CODEWITHSUNDEEP
apache-sparkpysparkapache-spark-sql
Oleksiy
Oleksiy

Reputation: 6567

Fill in null with previously known good value with pyspark

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|
+---------+-----------+-----------+      +---------+-----------+-----------+

Upvotes: 40

Views: 36507

Answers (4)

sainathreddy nandyal
sainathreddy nandyal

Reputation: 1

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()

Upvotes: 0

elmosca
elmosca

Reputation: 434

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|
# +-------+---+----+

Upvotes: 42

brett
brett

Reputation: 101

@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

Upvotes: 10

Oleksiy
Oleksiy

Reputation: 6567

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.

Upvotes: 13

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