Pandas DataFrame head() method returns the first n rows of a DataFrame, allowing developers to inspect the top portion of the dataset quickly. It provides a quick snapshot.

In the above figure, we are fetching the first five rows by default.

Syntax

DataFrame.head(n=5)

Parameters

Name	Description
n (optional, int)	It represents the number of rows to select. The default is 5. It can be a positive or negative number, depending on your requirement.

Default (First 5 Rows)

Let’s display the first five rows of the DataFrame, preserving columns and index.

import pandas as pd

df = pd.DataFrame({
    'Name': ['Krunal', 'Tom', 'Niva', 'Vidisha', 'Harry', 'Maya', 'Bam', 'Chandani'],
    'Gender': ['Male', 'Male', 'Female', 'Female', 'Male', 'Female', 'Male', 'Female']
})

print(df.head())

You can see that it does not modify the row index and columns.

Custom number of rows

To control the number of rows, use the n parameter, which allows you to pass any number, and it will return the DataFrame with that number of rows.

Let’s fetch just the first two rows.

import pandas as pd

df = pd.DataFrame({
    'Name': ['Krunal', 'Tom', 'Niva', 'Vidisha', 'Harry', 'Maya', 'Bam', 'Chandani'],
    'Gender': ['Male', 'Male', 'Female', 'Female', 'Male', 'Female', 'Male', 'Female']
})

print(df.head(2))

Negative n

When you pass a negative integer as an n parameter in df.head(n), it returns all rows except the last n rows of the DataFrame.

For example, if n = -3, it returns the DataFrame except the last three rows.

import pandas as pd

df = pd.DataFrame({
    'Name': ['Krunal', 'Tom', 'Niva', 'Vidisha', 'Harry', 'Maya', 'Bam', 'Chandani'],
    'Gender': ['Male', 'Male', 'Female', 'Female', 'Male', 'Female', 'Male', 'Female']
})

print(df.head(-3))

n is larger than the DataFrame size

What if you pass a number of rows (n) that is larger than the DataFrame size? In other words, if the input DataFrame has a total of 8 rows and you pass the number of rows as 9. Well, in that case, it returns the whole DataFrame in the output.

import pandas as pd

df = pd.DataFrame({
    'Name': ['Krunal', 'Tom', 'Niva', 'Vidisha', 'Harry', 'Maya', 'Bam', 'Chandani'],
    'Gender': ['Male', 'Male', 'Female', 'Female', 'Male', 'Female', 'Male', 'Female']
})

print(df.head(9))

Avoid using very large n values; prefer slicing for better control.

Empty DataFrame handling

In case of an empty DataFrame, you will get an empty DataFrame as a result.

import pandas as pd

empty_df = pd.DataFrame()

print(empty_df.head())

# Output:
# Empty DataFrame
# Columns: []
# Index: []

That’s all!