I\u2019m working with a fairly large dataset (CSV) (~3 crore \/ 30 million rows). Due to memory and compute limits (I\u2019m currently using Google Colab), I can\u2019t load the entire dataset into memory at once.<\/p>\n
What I\u2019ve done so far:<\/p>\n
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- Randomly sampled ~1 lakh (100k) rows<\/li>\n
- Performed EDA on the sample to understand distributions, correlations, and basic patterns<\/li>\n<\/ul>\n
However, I\u2019m concerned that sampling may lose important data context, especially:<\/p>\n
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- Outliers or rare events<\/li>\n
- Long-tail behavior<\/li>\n
- Rare categories that may not appear in the sample<\/li>\n<\/ul>\n
So I\u2019m considering an alternative approach using pandas chunking:<\/p>\n
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- Read the data with chunksize=1_000_000<\/li>\n
- Define separate functions for:<\/li>\n
- preprocessing<\/li>\n
- EDA\/statistics<\/li>\n
- feature engineering<\/li>\n<\/ul>\n
Apply these functions to each chunk<\/p>\n
Store the processed chunks in a list<\/p>\n
Concatenate everything at the end into a final DataFrame<\/p>\n
My questions:<\/p>\n
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Is this chunk-based approach actually safe and scalable for ~30M rows in pandas?<\/p>\n<\/li>\n
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Which types of preprocessing \/ feature engineering are not safe to do chunk-wise due to missing global context?<\/p>\n<\/li>\n
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If sampling can lose data context, what\u2019s the recommended way to analyze and process such large datasets while still capturing outliers and rare patterns?<\/p>\n<\/li>\n
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Specifically for Google Colab, what are best practices here?<\/p>\n<\/li>\n<\/ol>\n
-Multiple passes over data? -Storing intermediate results to disk (Parquet\/CSV)? -Using Dask\/Polars instead of pandas?<\/p>\n
I\u2019m trying to balance:<\/p>\n
-Limited RAM -Correct statistical behavior -Practical workflows (not enterprise Spark clusters)<\/p>\n
Would love to hear how others handle large datasets like this in Colab or similar constrained environments<\/p>\n<\/div>\n
submitted by \/u\/insidePassenger0 <\/a>
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