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I have a dataframe which consists lists in columns similar to the following. The length of the lists in all columns is not same.
Name Age Subjects Grades
[Bob] [16] [Maths,Physics,Chemistry] [A,B,C]
I want to explode the dataframe in such a way that i get the following output-
Name Age Subjects Grades
Bob 16 Maths A
Bob 16 Physics B
Bob 16 Chemistry C
How can I achieve this?
259
PySpark function explode(e: Column) is used to explode or create array or map columns to rows. When an array is passed to this function, it creates a new default column “col1” and it contains all array elements.
You can select the single or multiple columns of the Spark DataFrame by passing the column names you wanted to select to the select() function. Since DataFrame is immutable, this creates a new DataFrame with a selected columns. show() function is used to show the DataFrame contents.
Solution: PySpark explode function can be used to explode an Array of Array (nested Array) ArrayType(ArrayType(StringType)) columns to rows on PySpark DataFrame using python example. Before we start, let's create a DataFrame with a nested array column.
PySpark has added an arrays_zip function in 2.4, which eliminates the need for a Python UDF to zip the arrays.
import pyspark.sql.functions as F
from pyspark.sql.types import *
df = sql.createDataFrame(
[(['Bob'], [16], ['Maths','Physics','Chemistry'], ['A','B','C'])],
['Name','Age','Subjects', 'Grades'])
df = df.withColumn("new", F.arrays_zip("Subjects", "Grades"))\
.withColumn("new", F.explode("new"))\
.select("Name", "Age", F.col("new.Subjects").alias("Subjects"), F.col("new.Grades").alias("Grades"))
df.show()
+-----+----+---------+------+
| Name| Age| Subjects|Grades|
+-----+----+---------+------+
|[Bob]|[16]| Maths| A|
|[Bob]|[16]| Physics| B|
|[Bob]|[16]|Chemistry| C|
+-----+----+---------+------+
This works,
import pyspark.sql.functions as F
from pyspark.sql.types import *
df = sql.createDataFrame(
[(['Bob'], [16], ['Maths','Physics','Chemistry'], ['A','B','C'])],
['Name','Age','Subjects', 'Grades'])
df.show()
+-----+----+--------------------+---------+
| Name| Age| Subjects| Grades|
+-----+----+--------------------+---------+
|[Bob]|[16]|[Maths, Physics, ...|[A, B, C]|
+-----+----+--------------------+---------+
Use udf with zip. Those columns needed to explode have to be merged before exploding.
combine = F.udf(lambda x, y: list(zip(x, y)),
ArrayType(StructType([StructField("subs", StringType()),
StructField("grades", StringType())])))
df = df.withColumn("new", combine("Subjects", "Grades"))\
.withColumn("new", F.explode("new"))\
.select("Name", "Age", F.col("new.subs").alias("Subjects"), F.col("new.grades").alias("Grades"))
df.show()
+-----+----+---------+------+
| Name| Age| Subjects|Grades|
+-----+----+---------+------+
|[Bob]|[16]| Maths| A|
|[Bob]|[16]| Physics| B|
|[Bob]|[16]|Chemistry| C|
+-----+----+---------+------+
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