28. How to Merge and Join Tables in pandas for Blinkit Data

28.5 Three-Table Path: orders ⨝ stores ⨝ riders

By Ravindra Bagale, trainer · LinkedIn · Last updated: 24 October 2026

Why. Delivery SLA stories need store city and rider name. Chain merges carefully — one validated step at a time.

What. Merge orders→stores, check length, then merge→riders on rider_id. Prefer left joins from the fact table.

How.

Worked example A — Chain.

import pandas as pd

orders = pd.DataFrame({
    "order_id": ["BLK-1", "BLK-2", "BLK-3"],
    "store_id": ["PUN-01", "NSK-01", "PUN-01"],
    "rider_id": ["R-9", "R-2", "R-9"],
    "amount_inr": [100, 90, 80],
})
stores = pd.DataFrame({
    "store_id": ["PUN-01", "NSK-01"],
    "city": ["Pune", "Nashik"],
})
riders = pd.DataFrame({
    "rider_id": ["R-9", "R-2"],
    "rider_name": ["Salman", "Zoya"],
})

o_s = orders.merge(stores, on="store_id", how="left")
assert len(o_s) == len(orders)
o_sr = o_s.merge(riders, on="rider_id", how="left")
assert len(o_sr) == len(orders)
print(o_sr)

Worked example B — GMV by city and rider.

g = (
    o_sr.groupby(["city", "rider_name"], as_index=False)["amount_inr"]
    .sum()
    .sort_values("amount_inr", ascending=False)
)
print(g)

Worked example C — Stop if length jumps.

If len grows, halt and run the duplicate-key check — do not “fix with a chart”.