How to Prepare a Parquet File in Python — Tutorial

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. 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 — polars write_parquet 2:48 - Save and run 3:06 - Size comparison — CSV vs three parquets 3:34 - End screen — recap and next Key Takeaways: 1. Parquet is a columnar binary format. Columns of similar values compress well, dates and integers stay typed across save and reload, and you can read only the columns you ask for. For any CSV that gets read more than once, parquet is the next stop. The conversion in Python is always two lines: read the CSV, call a write method. 2. Pandas is the path of least resistance. df.to_parquet writes a snappy-compressed file via pyarrow under the hood. Most analysts already have pandas installed; this is the right default for one-off conversions and the conversion you will use ninety percent of the time in real work. 3. PyArrow is the lowest layer and gives you full control. pq.write_table accepts arguments for compression codec, compression level, row-group size, and dictionary encoding. Reach for it when you need a specific output shape — archival files in zstd, large row groups for parallel reads, or schema overrides. 4. Polars writes the smallest file by default. Same input, same dataset, polars produces a parquet about half the size of pandas's or pyarrow's. The reason is smarter default column encodings: dictionary encoding for repeated string values, run-length encoding for sorted columns. Same two-line API, smaller output, faster I/O. The right choice for any production pipeline that writes parquet at scale. 5. Schema preservation is the quiet killer feature. CSV reload infers types from the first rows of the file, which means a single decimal value can flip an Int64 column to Float64 on the next reload. Parquet stores the schema in the file. Datetime columns stay Datetime. String columns stay String. No more parse_dates hooks, no more dtype= arguments, no more silent type drift in your pipeline. This channel is run by Claude AI. Tutorials AI-produced; reviewed and published by Codegiz. Source code at codegiz.com. #Python #Parquet #PyArrow #Pandas #Polars #DataEngineering #DataAnalytics #PythonTutorial #CSV #LearnPython --- Generated by Claude AI · part of the Common Questions in Python series