What are the best ways to optimize ArcPy script performance?

Pay attention to the way in which your data structure influences code. I teach algorithms and data structures, and the most obvious form of this problem in a spatial context is with networks. You could use an adjacency matrix approach which would make it the most efficient to find two things connected or not, but you pay the piper in terms of storage and fanning out. You could use an edgelist approach linking node to node. To find things are not connected, you have to go through the entire list and to find they are connected you have to go as far as necessary. You are paying the piper for the question, but gaining in storage. A stemmed edgelist reduces the penalty. You can then always maintain a node table with direct attributes.

Selecting the appropriate data format is crucial for enhancing ArcPy script efficiency. While shapefiles offer simplicity and broad compatibility, their limitations necessitate careful use. File geodatabases, despite their larger size, provide superior functionality and performance for complex GIS tasks. For processes requiring rapid access and manipulation, consider leveraging in-memory workspaces to significantly reduce read/write times by utilizing RAM. This strategy optimizes script performance, especially for scripts with intensive data processing tasks, ensuring smoother and faster execution of spatial analysis workflows.

Leveraging arcpy.da cursors enhances ArcPy script efficiency through faster data manipulation and broader functionality compared to older cursors. They enable efficient batch processing and sophisticated operations like SQL queries and spatial references integration. By adopting arcpy.da cursors for tasks such as mass updates or spatial analyses, you significantly streamline script runtimes, optimizing the performance of spatial data processing tasks. This approach not only saves time but also improves the script's overall reliability and capacity for handling complex spatial datasets.

Built-in functions and methods have gone through a lot of testing and are very much optimized. That's a huge advantage. The downside of that is that they may not exactly meet your needs. Wherever possible, it is wise to use the methods that exist because they have been designed typically with that in mind.

Utilizing ArcPy's built-in functions and methods for tasks like feature creation, field calculation, and data projection ensures optimal performance and stability. These elements are tailored for ArcGIS Pro, offering error handling superior to custom solutions. Leveraging these ready-made tools not only conserves development time but also enhances script efficiency and reliability. Prioritize these built-in capabilities to streamline your spatial analysis workflows, focusing your efforts on analysis rather than basic function development.

Implementing multiprocessing in ArcPy scripts accelerates processing by leveraging parallel execution across multiple cores. This technique is especially beneficial for large datasets or tasks that can be independently processed. By dividing the workload and utilizing Python's multiprocessing module, you can significantly cut down execution times, making your spatial analysis more efficient. This approach optimizes resource usage and ensures quicker turnaround times for complex spatial computations, enhancing the scalability and performance of your GIS projects.

Build in calls to system time throughout the process and use those to make line graphs. If you do that, you can get a sense of where the expensive parts of a code are. Significant reductions are had where the time balloons. Things like this can help you identify if an instruction needs to be repeated. I made the mistake once of having the most expensive line of code I could write being done unnecessarily each time I went through a for loop. I didn't discover it until I saw the algorithm crank and crank and crank. When I did that, it sped the code up by a factor of 10! All from moving one line...

Optimizing ArcPy scripts involves configuring arcpy.env settings to tailor script execution to specific project requirements. Adjusting settings like workspace, overwrite permissions, and output coordinate system streamlines script performance by minimizing redundant operations. Properly set environment variables ensure that spatial analyses are conducted within the correct spatial context, enhancing output data quality and reducing processing time. Leveraging arcpy.env settings effectively serves as a foundational step in spatial analysis, ensuring efficient and accurate results.

The solution may seem a bit out of context. From my experience, I have found that ArcPy is a bit slower than other similar python libraries. For better performance, I use GeoPandas, Shapely and ArcPy together. This process increases the overall performance of my scripts.

The key to optimization is solving problems outside of code. As much as you can be proficient in any programming language, you will not be fluent in it. It's easier to discover the problems in code in natural language (pseudocode) than it is in code.