loc vs iloc in Pandas: Label-Based vs Integer-Based Indexing

Question

Given a DataFrame with custom string index labels (e.g. ['c1', 'c2', 'c3']), demonstrate how to select rows 'c1' through 'c2' using `.loc`, and the first 2 rows using `.iloc`. Explain why .loc includes the endpoint whereas .iloc excludes it.

Accepted Answer

Optimal Solution:
import pandas as pd

# Label-based slice (.loc is INCLUSIVE of endpoint)
loc_subset = df.loc['c1':'c2', ['revenue']]

# Position-based slice (.iloc is EXCLUSIVE of endpoint like Python lists)
iloc_subset = df.iloc[0:2, [0]]

Technical Explanation:
`.loc` is label-based: slicing `'c1':'c2'` includes `'c2'`. `.iloc` is 0-indexed integer position-based: slicing `0:2` extracts indices 0 and 1, excluding 2.

index	revenue	orders
c1	12000	45
c2	18500	72
c3	9400	31