Pandas Advanced (GroupBy & Pivot Tables) Cheat Sheet

import pandas as pd# Multiple named aggregations per columnsummary = df.groupby("region").agg(    total_sales=("amount", "sum"),    avg_sales=("amount", "mean"),    num_orders=("order_id", "count")).reset_index()# transform() adds group stats back to the original rowsdf["region_avg"] = df.groupby("region")["amount"].transform("mean")# Custom aggregation functiondf.groupby("region")["amount"].agg(lambda x: x.max() - x.min())# groupby multiple keysdf.groupby(["region", "category"])["amount"].sum()

pivot = pd.pivot_table(    df,    values="amount",    index="region",    columns="category",    aggfunc="sum",    fill_value=0,    margins=True,        # adds row/column totals    margins_name="Total")# Multiple aggregation functions at oncepd.pivot_table(df, values="amount", index="region",                aggfunc=["sum", "mean", "count"])

# Reshape long -> widewide = df.pivot(index="date", columns="product", values="sales")# Reshape wide -> longlong = wide.reset_index().melt(id_vars="date", var_name="product", value_name="sales")# Flatten MultiIndex columns from groupby.agggrouped = df.groupby(["region", "category"]).agg({"amount": ["sum", "mean"]})grouped.columns = ["_".join(col) for col in grouped.columns]