📊 CSV vs. Parquet vs. Arrow: Which one should you use?
If you work with tabular data in Python, you’ll eventually encounter these three formats. Do you know when to use each one?
CSV — Simple and universal
- Plain text, one row per line
- Compatible with Excel, pandas, databases, any tool
- ❌ No explicit schema: types must be inferred every load
- ❌ Slow with large datasets: parsing text is CPU-intensive
Parquet — For large-scale analytics
- Binary columnar format: stores all values of each column together
- ✅ Excellent compression (Snappy, Gzip): files 5-10x smaller than CSV
- ✅ Reads only the columns you need (column pruning)
- ✅ Schema stored: types preserved without inference
- Ideal for batch processing and millions/billions of rows
Arrow — The in-memory format
- Columnar in RAM: designed for ultra-fast operations
- ✅ Zero-copy reads: access data on disk without loading into memory
- ✅ Vectorized operations on columns
- ✅ Interoperable with pandas, Polars, Spark, PyArrow
- In Hugging Face Datasets, every dataset is internally Arrow
🔍 Explanation in a nutshell
“Columnar” means that instead of storing row by row (name1, age1, city1 / name2, age2, city2), all names are stored together, all ages together. This greatly speeds up queries that only need a few columns and allows similar data to compress better.
📌 Practical rule: CSV for quick experiments → Parquet for storing large tables → Arrow for fast in-memory training.
More information at the link 👇
Also published on LinkedIn.
