search
Search
Join our weekly DS/ML newsletter layers DS/ML Guides
menu
menu search toc more_vert
Robocat
Guest 0reps
Thanks for the thanks!
close
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
help Ask a question
Share on Twitter
search
keyboard_voice
close
Searching Tips
Search for a recipe:
"Creating a table in MySQL"
Search for an API documentation: "@append"
Search for code: "!dataframe"
Apply a tag filter: "#python"
Useful Shortcuts
/ to open search panel
Esc to close search panel
to navigate between search results
d to clear all current filters
Enter to expand content preview
icon_star
Doc Search
icon_star
Code Search Beta
SORRY NOTHING FOUND!
mic
Start speaking...
Voice search is only supported in Safari and Chrome.
Navigate to
A
A
brightness_medium
share
arrow_backShare
Twitter
Facebook

PySpark DataFrame | head method

Machine Learning
chevron_right
PySpark
chevron_right
Documentation
chevron_right
PySpark DataFrame
schedule Jul 1, 2022
Last updated
local_offer PySpark
Tags

PySpark DataFrame's head(~) method returns the first n number of rows as Row objects.

Parameters

1. n | int | optional

The number of rows to return. By default, n=1.

Return Value

  • If n is larger than 1, then a list of Row objects is returned

  • if n is equal to 1, then a single Row object (pyspark.sql.types.Row) is returned

Examples

Consider the following PySpark DataFrame:

columns = ["name", "age"]
data = [("Alex", 15), ("Bob", 20), ("Cathy", 25)]
df = spark.createDataFrame(data, columns)
df.show()
+-----+---+
| name|age|
+-----+---+
| Alex| 15|
| Bob| 20|
|Cathy| 25|
+-----+---+

Getting the first row of PySpark DataFrame as a Row object

To get the first row as a Row object:

df.head()
Row(name='Alex', age=15)

Getting a list of first n Row objects of PySpark DataFrame

To get the first two rows as a list of Row objects:

df.head(n=2)
[Row(name='Alex', age=15), Row(name='Bob', age=20)]
mail
Join our newsletter for updates on new DS/ML comprehensive guides (spam-free)
robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!