Data Automation & Cleaning Beginner

Split Massive 5GB+ CSV Files into Smaller Row Chunks Without RAM Crash in Python

Stream and split multi-gigabyte CSV files into Excel-compatible 500,000-row chunks in Python using Pandas `chunksize` iterator with near-zero RAM usage.

#pandas

Problem Statement

Excel cannot open CSV files exceeding 1,048,576 rows, and loading a 5GB file with standard `pd.read_csv()` crashes 8GB/16GB laptops. Streaming via `chunksize` processes the file in small memory buffers.

Formula / Core Logic

Iterates over pd.read_csv(filepath, chunksize=500000) and writes each chunk to `part_01.csv`, `part_02.csv`, etc.

Installation

pip install pandas

split-massive-csv-into-smaller-chunks-python.py

import pandas as pd
import os

def split_huge_csv(input_csv, rows_per_file=500000, out_dir='csv_parts'):
    os.makedirs(out_dir, exist_ok=True)
    for idx, chunk in enumerate(pd.read_csv(input_csv, chunksize=rows_per_file), start=1):
        out_path = os.path.join(out_dir, f'part_{idx:03d}.csv')
        chunk.to_csv(out_path, index=False)
        print(f'Saved {out_path} ({len(chunk)} rows)')

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Frequently Asked Questions

Q: Does every split file keep the original column headers?

Yes, `chunk.to_csv(..., index=False)` writes the exact header row at the top of every generated part file.