5 Pandas Tricks That Let You Work With CSVs Bigger Than Your RAM

Bernd Fischer

5 min read

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Sep 25, 2026

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5 read_csv() techniques that let you process huge CSVs without loading all that data into RAM.

The solution is not abandoning Pandas. It is telling Pandas what your data looks like before loading more than you actually need.

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No additional packages are required beyond Pandas:

uv add pandas

The Sample Data

All examples use the same orders.csv file stored next to main.py. The sample is deliberately tiny so that we can inspect it, but imagine the same structure with 20 million orders.

order_id,customer_id,amount,quantity,status,country,created_at,comment
1001,C1001,129.90,3,paid,DE,2026-08-20 09:15:00,Priority customer
1002,C1002,49.50,7,pending,DE,2026-08-20 10:42:00,Standard delivery
1003,C1003,219.00,2,paid,US,2026-08-20 13:08:00,Express delivery
1004,C1004,89.90,5,cancelled,DE,2026-08-21 08:03:00,Customer cancelled
1005,C1005,310.00,4,paid,US,2026-08-21 09:27:00,Priority customer

In every example, we resolve the CSV relative to the Python file. This makes the script independent of the directory from which it is executed.

from pathlib import Path


CSV_FILE = Path(__file__).parent.resolve() / "orders.csv"

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