Fill in null with previously known good value with pyspark - apache-spark

Question

Is there a way to replace null values in pyspark dataframe with the last valid value? There is addtional timestamp and session columns if you think you need them for windows partitioning and ordering. More specifically, I'd like to achieve the following conversion:
+---------+-----------+-----------+ +---------+-----------+-----------+
| session | timestamp | id| | session | timestamp | id|
+---------+-----------+-----------+ +---------+-----------+-----------+
| 1| 1| null| | 1| 1| null|
| 1| 2| 109| | 1| 2| 109|
| 1| 3| null| | 1| 3| 109|
| 1| 4| null| | 1| 4| 109|
| 1| 5| 109| => | 1| 5| 109|
| 1| 6| null| | 1| 6| 109|
| 1| 7| 110| | 1| 7| 110|
| 1| 8| null| | 1| 8| 110|
| 1| 9| null| | 1| 9| 110|
| 1| 10| null| | 1| 10| 110|
+---------+-----------+-----------+ +---------+-----------+-----------+

Answer 1

This uses last and ignores nulls.
Let's re-create something similar to the original data:
import sys
from pyspark.sql.window import Window
import pyspark.sql.functions as func
d = [{'session': 1, 'ts': 1}, {'session': 1, 'ts': 2, 'id': 109}, {'session': 1, 'ts': 3}, {'session': 1, 'ts': 4, 'id': 110}, {'session': 1, 'ts': 5}, {'session': 1, 'ts': 6}]
df = spark.createDataFrame(d)
df.show()
# +-------+---+----+
# |session| ts| id|
# +-------+---+----+
# | 1| 1|null|
# | 1| 2| 109|
# | 1| 3|null|
# | 1| 4| 110|
# | 1| 5|null|
# | 1| 6|null|
# +-------+---+----+
Now, let's use window function last:
df.withColumn("id", func.last('id', True).over(Window.partitionBy('session').orderBy('ts').rowsBetween(-sys.maxsize, 0))).show()
# +-------+---+----+
# |session| ts| id|
# +-------+---+----+
# | 1| 1|null|
# | 1| 2| 109|
# | 1| 3| 109|
# | 1| 4| 110|
# | 1| 5| 110|
# | 1| 6| 110|
# +-------+---+----+

Answer 2

This seems to be doing the trick using Window functions:
import sys
from pyspark.sql.window import Window
import pyspark.sql.functions as func
def fill_nulls(df):
df_na = df.na.fill(-1)
lag = df_na.withColumn('id_lag', func.lag('id', default=-1)\
.over(Window.partitionBy('session')\
.orderBy('timestamp')))
switch = lag.withColumn('id_change',
((lag['id'] != lag['id_lag']) &
(lag['id'] != -1)).cast('integer'))
switch_sess = switch.withColumn(
'sub_session',
func.sum("id_change")
.over(
Window.partitionBy("session")
.orderBy("timestamp")
.rowsBetween(-sys.maxsize, 0))
)
fid = switch_sess.withColumn('nn_id',
func.first('id')\
.over(Window.partitionBy('session', 'sub_session')\
.orderBy('timestamp')))
fid_na = fid.replace(-1, 'null')
ff = fid_na.drop('id').drop('id_lag')\
.drop('id_change')\
.drop('sub_session').\
withColumnRenamed('nn_id', 'id')
return ff
Here is the full null_test.py.

Answer 3

#Oleksiy's answer is great, but didn't fully work for my requirements. Within a session, if multiple nulls are observed, all are filled with the first non-null for the session. I needed the last non-null value to propagate forward.
The following tweak worked for my use case:
def fill_forward(df, id_column, key_column, fill_column):
# Fill null's with last *non null* value in the window
ff = df.withColumn(
'fill_fwd',
func.last(fill_column, True) # True: fill with last non-null
.over(
Window.partitionBy(id_column)
.orderBy(key_column)
.rowsBetween(-sys.maxsize, 0))
)
# Drop the old column and rename the new column
ff_out = ff.drop(fill_column).withColumnRenamed('fill_fwd', fill_column)
return ff_out

Answer 4

Here is the trick I followed by converting pyspark dataframe into pandas dataframe and doing the operation as pandas has built-in function to fill null values with previously known good value. And changing it back to pyspark dataframe.
Here is the code!!
d = [{'session': 1, 'ts': 1}, {'session': 1, 'ts': 2, 'id': 109}, {'session': 1, 'ts': 3}, {'session': 1, 'ts': 4, 'id': 110}, {'session': 1, 'ts': 5}, {'session': 1, 'ts': 6},{'session': 1, 'ts': 7, 'id': 110},{'session': 1, 'ts': 8},{'session': 1, 'ts': 9},{'session': 1, 'ts': 10}]\
dt = spark.createDataFrame(d)
import pandas as pd\
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
psdf= dt.select("*").toPandas()\
psdf["id"].fillna(method='ffill', inplace=True)\
dt= spark.createDataFrame(psdf)\
dt.show()