When I started my reforestation suitability model, I had a clear goal: identify the best areas for replanting trees using real-world environmental data. What I didn’t expect was how much I’d learn about using Python with ArcGIS along the way. As someone with a GIS background but only basic Python experience, this project pushed me into new territory. And honestly? I stumbled a lot. But those struggles became some of the most valuable parts of the journey.
In this post, I want to go beyond the final map and share some of the biggest lessons I learned while combining Python scripting with ArcGIS workflows. Whether you’re just starting out or looking to improve your skills, I hope these experiences help you avoid a few potholes I fell into.
1. File Paths Will Break Everything (If You Let Them)
One of the first hurdles I ran into was how sensitive Python and ArcGIS are to file paths:
- Backslashes (\) from copying & pasting Windows filepaths constantly triggered syntax errors unless I doubled them or used raw strings (e.g.,
r"C:\folder\file.tif"). - OneDrive sync folders caused crashes and slowdowns, especially when ArcGIS tried to access files being backed up in real time.
- Some tools failed silently or produced cryptic errors unless I moved everything to a clean, local directory like
C:/GIS Reforestation Model Data/.
Lesson: Keep your datasets in a dedicated local folder and always use forward slashes (/) or raw strings for file paths.
2. Coordinate Reference Systems (CRS) Are Not Optional
A big surprise was how important consistent coordinate systems are when working with multiple datasets.
ArcGIS will look like it’s handling different CRSs just fine in the map view—but Python scripts won’t be so forgiving. I had to reproject:
- Vector shapefiles with GeoPandas
- Rasters using Rasterio’s
reproject()method
Without that, clipping, merging, or overlaying rasters would fail or silently produce empty outputs.
Lesson: Always check and match CRS across your inputs before processing. In my case, the HU4 watershed raster used NAD83 / Conus Albers, so I reprojected everything to match that.
3. Rasterio Is Powerful, But Has a Learning Curve
I had originally planned to use GDAL for slope calculation and raster math, but ran into permission errors and installation issues. Switching to Rasterio turned out to be a great choice—it’s pure Python and worked seamlessly with NumPy.
Highlights:
- Merging DEMs and generating slope
- Clipping rasters to polygon boundaries
- Reclassifying raster values on a 0–1 scale
- Performing weighted overlay with NumPy arrays
Lesson: Rasterio + NumPy is a great toolkit for Python-based raster analysis—but you need to understand how array dimensions, metadata, and transforms interact.
4. Errors Are Your Friends (Eventually)
Here are just a few of the errors I encountered:
ValueError: operands could not be broadcast together→ My rasters weren’t aligned in shaperasterio.errors.WindowError: Intersection is empty→ My clip polygon didn’t overlap the rasterAttributeError: 'NoneType' object has no attribute 'GetSubDatasets'→ HDF file was unreadable
In each case, I had to stop, research the error, inspect my data, and often go back a few steps to clean or reprocess files.
Lesson: Don’t panic when something breaks. Use print() statements to debug shapes and data types, and check metadata like CRS, bounds, and pixel size before jumping to conclusions.
5. Not Everything Can Be Automated—and That’s Okay
At one point, I tried downloading over 900 individual .tif files from NASA’s AppEEARS tool, thinking I could just zip them all and run a batch script. Turns out, the website didn’t offer bulk download for all products, and I had to change course.
Another time, ArcGIS refused to clip a raster because I forgot to save edits after digitizing the boundary polygon.
Lesson: Sometimes the simplest solution is the best one. Not every step has to be automated. Be flexible, and don’t be afraid to switch to a manual process when it makes sense.
Final Thoughts
This project taught me that learning Python with ArcGIS isn’t about mastering everything overnight. It’s about incremental progress, trial and error, and learning how to ask the right questions when something breaks.
There’s still so much more I want to explore—web mapping, automated reports, even machine learning—but for now, I’m proud of the foundation I’ve built. And if you’re just starting out with Python and GIS, I promise: the struggles are worth it.
Stick with it, and you’ll be amazed at what you can create.