PySpark – Variance of a DataFrame Column

In this tutorial, we will look at how to get the variance of a column in a Pyspark dataframe with the help of some examples.

How to get variance for a Pyspark dataframe column?

You can use the variance() function from the pyspark.sql.functions module to compute the variance of a Pyspark column. The following is the syntax –

variance("column_name")

Pass the column name as a parameter to the variance() function.

You can similarly use the variance_samp() function to get the sample variance and the variance_pop() function to get the population variance. Both the functions are available in the same pyspark.sql.functions module.

Examples

Let’s look at some examples of computing variance for column(s) in a Pyspark dataframe. First, let’s create a sample Pyspark dataframe that we will be using throughout this tutorial.

#import the pyspark module
import pyspark
  
# import the  sparksession class  from pyspark.sql
from pyspark.sql import SparkSession

# create an app from SparkSession class
spark = SparkSession.builder.appName('datascience_parichay').getOrCreate()

# books data as list of lists
df = [[1, "PHP", "Sravan", 250, 454],
        [2, "SQL", "Chandra", 300, 320],
        [3, "Python", "Harsha", 250, 500],
        [4, "R", "Rohith", 1200, 310],
        [5, "Hadoop", "Manasa", 700, 270],
        ]
  
# creating dataframe from books data
dataframe = spark.createDataFrame(df, ['Book_Id', 'Book_Name', 'Author', 'Price', 'Pages'])

# display the dataframe
dataframe.show()

Output:

+-------+---------+-------+-----+-----+
|Book_Id|Book_Name| Author|Price|Pages|
+-------+---------+-------+-----+-----+
|      1|      PHP| Sravan|  250|  454|
|      2|      SQL|Chandra|  300|  320|
|      3|   Python| Harsha|  250|  500|
|      4|        R| Rohith| 1200|  310|
|      5|   Hadoop| Manasa|  700|  270|
+-------+---------+-------+-----+-----+

We have a dataframe containing information on books like their author names, prices, pages, etc.

Variance of a single column

Let’s compute the variance for the “Price” column in the dataframe. To do so, you can use the variance() function in combination with the Pyspark select() function.

from pyspark.sql.functions import variance

# variance of the Price column
dataframe.select(variance("Price")).show()

Output:

+---------------+
|var_samp(Price)|
+---------------+
|       171750.0|
+---------------+

We get the variance for the “Price” column. Note that the variance() function gives the sample variance.

Alternatively, you can use the Pyspark agg() function to compute the variance for a column.

# variance of the Price column
dataframe.agg({'Price': 'variance'}).show()

Output:

+---------------+
|variance(Price)|
+---------------+
|       171750.0|
+---------------+

We get the same result as above.

Let’s now use the var_samp() and var_pop() functions on the same column along with the variance() function to compare their results.

from pyspark.sql.functions import variance, var_samp, var_pop

# variance of the Price column
dataframe.select(variance("Price"), var_samp("Price"), var_pop("Price")).show()

Output:

+---------------+---------------+--------------+
|var_samp(Price)|var_samp(Price)|var_pop(Price)|
+---------------+---------------+--------------+
|       171750.0|       171750.0|      137400.0|
+---------------+---------------+--------------+

You can see that variance() and var_samp() give the same result which is the sample variance whereas the var_pop() function gave the population variance.

Variance for more than one column

You can get the variance for more than one column as well. Inside the select() function, use a separate variance() function for each column you want to compute the variance for.

Let’s compute the variance for the “Price” and the “Pages” columns.

from pyspark.sql.functions import variance

# variance of the Price and Pages columns
dataframe.select(variance("Price"), variance("Pages")).show()

Output:

+---------------+---------------+
|var_samp(Price)|var_samp(Pages)|
+---------------+---------------+
|       171750.0|        10013.2|
+---------------+---------------+

We get the desired output.

You can also use the agg() function to compute the variance of multiple columns.

# variance of the Price and Pages columns
dataframe.agg({'Price': 'variance', 'Pages': 'variance'}).show()

Output:

+---------------+---------------+
|variance(Pages)|variance(Price)|
+---------------+---------------+
|        10013.2|       171750.0|
+---------------+---------------+

We get the same result as above.

You might also be interested in –

• Pyspark – Standard Deviation of a Column
• Calculate Standard Deviation in Python
• Pandas – Get Standard Deviation of one or more Columns