Python 3.11: How to optimize performance when reading large CSV files with pandas?
I'm experimenting with I'm migrating some code and I'm currently working on a Python 3.11 project where I need to process large CSV files (up to several gigabytes) using `pandas`. I've noticed that reading these files into a DataFrame is quite slow, which affects the overall performance of my application. I am using the following code snippet to read a CSV file: ```python import pandas as pd def read_large_csv(file_path): df = pd.read_csv(file_path) return df large_file = 'path/to/large_file.csv' df = read_large_csv(large_file) ``` When I run this, it takes a significant amount of time, sometimes over a minute, to load the data. I’ve tried the following approaches to improve performance: - Specifying the `dtype` of columns explicitly to reduce memory usage. - Using the `chunksize` parameter to read the file in smaller chunks, which improved memory consumption but not the overall speed. - Leveraging the `usecols` parameter to only read specific columns I need. Despite these efforts, the performance is still not where I need it to be. Additionally, I get warnings about `DtypeWarning` for columns with mixed types. I would like to know best practices or techniques to significantly speed up the process of reading large CSV files in pandas. Any insights or recommendations would be greatly appreciated! Could someone point me to the right documentation? What am I doing wrong?