Convert list to a dataframe column in pyspark - pyspark

Question

I have a dataframe in which one of the string type column contains a list of items that I want to explode and make it part of the parent dataframe. How can I do it?
Here is the code to create a sample dataframe:
from pyspark.sql import Row
from collections import OrderedDict
def convert_to_row(d: dict) -> Row:
return Row(**OrderedDict(sorted(d.items())))
df=sc.parallelize([{"arg1": "first", "arg2": "John", "arg3" : '[{"name" : "click", "datetime" : "1570103345039", "event" : "entry" }, {"name" : "drag", "datetime" : "1580133345039", "event" : "exit" }]'},{"arg1": "second", "arg2": "Joe", "arg3": '[{"name" : "click", "datetime" : "1670105345039", "event" : "entry" }, {"name" : "drop", "datetime" : "1750134345039", "event" : "exit" }]'},{"arg1": "third", "arg2": "Jane", "arg3" : '[{"name" : "click", "datetime" : "1580105245039", "event" : "entry" }, {"name" : "drop", "datetime" : "1650134345039", "event" : "exit" }]'}]) \
.map(convert_to_row).toDF()
Running this code will create a dataframe as shown below:
+------+----+--------------------+
| arg1|arg2| arg3|
+------+----+--------------------+
| first|John|[{"name" : "click...|
|second| Joe|[{"name" : "click...|
| third|Jane|[{"name" : "click...|
+------+----+--------------------+
The arg3 column contains a list which I want to explode it into the detailed columns. I want the dataframe as follows:
arg1 | arg2 | arg3 | name | datetime | event
How can I achieve that?

Answer 1

You need to specify array to the schema in from_json function:
from pyspark.sql.functions import explode, from_json
schema = 'array<struct<name:string,datetime:string,event:string>>'
df.withColumn('data', explode(from_json('arg3', schema))) \
.select(*df.columns, 'data.*') \
.show()
+------+----+--------------------+-----+-------------+-----+
| arg1|arg2| arg3| name| datetime|event|
+------+----+--------------------+-----+-------------+-----+
| first|John|[{"name" : "click...|click|1570103345039|entry|
| first|John|[{"name" : "click...| drag|1580133345039| exit|
|second| Joe|[{"name" : "click...|click|1670105345039|entry|
|second| Joe|[{"name" : "click...| drop|1750134345039| exit|
| third|Jane|[{"name" : "click...|click|1580105245039|entry|
| third|Jane|[{"name" : "click...| drop|1650134345039| exit|
+------+----+--------------------+-----+-------------+-----+
Note: if your Spark version does not support simpleString format for schema, try the following:
from pyspark.sql.types import ArrayType, StringType, StructType, StructField
schema = ArrayType(
StructType([
StructField('name',StringType())
, StructField('datetime',StringType())
, StructField('event',StringType())
])
)