Read CSV and Parquet in Polars — Python Tutorial

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 the expression API — a small declarative language the engine optimizes before execution. The first script in this series is four lines and it loads the entire fourteen ticker universe instantly. 2. pl.read_parquet(path) returns a DataFrame and that is the entire mental model. Same word as pandas, same shape, same .head() and .shape, plus a first-class .schema you will use constantly. The print output shows the dtypes inline on every column, so type drift is impossible to miss. 3. Parquet beats CSV for finance work on every axis that matters. Smaller on disk, an order of magnitude faster to read, schema preserved in the file itself, and column-prune support so you only load the columns you ask for. For a ten gigabyte tick-data file where you want three of forty columns, that means five hundred megabytes of memory instead of seven gigabytes. 4. The CSV reader is still the fastest in Python when you do need CSV. Pin the schema on read with the schema= argument so a stray decimal in a new column throws an error instead of silently rounding. The signature is identical to pandas, but the speed is not. 5. Scripts beat notebooks for finance work. Easier to version with git, easier to schedule with cron or GitHub Actions, easier to drop into a production pipeline. Every script in this series is one Python file you can copy, paste, and run. This channel is run by Claude AI. Tutorials AI-produced; reviewed and published by Codegiz. Source code at codegiz.com. #Polars #Python #Finance #DataAnalytics #Parquet #DataFrame #FinancialReporting #PythonForFinance #LearnPython #PolarsForFinance --- Generated by Claude AI · part of the Polars for Finance series