CODEWITHSUNDEEP
pyspark
Arvind Kandaswamy
Arvind Kandaswamy

Reputation: 2261

Convert list to a dataframe column in pyspark

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?

Upvotes: 3

Views: 5234

Answers (1)

jxc
jxc

Reputation: 13998

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())
    ])
)

Upvotes: 3

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