Read CSV and Parquet in Polars — Python Tutorial

About this lesson

Polars is a dataframe library written in Rust, backed by Apache Arrow. Same shape as pandas, ten times faster on real-world workloads, and a query API that reads more like SQL than chained method calls. In this episode you install Polars, load a snapshot of fourteen tickers from a parquet file, and inspect the frame with the four methods you will use every day: head, tail, schema, describe. The whole script is four lines. Source code: https://github.com/GoCelesteAI/polars-for-finance This is episode one of the Polars for Finance series — same dataset universe as Pandas for Finance, side-by-side rewrites of every idiom analysts already know. By the end of the series you will be writing groupby aggregates, joins, rolling windows, resamples, and lazy queries that scan ten million rows from disk without loading the file. What You'll Build: - A working Python virtualenv with polars, pyarrow, and yfinance installed in one pip command. - A four-line read_prices.py script that loads fourteen tickers of daily OHLCV from a parquet file and prints head, shape, and schema. - A first feel for Polars's DataFrame: dtypes printed inline on every output, columnar storage, multithreaded reads by default. - The CSV reader path with explicit schema pinning for production-grade ingestion. - A side-by-side on parquet vs CSV: size, read time, column-prune support, and why parquet is the right disk format for finance work. Timestamps: 0:00 - Intro — Polars for Finance starts here 0:14 - Preview — read parquet, inspect, why Polars 0:50 - Install polars and pyarrow 1:06 - Write read_prices.py in nvim 1:38 - pl.read_parquet is the workhorse 2:08 - Save, cat, run 2:26 - Twenty eight thousand rows in milliseconds 2:50 - Schema, shape, describe 3:18 - End screen — recap and what's next Key Takeaways: 1. Polars is the Python dataframe library written in Rust. The speed comes from columnar storage, multi-threaded reads by default, and an Apache Arrow memory layout. The ergonomics come from t

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