How to Select Rows & Columns by Name or Index in Pandas Dataframe – Using loc and iloc

When working with labeled data or referencing specific positions in a DataFrame, selecting specific rows and columns from Pandas DataFrame is important. In this article, we’ll focus on pandas functions—loc and iloc—that allow you to select rows and columns either by their labels (names) or their integer positions (indexes).

Let’s see an basic example to understand both methods:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],'Age': [25, 30, 35],'City': ['New York', 'Los Angeles', 'Chicago']}
df = pd.DataFrame(data)

# Using loc (label-based)
result_loc = df.loc[0, 'Name']  # Select value at row 0 and column 'Name'

# Using iloc (position-based)
result_iloc = df.iloc[1, 2]  # Select value at row 1 and column 2

print("Using loc:", result_loc)   
print("Using iloc:", result_iloc)

Output:

Using loc: Alice
Using iloc: Los Angeles

Selecting Rows and Columns Using .loc[] (Label-Based Indexing)

The .loc[] method selects data based on labels (names of rows or columns). It is flexible and supports various operations like selecting single rows/columns, multiple rows/columns, or specific subsets.

Key Features of .loc[]:

• Label-based indexing.
• Can select both rows and columns simultaneously.
• Supports slicing and filtering.

Select a Single Row by Label:

row = df.loc[0]  # Select the first row
print(row)

Output:

Name       Alice
Age           25
City    New York
Name: 0, dtype: object

Select Multiple Rows by Labels:

rows = df.loc[[0, 2]]  # Select rows with index labels 0 and 2
print(rows)

Output:

      Name  Age      City
0    Alice   25  New York
2  Charlie   35   Chicago

Select Specific Rows and Columns:

subset = df.loc[0:1, ['Name', 'City']]  # Select first two rows and specific columns
print(subset)

Output:

    Name         City
0  Alice     New York
1    Bob  Los Angeles

Filter Rows Based on Conditions:

filtered = df.loc[df['Age'] > 25]  # Select rows where Age > 25
print(filtered)

Output:

      Name  Age         City
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

Selecting Rows and Columns Using .iloc[] (Integer-Position Based Indexing)

The .iloc[] method selects data based on integer positions (index numbers). It is particularly useful when you don’t know the labels but know the positions.

Key Features of .iloc[]:

• Uses integer positions (0, 1, 2, …) to index rows and columns.
• Just like .loc[], you can pass a range or a list of indices.
• Supports slicing, similar to Python lists.
• Unlike .loc[], it is exclusive when indexing ranges, meaning that the end index is excluded.

Select a Single Row by Position:

row = df.iloc[1]  # Select the second row (index position = 1)
print(row)

Output:

Name            Bob
Age              30
City    Los Angeles
Name: 1, dtype: object

Select Multiple Rows by Positions:

rows = df.iloc[[0, 2]]  # Select first and third rows by position
print(rows)

Output:

      Name  Age      City
0    Alice   25  New York
2  Charlie   35   Chicago

Select Specific Rows and Columns:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],'Age': [25, 30, 35],'City': ['New York', 'Los Angeles', 'Chicago']}
df = pd.DataFrame(data)
subset=df.iloc[[0,2],[1]]
print(subset)

Output:

   Age
0   25
2   35

Key Differences Between loc and iloc

Although both loc and iloc allow row and column selection, they differ in how they handle indexes:

Use loc when:

• You have specific labels for rows or columns and want to work with them directly.
• Your dataset has non-numeric indexes (e.g., strings, datetime).

Use iloc when:

• You are working with numeric positions and don’t need to reference row/column names directly.
• You want to select data based on positions within the structure.

Both loc and iloc are incredibly useful tools for selecting specific data in a Pandas DataFrame. The key difference is whether you’re selecting by label (loc) or index position (iloc). Understanding how and when to use these methods is essential for efficient data manipulation.

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Article Tags :

• AI-ML-DS
• Pandas
• Python
• AI-ML-DS With Python
• Python pandas-dataFrame
• Python-pandas

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