search
Search
Login
Unlock 100+ guides
menu
menu
web
search toc
close
Comments
Log in or sign up
Cancel
Post
account_circle
Profile
exit_to_app
Sign out
What does this mean?
Why is this true?
Give me some examples!
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

PySpark SQL Functions | mean method

schedule Aug 12, 2023
Last updated
local_offer
PySpark
Tags
mode_heat
Master the mathematics behind data science with 100+ top-tier guides
Start your free 7-days trial now!

PySpark SQL Functions' mean(~) method returns the mean value in the specified column.

Parameters

1. col | string or Column

The column in which to obtain the mean value.

Return Value

A PySpark Column (pyspark.sql.column.Column).

Examples

Consider the following PySpark DataFrame:

df = spark.createDataFrame([["Alex", 25], ["Bob", 30]], ["name", "age"])
df.show()
+----+---+
|name|age|
+----+---+
|Alex| 25|
| Bob| 30|
+----+---+

Getting the mean of a PySpark column

To obtain the mean age:

import pyspark.sql.functions as F
df.select(F.mean("age")).show()
+--------+
|avg(age)|
+--------+
| 27.5|
+--------+

To get the mean age as an integer:

list_rows = df.select(F.mean("age")).collect()
list_rows[0][0]
27.5

Here, we are converting the PySpark DataFrame returned from select(~) into a list of Row objects using the collect() method. This list is guaranteed to be of size one because the mean(~) reduces column values into a single number. To access the content of the Row object, we use another [0].

Getting the mean of each group in PySpark

Consider the following PySpark DataFrame:

df = spark.createDataFrame([["Alex", 20, "A"],\
["Bob", 30, "B"],\
["Cathy", 50, "A"]],
["name", "age", "class"])
df.show()
+-----+---+-----+
| name|age|class|
+-----+---+-----+
| Alex| 20| A|
| Bob| 30| B|
|Cathy| 50| A|
+-----+---+-----+

To get the mean age of each class:

df.groupby("class").agg(F.mean("age").alias("MEAN AGE")).show()
+-----+--------+
|class|MEAN AGE|
+-----+--------+
| A| 35.0|
| B| 30.0|
+-----+--------+

Here, we are using alias("MEAN AGE") to assign a label to the aggregated age column.

robocat
Published by Isshin Inada
Edited by 0 others
Did you find this page useful?
thumb_up
thumb_down
Comment
Citation
Ask a question or leave a feedback...
thumb_up
0
thumb_down
0
chat_bubble_outline
0
settings
Enjoy our search
Hit / to insta-search docs and recipes!