You can use the **head()** function to view the first *n* rows of a pandas DataFrame.

This function uses the following basic syntax:

df.head()

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df points assists rebounds 0 25 5 11 1 12 7 8 2 15 7 10 3 14 9 6 4 19 12 6 5 23 9 5 6 25 9 9 7 29 4 12

**Example 1: View First 5 Rows of DataFrame**

By default, the **head()** function displays the first five rows of a DataFrame:

#view first five rows of DataFrame df.head() points assists rebounds 0 25 5 11 1 12 7 8 2 15 7 10 3 14 9 6 4 19 12 6

**Example 2: View First ***n* Rows of DataFrame

*n*Rows of DataFrame

We can use the **n** argument to view the first *n* rows of a pandas DataFrame:

#view first three rows of DataFrame df.head(n=3) points assists rebounds 0 25 5 11 1 12 7 8 2 15 7 10

**Example 3: View First ***n* Rows of Specific Column

*n*Rows of Specific Column

The following code shows how to view the first five rows of a specific column in a DataFrame:

#view first five rows of values in 'points' column df['points'].head() 0 25 1 12 2 15 3 14 4 19 Name: points, dtype: int64

**Example 4: View First ***n* Rows of Several Columns

*n*Rows of Several Columns

The following code shows how to view the first five rows of several specific columns in a DataFrame:

#view first five rows of values in 'points' and 'assists' columns df[['points', 'assists']].head() points assists 0 25 5 1 12 7 2 15 7 3 14 9 4 19 12

**Additional Resources**

The following tutorials explain how to perform other common functions in pandas:

How to Select Unique Rows in Pandas

How to Shuffle Rows in a Pandas DataFrame

How to Get Index of Rows Whose Column Matches Value in Pandas