Best GIS Python Packages for Mapping & Spatial Analysis
It is no exaggeration to say that Python has become one of the most popular programming languages for GIS, spatial analysis, remote sensing, and geospatial data science. The numerous open-source libraries allow processing both vector and raster data, conducting spatial analysis, creating maps, handling satellite images, and automating geospatial workflows.
For those who work as GIS analysts, remote sensing experts, data scientists, or developers creating geospatial applications, selecting the correct Python library becomes a key to increased efficiency and effectiveness.
Why Use Python for GIS?
Traditional GIS desktop software has the ability to offer great visualization and analysis capabilities. However, there are added benefits to using Python, including its ability to automate processes and integrate well into the data science workflow.
With Python, we can:
Process big spatial data sets.
Automate GIS tasks
Analyze vector and raster data.
Create interactive web maps.
Process satellite imagery
Perform spatial statistics
Convert geospatial formats
Integrate GIS tasks with machine learning tasks.
Create custom geospatial applications.
Process geospatial data in the cloud
Popular geospatial formats supported by the Python ecosystem include: Shapefile, GeoJSON, GeoPackage, GeoTIFF, Cloud Optimized GeoTIFF (COG), GeoParquet, KML, and LAS/LAZ.
GeoPandas
GeoPandas is one of the most valuable Python libraries when it comes to vector geospatial data processing. GeoPandas expands the pandas library by adding geometry types (points, lines, polygons) support.
GeoPandas may be especially helpful for GIS specialists who know pandas.
Main abilities
Spatial vector data read/write.
Spatial join
Buffering
Intersection and overlay operations
Transformations of coordinate reference systems
Geometry operations
Spatial filtering
Analysis based on attribute values
Example:
import geopandas as gpd
roads = gpd.read_file("roads.geojson")
print(roads.crs)
roads_utm = roads.to_crs("EPSG:32643")
buffers = roads_utm.buffer(50)
GeoPandas can efficiently work with small- to medium-sized vector datasets.
Shapely
Shapely allows users to do geometry operations in Python. Shapely is often used with GeoPandas for analysis of geometric data.
Shapes supported by Shapely include:
Point
LineString
Polygon
MultiPoint
MultiLineString
MultiPolygon
Geometry Collections
Example:
from shapely.geometry import Point location = Point(77.5946, 12.9716) print(location.x) print(location.y)
Shapely can perform operations such as intersection, union, difference, distance, buffering, and containment.
For example:
buffer = locationbuffer(0.01)For more accurate distance and buffer calculations, geographic coordinates should generally be projected into an appropriate projected CRS first.
Rasterio
Rasterio is a common Python library for handling raster datasets, especially GeoTIFFs.
It is a Python binding that facilitates geospatial raster data analysis.
Use cases
Satellite images manipulation
Digital elevation models processing
Land cover classification
Calculation of NDVI
Clipping rasters
Reprojection of rasters
Mosaic of rasters
Cloud-optimized GeoTIFF workflow
Example:
import rasterio
with rasterio.open("elevation.tif") as src:
elevation = src.read(1)
print(src.crs)
print(src.width, src.height)
Rasterio is a key package in raster-based GIS and remote sensing applications.
Fiona
The Fiona module is a Python interface for reading and writing geospatial data in vector format by using the GDAL/OGR software suite.
While many users get access to Fiona via GeoPandas, this can be a helpful package in case of more direct access to input/output of vector data.
Supports many geospatial data formats and works great in Python data processing workflows.
PyProj
Coordinate systems are an important part of GIS, and the package PyProj contains Python interfaces for performing coordinate transformations and projections.
Works with the PROJ library and allows you to transform coordinates from one coordinate system into another.
Example:
from pyproj import Transformer
transformer = Transformer.from_crs(
"EPSG:4326",
"EPSG:32643",
always_xy=True
)
x, y = transformer.transform(77.5946, 12.9716)
print(x, y)
PyProj can be especially helpful in case of:
EPSG codes
Geographic coordinate systems
Projected coordinate systems
Datum transformation
Coordinate conversion
Folium
Folium simplifies the creation of interactive maps in Python through the use of the Leaflet JavaScript map library.
It can be helpful in quickly visualizing geospatial data in Jupyter Notebooks or web environments.
Example:
import folium
m = folium.Map(
location=[37.7749, -122.4194],
zoom_start=10
)
folium.Marker(
[37.7749, -122.4194],
popup="San Francisco"
).add_to(m)
m
Folium can be used when there is a need for interactive maps.
