GroupBy and Aggregate in Polars — Python Tutorial

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 per group 0:18 - Preview — four statistics per ticker in one call 0:54 - Open groupby_agg.py in nvim 1:14 - Compute daily returns first with .over 1:38 - The four-element .agg list 2:08 - Why .alias matters for every aggregation 2:30 - Save and run 2:48 - Fourteen rows, four statistics 3:14 - End screen — recap and what's next Key Takeaways: 1. df.group_by Ticker dot agg with a list of expressions is the canonical multi-aggregation shape in Polars. The list is a small declarative program the engine fuses and runs in a single pass over the data. Every expression in the list becomes one output column. The result has one row per group and one column per expression, plus the group key itself. 2. Always .alias every aggregate. Without an alias, Polars defaults to the source column name, which collides the moment you compute two aggregations on the same column. Read the .alias as the SQL AS keyword. Stable names make downstream code reliable and pipeline consumers happier. 3. The seven aggregations cover almost everything an analyst needs. Mean, std, min, max for central tendency and spread. Quantile for percentiles. Count and len for row counts. Sum for additive measures. Each is one method on the expression. Pick from the family; combine in the same .agg list. 4. Polars supports filter-inside-agg in a way pandas struggles with. pl.col Volume dot filter pl.col Close greater than one hundred dot mean computes the mean volume only on rows where Close exceeded one hundred. Pandas needs groupby plus apply with a lambda for this; Polars stays in one chained expression and runs ten times faster. 5. group_by plus agg pairs with .over from Episode 3 as the two cardinality moves in Polars. Use .over to add a per-group column to every row. Use group_by plus agg to reduce to one row per group. Compute the .over column first, then group_by the result if you need both — the engine optimizes the combined query into a single pass. This channel is run by Claude AI. Tutorials AI-produced; reviewed and published by Codegiz. Source code at codegiz.com. #Polars #Python #Finance #DataAnalytics #GroupBy #Aggregation #DataFrame #PythonForFinance #PolarsForFinance #SQL --- Generated by Claude AI · part of the Polars for Finance series