Filter Rows and Select Columns in Polars — Python Tutorial

Polars filter and select are the two narrowing primitives every dataframe pipeline opens with. Filter cuts rows, select cuts columns, and together they're eighty percent of what an analyst does before any aggregation runs. The Polars version is built on the expression API — pl.col Close compared to two hundred is a query plan node, not a boolean mask, which is what lets Polars optimize predicates and push them down into the parquet read in Episode 8. Source code: https://github.com/GoCelesteAI/polars-for-finance This is also the episode where the Polars expression idiom appears. df.filter wraps a column comparison, then .select picks three columns by name — reads left to right like SQL. Take the prices, keep rows where Close exceeded two hundred, then keep three columns out of eight. Same dataset as Episode 1 — fourteen tickers, six years of daily OHLCV. We write three queries: AAPL above two hundred, a three-column slim view, and a high-volume-day filter chained into a four-column select. What You'll Build: - filter_select.py — load the cached prices, filter to AAPL closes above two hundred dollars, then build a slim three-column view across all 14 tickers. - The expression pattern: df.filter wrapping two pl.col comparisons joined with & — parenthesize each operand, combine with &, |, ~. - Column-narrow via df.select with a list of column names — same rows, fewer columns, propagates into parquet reads when you go lazy. - The chain idiom: filter then select then filter again — Polars optimizes the order, so write whichever reads cleaner. - is_in for set membership and is_between for numeric or date ranges — the everyday-finance filter helpers. Timestamps: 0:00 - Intro — filter and select, the narrowing primitives 0:18 - Preview — the expression API in one line 0:56 - Open filter_select.py in nvim 1:14 - Filter AAPL closes above 200 dollars 1:38 - Select three columns out of eight 2:00 - Chain filter and select on high-volume days 2:24 - Save and run 2:42 - Three result frames, smaller each time 3:10 - End screen — recap and what's next Key Takeaways: 1. pl.col("X") is an expression — a query plan node, not a value. Pass it to filter or select and Polars compiles it to multi-threaded vectorized code. Compose expressions with &, |, and ~, parenthesizing each operand the same way you do in pandas and numpy. This is the syntax you will use for every Polars operation from here on; learn the grip once and the rest of the library reads as variations on the same pattern. 2. df.filter(expr) returns a new DataFrame with only the matching rows; df.select([cols]) returns a new DataFrame with only the named columns. Both are pure — the original is untouched. Chain them and Polars optimizes the order behind the scenes, so write whichever reads cleaner left to right. 3. Select propagates back into the read when you go lazy. pl.read_parquet(path, columns=[...]) reads only the asked-for columns from disk — a 10 GB tick-data file with 40 columns shrinks to 500 megabytes of memory if you only need 3. The Episode 8 lazy mode pushes this further by also pushing filters down. 4. is_in for set membership replaces pandas's isin. is_between for numeric or date ranges replaces the manual two-sided bound pair joined with &. Dates parse as strings automatically — Polars infers the target type. These two helpers cover most everyday-finance filter shapes. 5. Polars is strict about types — comparing a string column to a float raises InvalidOperationError where pandas would silently fall through. The strictness is the point; type drift in finance pipelines is the kind of bug that loses jobs. Polars surfaces it on the line that caused it, not three transformations later when the aggregate looks wrong. This channel is run by Claude AI. Tutorials AI-produced; reviewed and published by Codegiz. Source code at codegiz.com. #Polars #Python #Finance #DataAnalytics #Filter #Select #Expressions #DataFrame #PythonForFinance #PolarsForFinance --- Generated by Claude AI · part of the Polars for Finance series