How to Prepare a Parquet File in Python — Tutorial

About this lesson

Parquet is the columnar binary format that compresses three to ten times smaller than CSV, reads faster, and preserves the schema in the file. If you searched for "how to prepare a parquet file in Python", you probably hit a CSV that is too slow, too big, or losing its types every time you reload it. Parquet fixes all three. Source code: https://github.com/GoCelesteAI/prepare-parquet-file This tutorial shows the two-line conversion in each of the three Python libraries that matter: pandas, pyarrow, and polars. Same dataset, same operation, side-by-side. On a fourteen-ticker, twenty eight thousand row stock-price CSV, pandas and pyarrow each produce a one point one three megabyte parquet file; polars produces a five hundred eighty kilobyte file from the same input — the writer's column encodings are smarter by default. You will see the size comparison on disk, the schema-preserved-on-read demo, and a quick tour of the compression codecs worth knowing. What You'll Build: - A working Python venv with pandas, pyarrow, and polars installed in one pip command. - prepare_parquet.py — read prices.csv, write three parquet files (one per library), and print the size comparison so you can see the three to six times compression for yourself. - The two-line idiom in each library — pandas df.to_parquet, pyarrow pq.write_table, polars df.write_parquet. Pick whichever library fits the rest of your pipeline. - The schema-preserved demo — CSV reload turns dates into strings; parquet reload keeps them as Datetime. This is the quiet killer feature for any pipeline that hits the same file twice. - A reference table of the five compression codecs — snappy, zstd, gzip, lz4, brotli — and when to reach for each one. Timestamps: 0:00 - Intro — why parquet beats CSV 0:18 - Preview — three libraries, two lines each 0:54 - Install pandas, pyarrow, polars 1:08 - Open prepare_parquet.py in nvim 1:24 - Method 1 — pandas df.to_parquet 1:50 - Method 2 — pyarrow pq.write_table 2:20 - Method 3 —

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