How can values in a column be counted using a condition in PySpark?

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You can use the following methods to count the number of values in a column of a PySpark DataFrame that meet a specific condition:

Method 1: Count Values that Meet One Condition

#count values in 'team' column that are equal to 'C'
df.filter(df.team == 'C').count()

Method 2: Count Values that Meet One of Several Conditions

from pyspark.sql.functions import col

#count values in 'team' column that are equal to 'A' or 'D'
df.filter(col('team').isin(['A','D'])).count()

The following examples show how to use each method in practice with the following PySpark DataFrame that contains information about various basketball players:

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()

#define data
data = [['A', 'East', 11], 
        ['A', 'East', 8], 
        ['A', 'East', 10], 
        ['B', 'West', 6], 
        ['B', 'West', 6], 
        ['C', 'East', 5],
        ['C', 'East', 15],
        ['C', 'West', 31],
        ['D', 'West', 24]] 
  
#define column names
columns = ['team', 'conference', 'points'] 
  
#create dataframe using data and column names
df = spark.createDataFrame(data, columns) 
  
#view dataframe
df.show()

+----+----------+------+
|team|conference|points|
+----+----------+------+
|   A|      East|    11|
|   A|      East|     8|
|   A|      East|    10|
|   B|      West|     6|
|   B|      West|     6|
|   C|      East|     5|
|   C|      East|    15|
|   C|      West|    31|
|   D|      West|    24|
+----+----------+------+

Example 1: Count Values that Meet One Condition

We can use the following syntax to count the number of values in the team column that are equal to C:

#count values in 'team' column that are equal to 'C'
df.filter(df.team == 'C').count()

3

We can see that a total of 3 values in the team column are equal to C.

Example 2: Count Values that Meet One of Several Conditions

We can use the following syntax to count the number of values in the team column that are equal to either A or D:

from pyspark.sql.functions import col

#count values in 'team' column that are equal to 'A' or 'D'
df.filter(col('team').isin(['A','D'])).count()

4

We can see that a total of 4 values in the team column are equal to either A or D.

Note: You can find the complete documentation for the PySpark filter function .

Additional Resources