How to Select First n Rows in pandas DataFrame

To select the first n rows in a pandas DataFrame, use the head(n) method. This returns a new DataFrame containing only the first n rows, making it easy to preview or work with a subset of your data.

Syntax

The basic syntax to select the first n rows from a pandas DataFrame is:

• DataFrame.head(n): Returns the first n rows.

Here, DataFrame is your data table, and n is the number of rows you want to select.

python

DataFrame.head(n)

Example

This example shows how to create a simple DataFrame and select the first 3 rows using head(3).

python

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
        'Age': [25, 30, 35, 40, 45]}
df = pd.DataFrame(data)

first_three_rows = df.head(3)
print(first_three_rows)

Output

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   35

Common Pitfalls

Some common mistakes when selecting the first n rows include:

• Using slicing like df[:n] works but is less clear than head(n).
• Forgetting that head() returns a new DataFrame and does not modify the original.
• Passing a negative number to head() returns all rows except the last abs(n), which can be confusing.

python

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)

# Negative number returns all but last rows
print(df.head(-1))

# Positive number to get first n rows
print(df.head(2))

Output

      Name  Age
0    Alice   25
1      Bob   30

      Name  Age
0    Alice   25
1      Bob   30

Quick Reference

Method	Description	Example
head(n)	Returns first n rows	df.head(5)
iloc[:n]	Select rows by position	df.iloc[:5]
slice [:n]	Python slicing for rows	df[:5]

Key Takeaways

Use df.head(n) to select the first n rows clearly and efficiently.

head(n) returns a new DataFrame and does not change the original data.

Avoid passing negative numbers to head() unless you want all but last rows.

You can also use df.iloc[:n] or df[:n] but head(n) is more readable.

Selecting first n rows is useful for quick data previews and analysis.