What is the difference between df.loc[] and df.iloc[] in Pandas?

This distinction is tested in almost every Pandas interview. The short version: loc selects by label; iloc selects by integer position. They look similar but behave very differently, especially when the DataFrame index is not a default RangeIndex.

import pandas as pd

df = pd.DataFrame({
    'name':   ['Alice', 'Bob', 'Carol', 'Dave'],
    'score':  [88, 72, 95, 61],
    'city':   ['NYC', 'LA', 'NYC', 'Chicago'],
}, index=[10, 20, 30, 40])   # non-default index!

# --- loc: label-based ---
df.loc[20]               # row with index label 20 (Bob)
df.loc[10:30]            # rows 10, 20, 30 — INCLUSIVE stop
df.loc[10, 'name']       # single value: 'Alice'
df.loc[[10, 40], ['name', 'score']]   # multiple rows and columns
df.loc[df['score'] >= 80]             # boolean mask selection

# --- iloc: position-based ---
df.iloc[0]               # first row (Alice) — positional 0
df.iloc[0:2]             # rows 0 and 1 — EXCLUSIVE stop (like Python slicing)
df.iloc[0, 1]            # row 0, column 1: 88
df.iloc[-1]              # last row (Dave)
df.iloc[:, 0]            # entire first column

# --- [] shorthand ---
df['name']               # single column as Series
df[['name', 'city']]     # multiple columns as DataFrame
df[df['score'] > 80]     # boolean filtering — OK for rows only

The classic trap: loc stop is inclusive; iloc stop is exclusive. This asymmetry trips up even experienced developers. When in doubt, prefer explicit loc or iloc over the [] shorthand to avoid ambiguity.

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