CODEWITHSUNDEEP
dataframepysparkapache-spark-sql
Nygen Patricia
Nygen Patricia

Reputation: 229

pyspark dataframe sum

I am trying to perform the following operation on pyspark.sql.dataframe

from pyspark.sql.functions import sum as spark_sum
df = spark.createDataFrame([
    ('a', 1.0, 1.0), ('a',1.0, 0.2), ('b', 1.0, 1.0),
    ('c' ,1.0, 0.5), ('d', 0.55, 1.0),('e', 1.0, 1.0)
])
>>> df.show()
+---+----+---+                                                                  
| _1|  _2| _3|
+---+----+---+
|  a| 1.0|1.0|
|  a| 1.0|0.2|
|  b| 1.0|1.0|
|  c| 1.0|0.5|
|  d|0.55|1.0|
|  e| 1.0|1.0|
+---+----+---+

Then, I am trying to do the following operation.

1) Select the rows when column df[_2] > df[_3]

2) For each row of selected from above, multiply df[_2] * df[_3], then take their sum

3) divide the result from above by the sum of column of df[_3]

Here is what I did:

>>> filter_df = df.where(df['_2'] > df['_3'])
>>> filter_df.show()
+---+---+---+
| _1| _2| _3|
+---+---+---+
|  a|1.0|0.2|
|  c|1.0|0.5|
+---+---+---+

>>> result = spark_sum(filter_df['_2'] * filter_df['_3']) 
             / spark_sum(filter_df['_3'])

>>> df.select(result).show()
+--------------------------+
|(sum((_2 * _3)) / sum(_3))|
+--------------------------+
|        0.9042553191489361|
+--------------------------+

But the answer should be (1.0 * 0.2 + 1.0 * 0.5) / (0.2+0.5) = 1.0 This is not correct. What??

It seems to me that such operation only taken on the original df, but not the filter_df. WTF?

Upvotes: 4

Views: 1540

Answers (1)

Suresh
Suresh

Reputation: 5870

You need to call it in filter_df.

>>> result = spark_sum(filter_df['_2'] * filter_df['_3']) 
         / spark_sum(filter_df['_3'])

This is a transformation function which returns a column and gets applied on dataframe we apply it (lazy evaluation). Sum is an aggregate function and when called without any groups, it applies on whole dataset.

>>> filter_df.select(result).show()
+--------------------------+
|(sum((_2 * _3)) / sum(_3))|
+--------------------------+
|                       1.0|
+--------------------------+

Upvotes: 2

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