Contextily
Contextily can be used to overlay web map tiles and basemaps on GeoPandas/Matplotlib maps.
For example, it can be used to plot vector layers on top of satellite imagery and street map layers.
Typical usage might look like this:
import contextily as ctx
ax = roads.plot()
ctx.add_basemap(ax)
Contextily is especially helpful when making professional static GIS maps for presentations.
Rasterstats
Rasterstats offers functions for calculating statistics on rasters by vector geometries.
It can be used when you need to calculate the answers to questions like:
What is the mean elevation inside each polygon?
What is the minimum temperature within a study area?
What is the mean NDVI for each farm field?
What is the maximum raster value within administrative boundaries?
Example:
from rasterstats import zonal_stats
stats = zonal_stats(
"fields.shp",
"ndvi.tif",
stats=["mean", "min", "max"]
)
print(stats)
This will make raster-vector operations easier in Python.
Xarray
The Xarray library is used for working with labeled multidimensional datasets. The tool is especially important when processing environmental data, climate datasets, remote sensing data, and time series data.
While conventional raster processing involves working with two-dimensional arrays, the Xarray library can conveniently accommodate dimensions like:
Time
Latitude
Longitude
Bands
Elevation
For example, a satellite data cube may be modeled using dimensions like:
time × latitude × longitude × band
The Xarray library is therefore suitable for processing large multidimensional Earth observation datasets.
Leafmap
Leafmap is yet another Python library that is used for interactive geospatial visualization.
It works with vector datasets, raster datasets, cloud datasets, and interactive maps.
Leafmap can be helpful for:
GIS with Jupyter notebooks
Visualization of remote sensing data
Analysis of rasters
Development of web maps
Exploration of geospatial data
In particular, Leafmap might come in handy when developing interactive geospatial notebooks without building a full JavaScript mapping application.
GDAL and Its Python Interface
GDAL (Geospatial Data Abstraction Library) is an essential technology in the geospatial world.
It supports a wide variety of raster and vector file formats and helps to perform:
Format conversion
Reprojection of rasters
Raster translation
Mosaicking
Warping
Inspection of metadata
Raster calculations
Processing of vector data
Understanding GDAL is extremely useful for large-scale GIS work in Python.
PDAL
PDAL (Point Data Abstraction Library) is a library intended for point cloud processing.
PDAL is particularly pertinent in LiDAR and 3D GIS applications that work with LAS and LAZ files.
Some applications of PDAL include:
Filtering
Classification
Ground point selection
Coordinate transformations
Tiling point clouds
Converting LiDAR formats
Creating digital elevation models
PDAL is a good solution for processing large LiDAR data sets using Python workflows.
Laspy
Laspy is a Python package for reading and writing LAS and LAZ point cloud files.
Laspy can be useful when individual attributes of LiDAR need to be accessed, including:
X, Y, Z coordinates
Classification
Intensity
Return number
Time GPS
RGB values
Example:
import laspy
las = laspy.read("pointcloud.las")
x = las.x
y = las.y
z = las.z
print(len(x))
Laspy is useful when developers need to work directly with individual attributes of LiDAR point clouds in Python.
Python GIS Packages for Remote Sensing
Since remote sensing involves many packages because of multidimensional raster datasets in satellite/aerial images, here are some common remote sensing Python libraries:
This list can be used for workflows like computing NDVI, land cover classification, agricultural crop monitoring, flood mapping, change detection, etc.
Python GIS Packages for LiDAR
LiDAR involves the use of special tools that process point clouds.
The commonly used tools for LiDAR data that work with Python are:
These tools may be integrated to develop processes for point cloud classification, terrain modeling, building extraction, forestry analysis, and mapping.
Python supports one of the most developed ecosystems when it comes to GIS mapping, spatial analysis, remote sensing, LiDAR, and geospatial data science in general.
Vector data requires GeoPandas and Shapely. In the case of raster, the key packages are Rasterio, Xarray, and rioxarray. Coordinate system transformations can be accomplished via PyProj, whereas Folium, Leafmap, and Cartopy facilitate visualizations.
Rather than choosing one single best GIS Python library, the most successful strategy would be building up a package stack depending on the data, analysis needs, and scope of your project.
Given the appropriate combination of geospatial Python libraries, developers are able to set up efficient, automated, and scalable pipelines for mapping, spatial analysis, remote sensing, LiDAR, GeoAI, and other location intelligence solutions.
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