Q1. You have sales data with columns ''region'', ''product'', ''sales''. Group by region and compute total sales per region.
grouped = df.groupby(''region'')[''sales''].sum()
# .mean(), .count(), .agg() also workdf.groupby(''region'')[''sales''].agg([''sum'',''mean'',''count'']).Q2. 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() # pivot-like tablepivot_table: pd.pivot_table(df, values=''sales'', index=''region'', columns=''product'', aggfunc=''mean'').Q3. 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'']]Q4. 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''
}).agg(sales_sum=(''sales'',''sum'')).Q5. 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