Q1. Scenario: You have sales data with columns 'region', 'product', 'sales'. Group by region and compute total sales per region.
grouped = df.groupby(''region'')[''sales''].sum(). Also .mean(), .count(), .agg(). For multiple aggregations: df.groupby(''region'')[''sales''].agg([''sum'',''mean'',''count'']).
Q2. Scenario: Group by both 'region' and 'product', and calculate average sales. Then unstack the result into a table format.
avg_sales = df.groupby([''region'',''product''])[''sales''].mean(); table = avg_sales.unstack(). This creates a pivot-like table. Also can use pivot_table: pd.pivot_table(df, values=''sales'', index=''region'', columns=''product'', aggfunc=''mean'').
Q3. Scenario: Using groupby, get the maximum sales for each region and the product that achieved it. Use .idxmax() to find the product index.
max_idx = df.groupby(''region'')[''sales''].idxmax(); top_products = df.loc[max_idx, [''region'',''product'',''sales'']]. This returns the row(s) with max per group.
Q4. Scenario: Apply multiple aggregation functions to different columns: sum of sales, mean of quantity, and count of orders per region.
df.groupby(''region'').agg({''sales'': ''sum'', ''quantity'': ''mean'', ''order_id'': ''count''}). This is a dictionary mapping columns to functions. Can also use named aggregations: .agg(sales_sum=(''sales'',''sum'')).
Q5. Scenario: Compute the percentage of total sales contributed by each region. Use groupby transform to create a column with total sales per region.
df[''region_total''] = df.groupby(''region'')[''sales''].transform(''sum''); df[''region_pct''] = df[''sales''] / df[''region_total''] * 100. transform returns same shape as original, useful for normalization.