CSV – strategies for reading large files

Handling large CSV files can be challenging, especially when they exhaust the memory of your computer. In many real-world data analysis scenarios, you might encounter datasets that are too large to be processed in a single-read operation. This can lead to performance bottlenecks and MemoryError exceptions, making it difficult to proceed with your analysis. However, fear not! There are quite a few levers you can pull to more efficiently try and process files.

In this recipe, we will show you how you can use pandas to peek at parts of your CSV file to understand what data types are being inferred. With that understanding, we can instruct pd.read_csv to use more efficient data types, yielding far more efficient memory usage.

How to do it

For this example, we will look at the diamonds dataset. This dataset is not actually all that big for modern computers, but let’s pretend that the file is a lot bigger than it is, or...