How do you debug and test your Python code for GIS projects?

1 Use an IDE

An integrated development environment (IDE) is a software application that provides a comprehensive set of features and tools for writing, editing, running, and debugging Python code. IDE's can be beneficial for GIS projects in many ways, such as providing syntax highlighting, code completion, and formatting to help write clean code. Debugging tools like breakpoints and watch variables can help locate errors while testing tools like unit tests and code coverage can help verify and improve the quality and performance of your code. Project management tools like version control, file explorer, and code snippets can also assist in organizing and managing code files and resources. Popular IDE's for Python and GIS include PyCharm, Visual Studio Code, Spyder, and ArcGIS Pro.

2 Follow coding standards

Coding standards are a set of rules and guidelines that define how to write and format Python code. Adhering to coding standards can help to make your code more readable, maintainable, and compatible. Examples of coding standards for Python include PEP 8, the official style guide for Python code, PEP 257, the official style guide for Python docstrings, and ArcPy Coding Standards, best practices for writing Python code for ArcGIS. You can use tools such as pylint, flake8, or black to check and format your code according to the coding standards.

3 Use GIS-specific libraries

Python offers a vast selection of libraries and modules for GIS projects, such as ArcPy, GDAL/OGR, Shapely, and GeoPandas. ArcPy is the official Python module for ArcGIS and provides access to geoprocessing tools, data analysis functions, map automation scripts, and spatial data types. GDAL/OGR is a library for reading and writing various raster and vector geospatial data formats. Shapely is a library for manipulating and analyzing geometric objects. Lastly, GeoPandas is a library for working with geospatial data in pandas data frames. You can use tools such as pip, conda, or ArcGIS Pro Package Manager to install and manage the libraries and their dependencies.

4 Test your code with sample data

Testing your code with sample data is an essential step to guarantee that your code runs properly and produces the desired output. It can help you verify the logic and functionality of your code, such as loops, conditions, calculations, and geoprocessing tools. Additionally, it can be used to identify and fix any errors or bugs in your code, as well as evaluate the performance and efficiency of your code. Furthermore, it allows you to compare the results of your code with the expected results. Tools such as pytest, unittest, or ArcGIS Pro Test Runner can be used to create and run tests for your code.

5 Document your code and output

Documenting your code and output is an essential practice for communicating the purpose, usage, and results of your code to yourself and others. It can help explain the rationale and assumptions behind your code, provide instructions on how to run and use it, summarize and visualize the results, and cite sources used or modified. Tools such as docstrings, comments, markdown cells, or ArcGIS Pro Metadata can be used to document your code and output.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?