During this course, we will mainly focus on geographic Python, such as Shapely, GeoPandas, Rasterio, and GeoJSON.
Python GeoSpatial
PythonPython
Python GeoSpatial
Geo-information technology is transforming how we explore, plan, and make decisions about our world. Whether it’s climate analysis, urban development, logistics, or crisis response, spatial data helps us understand patterns and act more effectively. Python, known for its simplicity and power, plays a major role in this transformation.
Many top GIS tools—such as QGIS and ArcGIS—use Python to automate tasks, run analyses, and build custom workflows. With libraries like GeoPandas, Shapely, and Rasterio, you can process and visualize geodata using clean, readable code. Python lets you calculate distances, perform spatial joins, clip data, reproject coordinate systems, and more—without expensive software or steep learning curves.
This course is ideal for anyone interested in working with geographic data using Python. You don’t need to be a developer or GIS expert. We guide you through core concepts with real-world examples and hands-on exercises. By the end, you’ll be ready to apply Python to your own geospatial projects, whatever your background.
The Python Basics course is a useful foundation, but no prior experience with geographic data is needed to get started.
What will you learn
You’ll start by exploring Python’s core geospatial libraries. Shapely helps you work with points, lines, and polygons—allowing you to buffer, intersect, and merge geometries. GeoPandas extends pandas with spatial capabilities, so you can manage geodata using familiar data structures. You’ll also work with Rasterio to handle raster files, such as satellite images or elevation grids.
Next, you’ll learn how to set up your environment, load data from various formats, and clean and explore it. You’ll perform spatial analysis by filtering features, joining datasets, and transforming coordinate systems to ensure alignment.
A major focus of the course is map creation and data visualization. Using Python, you’ll build interactive maps that clearly show spatial patterns. Whether you’re analyzing urban growth, environmental changes, or transport routes, you’ll learn to present your insights effectively.
Throughout the course, you’ll work with both vector and raster data—giving you a strong, balanced foundation in Python-based geospatial analysis.
Why choose this course
This course is built for practical results. You won’t just study how libraries work—you’ll learn how to solve real problems with them. Whether you’re entering the GIS field, enhancing your programming skills, or applying geospatial analysis to your current role, this course will help you achieve your goals.
- Learn from experienced instructors with deep knowledge of Python and GIS
- Apply your skills with real-world, hands-on exercises
- Use cutting-edge open-source tools like Shapely, GeoPandas, and Rasterio
- Receive personal support in a flexible, welcoming learning environment
Course structure
Day 1
We begin with the foundations of spatial data in Python. You’ll be introduced to the key libraries—Shapely for working with geometric shapes, and GeoPandas for managing and analyzing vector data. You’ll learn how to load data from common formats like GeoJSON and shapefiles, explore its structure, and perform essential operations like clipping, buffering, and merging geometries.
We’ll also focus on understanding spatial relationships—such as identifying overlaps, intersections, and proximity between features. By the end of the day, you’ll have a working understanding of how spatial data behaves and how to manipulate it effectively using Python.
Day 2
On the second day, we dive deeper. You’ll learn how to perform spatial joins to combine datasets based on location and work with raster data using Rasterio—loading imagery, querying pixel values, and extracting data from specific areas.
Then we shift focus to map creation. You’ll learn how to visualize your results, apply styling, and generate clear, insightful maps using Python-based tools. Whether you’re mapping environmental changes or visualizing infrastructure, you’ll gain practical skills to turn your analysis into engaging visuals.
By the end of the course, you’ll have built a complete geospatial workflow—from loading and analyzing geographic data to creating professional-quality visual outputs ready to share or present.
Learning Outcomes
- Work confidently with Python libraries like Shapely, GeoPandas, and Rasterio
- Load, analyze, and visualize both vector and raster geospatial data
- Perform spatial operations including joins, filtering, and clipping
- Create clear, informative visualizations of geodata using Python
More Information?
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
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Frequently Asked Questions about Python GeoSpatial
In our 2-day training you will learn to process spatial data in Python, with modules such as Shapely and Geopandas, and perform spatial analyzes and data visualization.
Basic knowledge of Python is recommended, but not strictly necessary. Our course is designed for different experience levels.
Yes, we use GIS software such as ArcGIS and QGIS for practical skills in spatial data analysis.
We handle vector and raster data, and learn to manipulate and analyze them with Python and geographic modules.
Yes, we offer traineeships and study opportunities for starters in the geosector, in addition to our courses.
Definitely, our course is excellent for reskillers who want to gain practical skills in geospatial programming with Python.
Yes, we offer the course as in-company training, adapted to the specific needs of your company.