What is the difference between .loc and .iloc?

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In Pandas, .loc[] and .iloc[] are used to access rows and columns from a DataFrame, but they differ in how they reference the data — by label or by position.

🔹 .loc[] – Label-based indexing

Access data using row and column labels.

Includes both start and end labels when slicing.

Syntax:

df.loc[row_label, column_label]

Example:

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]}

df = pd.DataFrame(data, index=['a', 'b'])

print(df.loc['a'])       # Access row with label 'a'

print(df.loc['a', 'Age'])  # Output: 25

🔹 .iloc[] – Integer position-based indexing

Access data using zero-based integer positions.

Excludes the end index when slicing.

Syntax:

df.iloc[row_index, column_index]

Example:

print(df.iloc[0])         # First row

print(df.iloc[0, 1])      # Output: 25 (first row, second column)

🔁 Key Differences:

Feature .loc[] .iloc[]

Index type Labels (names) Integer positions

Slice behavior Inclusive Exclusive

Use case Named rows/columns Numeric indexing

📌 Summary:

Use .loc[] when selecting by row/column names.

Use .iloc[] when selecting by numerical positions.

Both are powerful tools for data selection and slicing in Pandas.

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