spark dataframe drop duplicates and keep first

Question

Question: in pandas when dropping duplicates you can specify which columns to keep. Is there an equivalent in Spark Dataframes?

Pandas:

df.sort_value         


        

Answer 1

To everyone saying that dropDuplicates keeps the first occurrence - this is not strictly correct.

dropDuplicates keeps the 'first occurrence' of a sort operation - only if there is 1 partition. See below for some examples.
However this is not practical for most Spark datasets. So I'm also including an example of 'first occurrence' drop duplicates operation using Window function + sort + rank + filter.
See bottom of post for example.

This is tested in Spark 2.4.0 using pyspark.

dropDuplicates examples

import pandas as pd

# generating some example data with pandas, will convert to spark df below
df1 = pd.DataFrame({'col1':range(0,5)})
df1['datestr'] = '2018-01-01'
df2 = pd.DataFrame({'col1':range(0,5)})
df2['datestr'] = '2018-02-01'
df3 = pd.DataFrame({'col1':range(0,5)})
df3['datestr'] = '2018-03-01'
dfall = pd.concat([df1,df2,df3])
print(dfall)

   col1     datestr
0     0  2018-01-01
1     1  2018-01-01
2     2  2018-01-01
3     3  2018-01-01
4     4  2018-01-01
0     0  2018-02-01
1     1  2018-02-01
2     2  2018-02-01
3     3  2018-02-01
4     4  2018-02-01
0     0  2018-03-01
1     1  2018-03-01
2     2  2018-03-01
3     3  2018-03-01
4     4  2018-03-01

# first example
# does not give first (based on datestr)
(spark.createDataFrame(dfall)
   .orderBy('datestr')
   .dropDuplicates(subset = ['col1'])
   .show()
)

# dropDuplicates NOT based on occurrence of sorted datestr

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-03-01|
|   1|2018-02-01|
|   3|2018-02-01|
|   2|2018-02-01|
|   4|2018-01-01|
+----+----------+

# second example
# testing what happens with repartition
(spark.createDataFrame(dfall)
   .orderBy('datestr')
   .repartition('datestr')
   .dropDuplicates(subset = ['col1'])
   .show()
)

# dropDuplicates NOT based on occurrence of sorted datestr

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-02-01|
|   1|2018-01-01|
|   3|2018-02-01|
|   2|2018-02-01|
|   4|2018-02-01|
+----+----------+

#third example
# testing with coalesce(1)
(spark
   .createDataFrame(dfall)
   .orderBy('datestr')
   .coalesce(1)
   .dropDuplicates(subset = ['col1'])
   .show()
)

# dropDuplicates based on occurrence of sorted datestr

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-01-01|
|   1|2018-01-01|
|   2|2018-01-01|
|   3|2018-01-01|
|   4|2018-01-01|
+----+----------+

# fourth example
# testing with reverse sort then coalesce(1)
(spark
   .createDataFrame(dfall)
   .orderBy('datestr', ascending = False)
   .coalesce(1)
   .dropDuplicates(subset = ['col1'])
   .show()
)
# dropDuplicates based on occurrence of sorted datestr```

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-03-01|
|   1|2018-03-01|
|   2|2018-03-01|
|   3|2018-03-01|
|   4|2018-03-01|
+----+----------+

window, sort, rank, filter example

# generating some example data with pandas
df1 = pd.DataFrame({'col1':range(0,5)})
df1['datestr'] = '2018-01-01'
df2 = pd.DataFrame({'col1':range(0,5)})
df2['datestr'] = '2018-02-01'
df3 = pd.DataFrame({'col1':range(0,5)})
df3['datestr'] = '2018-03-01'
dfall = pd.concat([df1,df2,df3])
# into spark df
df_s = (spark.createDataFrame(dfall))

from pyspark.sql import Window
from pyspark.sql.functions import rank
window = Window.partitionBy("col1").orderBy("datestr")
(df_s.withColumn('rank', rank().over(window))
.filter(col('rank') == 1)
.drop('rank')
.show()
)

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-01-01|
|   1|2018-01-01|
|   3|2018-01-01|
|   2|2018-01-01|
|   4|2018-01-01|
+----+----------+

# however this fails if ties/duplicates exist in the windowing paritions
# and so a tie breaker for the 'rank' function must be added

# generating some example data with pandas, will convert to spark df below
df1 = pd.DataFrame({'col1':range(0,5)})
df1['datestr'] = '2018-01-01'
df2 = pd.DataFrame({'col1':range(0,5)})
df2['datestr'] = '2018-01-01' # note duplicates in this dataset
df3 = pd.DataFrame({'col1':range(0,5)})
df3['datestr'] = '2018-03-01'
dfall = pd.concat([df1,df2,df3])
print(dfall)

   col1     datestr
0     0  2018-01-01
1     1  2018-01-01
2     2  2018-01-01
3     3  2018-01-01
4     4  2018-01-01
0     0  2018-01-01
1     1  2018-01-01
2     2  2018-01-01
3     3  2018-01-01
4     4  2018-01-01
0     0  2018-03-01
1     1  2018-03-01
2     2  2018-03-01
3     3  2018-03-01
4     4  2018-03-01

# this will fail, since duplicates exist within the window partitions
# and no way to specify ranking style exists in pyspark rank() fn
window = Window.partitionBy("col1").orderBy("datestr")
(df_s.withColumn('rank', rank().over(window))
.filter(col('rank') == 1)
.drop('rank')
.show()
)

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-01-01|
|   0|2018-01-01|
|   1|2018-01-01|
|   1|2018-01-01|
|   3|2018-01-01|
|   3|2018-01-01|
|   2|2018-01-01|
|   2|2018-01-01|
|   4|2018-01-01|
|   4|2018-01-01|
+----+----------+

# to deal with ties within window partitions, a tiebreaker column is added
from pyspark.sql import Window
from pyspark.sql.functions import rank, col, monotonically_increasing_id
window = Window.partitionBy("col1").orderBy("datestr",'tiebreak')
(df_s
 .withColumn('tiebreak', monotonically_increasing_id())
 .withColumn('rank', rank().over(window))
 .filter(col('rank') == 1).drop('rank','tiebreak')
 .show()
)

+----+----------+
|col1|   datestr|
+----+----------+
|   0|2018-01-01|
|   1|2018-01-01|
|   3|2018-01-01|
|   2|2018-01-01|
|   4|2018-01-01|
+----+----------+