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The Pandas head() function is used to return the first n rows from a dataframe, where “n” is an integer value. It also allows for the selection of a specific number of rows and columns from the dataframe, and can be used to quickly preview the data before further processing. This function is very useful for quickly exploring a dataset and understanding its basic structure. For example, one might use head() to determine the number of columns and the data types contained in each column of a dataframe.
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
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
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
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
The following tutorials explain how to perform other common functions in pandas: