
        
        
            
                
                
                    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 value of a column in PySpark DataFrame

To get the first value of the name column:


        
        
            
                
                
                    import pyspark.sql.functions as F
df.select(F.first(df.name)).show()
                
            
            +-----------+
|first(name)|
+-----------+
|       Alex|
+-----------+

Getting the first non-null value of a column in PySpark DataFrame

Consider the following PySpark DataFrame with null values:


        
        
            
                
                
                    columns = ["name", "age"]
data = [("Alex", None), ("Bob", 20), ("Cathy", 25)]
df = spark.createDataFrame(data, columns)
df.show()
                
            
            +-----+----+
| name| age|
+-----+----+
| Alex|null|
|  Bob|  20|
|Cathy|  25|
+-----+----+

By default, ignorenulls=False, which means that the first value is returned regardless of whether it is null or not:


        
        
            
                
                
                    df.select(F.first(df.age)).show()
                
            
            +----------+
|first(age)|
+----------+
|      null|
+----------+

To return the first non-null value instead:


        
        
            
                
                
                    df.select(F.first(df.age, ignorenulls=True)).show()
                
            
            +----------+
|first(age)|
+----------+
|        20|
+----------+

Getting the first value of each group in PySpark

The first(~) method is also useful in aggregations. Consider the following PySpark DataFrame:


        
        
            
                
                
                    columns = ["name", "class"]
data = [("Alex", "A"), ("Alex", "B"), ("Bob", None), ("Bob", "A"), ("Cathy", "C")]
df = spark.createDataFrame(data, columns)
df.show()
                
            
            +-----+-----+
| name|class|
+-----+-----+
| Alex|    A|
| Alex|    B|
|  Bob| null|
|  Bob|    A|
|Cathy|    C|
+-----+-----+

To get the first value of each aggregate:


        
        
            
                
                
                    df.groupby("name").agg(F.first("class")).show()
                
            
            +-----+------------+
| name|first(class)|
+-----+------------+
| Alex|           A|
|  Bob|        null|
|Cathy|           C|
+-----+------------+

Here, we are grouping by name, and then for each of these group, we are obtaining the first value that occurred in the class column.

Published by Isshin Inada

Edited by 0 others

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Official PySpark Documentation

https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.functions.first.html?highlight=first#pyspark.sql.functions.first

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