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Calculating the standard deviation in PySpark involves using the “stat” module from the PySpark library. This module provides a “summary” function which can be used to calculate the mean, count, standard deviation, and other summary statistics for a given column in a PySpark dataframe. The standard deviation can be easily calculated by passing in the column name as a parameter to the “summary” function. This method allows for efficient and accurate standard deviation calculations on large datasets in a distributed computing environment. Additionally, PySpark also offers various built-in functions and methods for calculating the standard deviation, making it a convenient and reliable tool for statistical analysis in big data applications.
Calculate Standard Deviation in PySpark
You can use the following methods to calculate the standard deviation of a column in a PySpark DataFrame:
Method 1: Calculate Standard Deviation for One Specific Column
from pyspark.sql import functions as F
#calculate standard deviation of values in 'game1' column
df.agg(F.stddev('game1')).collect()[0][0]
Method 2: Calculate Standard Deviation for Multiple Columns
from pyspark.sql.functions import stddev
#calculate standard deviation for game1, game2 and game3 columns
df.select(stddev(df.game1), stddev(df.game2), stddev(df.game3)).show() Note: The stddev function uses the to calculate the standard deviation.
If you would instead like to use the population standard deviation formula, then use the stddev_pop function instead.
The following examples show how to use each method in practice with the following PySpark DataFrame:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
#define data
data = [['Mavs', 25, 11, 10],
['Nets', 22, 8, 14],
['Hawks', 14, 22, 10],
['Kings', 30, 22, 35],
['Bulls', 15, 14, 12],
['Blazers', 10, 14, 18]]
#define column names
columns = ['team', 'game1', 'game2', 'game3']
#create dataframe using data and column names
df = spark.createDataFrame(data, columns)
#view dataframe
df.show()
+-------+-----+-----+-----+
| team|game1|game2|game3|
+-------+-----+-----+-----+
| Mavs| 25| 11| 10|
| Nets| 22| 8| 14|
| Hawks| 14| 22| 10|
| Kings| 30| 22| 35|
| Bulls| 15| 14| 12|
|Blazers| 10| 14| 18|
+-------+-----+-----+-----+ Example 1: Calculate Standard Deviation for One Specific Column
We can use the following syntax to calculate the standard deviation of values in the game1 column of the DataFrame only:
from pyspark.sql import functions as F
#calculate standard deviation of column named 'game1'
df.agg(F.stddev('game1')).collect()[0][0]
7.5806771905065755
The standard deviation of values in the game1 column turns out to be 7.5807.
Example 2: Calculate Standard Deviation for Multiple Columns
We can use the following syntax to calculate the standard deviation of values for the game1, game2 and game3 columns of the DataFrame:
from pyspark.sql.functions import stddev
#calculate standard deviation for game1, game2 and game3 columns
df.select(stddev(df.game1), stddev(df.game2), stddev(df.game3)).show()
+------------------+------------------+------------------+
|stddev_samp(game1)|stddev_samp(game2)|stddev_samp(game3)|
+------------------+------------------+------------------+
|7.5806771905065755| 5.741660619251774| 9.544631999192006|
+------------------+------------------+------------------+
From the output we can see:
- The standard deviation of values in the game1 column is 7.5807.
- The standard deviation of values in the game2 column is 5.7417.
- The standard deviation of values in the game3 column is 9.5446.
Note: If there are null values in the column, the stddev function will ignore these values by default.
Additional Resources
The following tutorials explain how to perform other common tasks in PySpark: