Filter Rows and Select Columns in Polars — Python Tutorial

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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 - Thre

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