame can be in a different projection, allowing you to have, for example, multiple versions (different projections) of the same geometry.
GeoSeries in a
GeoDataFrame is considered the active geometry, which means that all geometric operations applied to a
GeoDataFrame operate on this active column.
See more on data structures in the User Guide.
Let’s see how some of these concepts work in practice.
Reading and writing files¶
First, we need to read some data.
Assuming you have a file containing both data and geometry (e.g. GeoPackage, GeoJSON, Shapefile), you can read it using
geopandas.read_file(), which automatically detects the filetype and creates a
GeoDataFrame. This tutorial uses the
"nybb" dataset, a map of New York boroughs, which is part of the GeoPandas installation. Therefore, we use
geopandas.datasets.get_path() to retrieve the path to the dataset.
import geopandas path_to_data = geopandas.datasets.get_path("nybb") gdf = geopandas.read_file(path_to_data) gdf
|0||5||Staten Island||330470.010332||1.623820e+09||MULTIPOLYGON (((970217.022 145643.332, 970227....|
|1||4||Queens||896344.047763||3.045213e+09||MULTIPOLYGON (((1029606.077 156073.814, 102957...|
|2||3||Brooklyn||741080.523166||1.937479e+09||MULTIPOLYGON (((1021176.479 151374.797, 102100...|
|3||1||Manhattan||359299.096471||6.364715e+08||MULTIPOLYGON (((981219.056 188655.316, 980940....|
|4||2||Bronx||464392.991824||1.186925e+09||MULTIPOLYGON (((1012821.806 229228.265, 101278...|
Simple accessors and methods¶
Now we have our
GeoDataFrame and can start working with its geometry.
Since there was only one geometry column in the New York Boroughs dataset, this column automatically becomes the active geometry and spatial methods used on the
GeoDataFrame will be applied to the
To measure the area of each polygon (or MultiPolygon in this specific case), access the
GeoDataFrame.area attribute, which returns a
pandas.Series. Note that
GeoDataFrame.area is just
GeoSeries.area applied to the active geometry column.
But first, to make the results easier to read, set the names of the boroughs as the index:
gdf = gdf.set_index("BoroName")
gdf["area"] = gdf.area gdf["area"]
BoroName Staten Island 1.623822e+09 Queens 3.045214e+09 Brooklyn 1.937478e+09 Manhattan 6.364712e+08 Bronx 1.186926e+09 Name: area, dtype: float64
Getting polygon boundary and centroid¶
To get the boundary of each polygon (LineString), access the
gdf['boundary'] = gdf.boundary gdf['boundary']
BoroName Staten Island MULTILINESTRING ((970217.022 145643.332, 97022... Queens MULTILINESTRING ((1029606.077 156073.814, 1029... Brooklyn MULTILINESTRING ((1021176.479 151374.797, 1021... Manhattan MULTILINESTRING ((981219.056 188655.316, 98094... Bronx MULTILINESTRING ((1012821.806 229228.265, 1012... Name: boundary, dtype: geometry
Since we have saved boundary as a new column, we now have two geometry columns in the same
We can also create new geometries, which could be, for example, a buffered version of the original one (i.e.,
GeoDataFrame.buffer(10)) or its centroid:
gdf['centroid'] = gdf.centroid gdf['centroid']
BoroName Staten Island POINT (941639.450 150931.991) Queens POINT (1034578.078 197116.604) Brooklyn POINT (998769.115 174169.761) Manhattan POINT (993336.965 222451.437) Bronx POINT (1021174.790 249937.980) Name: centroid, dtype: geometry
We can also measure how far each centroid is from the first centroid location.
first_point = gdf['centroid'].iloc gdf['distance'] = gdf['centroid'].distance(first_point) gdf['distance']
BoroName Staten Island 0.000000 Queens 103781.535276 Brooklyn 61674.893421 Manhattan 88247.742789 Bronx 126996.283623 Name: distance, dtype: float64
geopandas.GeoDataFrame is a subclass of
pandas.DataFrame, so we have all the pandas functionality available to use on the geospatial dataset — we can even perform data manipulations with the attributes and geometry information together.
For example, to calculate the average of the distances measured above, access the ‘distance’ column and call the mean() method on it:
GeoPandas can also plot maps, so we can check how the geometries appear in space. To plot the active geometry, call
GeoDataFrame.plot(). To color code by another column, pass in that column as the first argument. In the example below, we plot the active geometry column and color code by the
"area" column. We also want to show a legend (
You can also explore your data interactively using
GeoDataFrame.explore(), which behaves in the same way
plot() does but returns an interactive map instead.