GroupBy and Aggregate in Polars — Python Tutorial

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Group by and aggregate is the operation that turns a long stack of rows into a per-entity summary. In Polars: df.group_by Ticker dot agg, with a list of expressions. One call, fourteen tickers, four statistics per ticker. The result is the per-symbol-summary view that every analyst builds at some point, in one expression instead of a Python loop. Source code: https://github.com/GoCelesteAI/polars-for-finance This episode pairs with Episode 3's .over Ticker window function. Where .over keeps the row count and adds a column, group_by plus agg reduces the frame to one row per group. Between the two cardinality moves you cover almost every analyst pipeline. Today the demo builds: mean close, max volume, daily return standard deviation, and the trading day count, all per ticker, in one with_columns chained into group_by chained into agg. What You'll Build: - groupby_agg.py — compute daily returns first with the over Ticker pattern, then collapse to one row per ticker with group_by plus a four-element agg list. Output is the fourteen by five summary frame. - The .agg list — pl.col Close dot mean, pl.col Volume dot max, pl.col daily_ret dot std, pl.col daily_ret dot count. Each expression becomes one column in the result. Use .alias to name the output stably. - The seven aggregations every analyst needs — mean, std, min, max, quantile, count, sum. All are methods on the expression; pick from the same family. - The filter-inside-agg idiom that pandas struggles with — pl.col Volume dot filter pl.col Close greater than one hundred dot mean. Conditional aggregation in one expression. - The maintain_order equals True knob — trade a small amount of speed for deterministic output ordering, critical when feeding downstream reports or tests. - The decision tree between .over (windowed, same row count) and .group_by plus .agg (aggregation, fewer rows). When to use which is the analyst's most-used judgment call. Timestamps: 0:00 - Intro — group_by collapses the frame to one row

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