Issue
I have an initial dataframe
df1 =
+---+---+---+
| A| B| C|
+---+---+---+
| 1| 1| 10|
| 1| 2| 11|
| 1| 2| 12|
| 3| 1| 13|
| 2| 1| 14|
| 2| 1| 15|
| 2| 1| 16|
| 4| 1| 17|
| 4| 2| 18|
| 4| 3| 19|
| 4| 4| 19|
| 4| 5| 20|
| 4| 5| 20|
+---+---+---+
Using pyspark I coded the dataframe with a window function using a sum taking into account the column 'A' and taking into account the column 'B' sorted.
spec = Window.partitionBy('A').orderBy('B')
df1 = df1.withColumn('D',sum('C').over(spec))
df1.show()
+---+---+---+-----+
| A| B| C| D|
+---+---+---+-----+
| 1| 1| 10| 10.0|
| 1| 2| 11| 33.0|
| 1| 2| 12| 33.0|
| 2| 1| 14| 45.0|
| 2| 1| 15| 45.0|
| 2| 1| 16| 45.0|
| 3| 1| 13| 13.0|
| 4| 1| 17| 17.0|
| 4| 2| 18| 35.0|
| 4| 3| 19| 54.0|
| 4| 4| 19| 73.0|
| 4| 5| 20|113.0|
| 4| 5| 20|113.0|
+---+---+---+-----+
Is it possible to do the same calculation using Pandas Dataframe?
I tried using
df['D'] = df.sort_values(['A','B']).groupby(['A', 'B'])['C'].transform('cumsum')
but it is not the same result
Thanks
Solution
In pandas we can groupby sum
on A
and B
. Then groupby cumsum
just over A
. To add results back to the DataFrame rename
to the new column name then join
the results back to the DataFrame on the initial group keys ['A', 'B']
:
df1 = df1.join(
df1.groupby(by=['A', 'B'])['C'].sum()
.groupby(level='A').cumsum()
.rename('D'),
on=['A', 'B']
)
df1
:
A B C D
0 1 1 10 10
1 1 2 11 33
2 1 2 12 33
3 3 1 13 13
4 2 1 14 45
5 2 1 15 45
6 2 1 16 45
7 4 1 17 17
8 4 2 18 35
9 4 3 19 54
10 4 4 19 73
11 4 5 20 113
12 4 5 20 113
Setup:
import pandas as pd
df1 = pd.DataFrame({
'A': [1, 1, 1, 3, 2, 2, 2, 4, 4, 4, 4, 4, 4],
'B': [1, 2, 2, 1, 1, 1, 1, 1, 2, 3, 4, 5, 5],
'C': [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 19, 20, 20]
})
Answered By - Henry Ecker
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