Pandas - lesson 6 | HackIndia Skills

Pandas - lesson 6 | HackIndia Skills | HackIndia

Example

Filter rows

This example keeps completed UPI orders of ₹100 or more. Notice the brackets around each condition. They matter when you combine conditions.

import pandas as pd

orders = pd.read_csv("data/raw/orders.csv")

paid_big_orders = orders[
    (orders["status"] == "paid") &
    (orders["payment_mode"] == "UPI") &
    (orders["amount"] >= 100)
]

print(paid_big_orders.shape)
print(paid_big_orders.head())

Example

Group orders

Here you calculate order count, total revenue, average amount, and highest order value for each city. This kind of table is useful in your final repo because it proves you can turn raw data into project-ready data.

city_summary = (
    paid_big_orders
    .groupby("city")
    .agg(
        orders_count=("order_id", "count"),
        total_revenue=("amount", "sum"),
        avg_order_value=("amount", "mean"),
        max_order_value=("amount", "max")
    )
    .reset_index()
    .sort_values("total_revenue", ascending=False)
)

print(city_summary.head(10))

city_summary.to_csv("data/clean/city_summary.csv", index=False)

Common mistake

Avoid chained assignment

Older tutorials often change filtered data in one line after slicing. In pandas 3.0, Copy-on-Write means chained assignment no longer works the way old tutorials expect.

Use a new variable, then assign with .loc when you need to edit rows. This keeps your notebook or script easier to debug.

# Avoid patterns like this from old tutorials:
# orders[orders["status"] == "paid"]["amount"] = 0

paid_orders = orders[orders["status"] == "paid"].copy()
paid_orders.loc[:, "amount_after_fee"] = paid_orders["amount"] - 2

Filter Group and Summarise

One idea

When to filter

Filter rows

Build a filter

Group and summarise

Group orders

Avoid chained assignment

Use AI carefully

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