330 MB File? No Problem! Python’s Memory-Saving CSV Reading Techniques

Python offers several memory-efficient methods for reading large CSV files. One approach involves using a for loop to read the file line-by-line, which prevents loading the entire file into memory at once. This can be done with or without a context manager, which ensures the file is closed after reading. For instance, a 330 MB file was successfully processed using this method. Another technique involves the `fileinput` module, allowing iteration over lines from multiple input streams. For parallel processing, dividing the file into chunks and reading them sequentially can optimize memory usage and speed. The `dask.dataframe` library also provides an efficient way to handle large datasets, supporting operations like grouping and slicing without overwhelming memory. Additionally, custom functions can be written to read files in chunks, adjusting chunk size based on hardware capabilities. These methods ensure that even gigabyte-sized files can be processed without memory issues.

Source: stackoverflow.com

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