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I have the following dataframe (50000 lines for 2016,2017,2018):

data.head() Values Date 2016-07-20 10.0 2016-07-20 10.0 2016-07-22 1.0 2016-07-22 1.0 2016-07-22 1.0

simple groupby :

data.groupby([(data1.index.year==2018),(data1.index.month==5)])['Values'].sum() False False 1461787.35 True 80714.53 True False 862589.66 True 172600.04 Name: Values, dtype: float64

How can I change False and True in year and month to 2018 and 5 ?

Output expected :

 2018 5 80714.53 Name: Values, dtype: float64
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  • Sorry, what is expected output? Commented Dec 22, 2018 at 15:31
  • The same but with numbers in place of (false/true) Commented Dec 22, 2018 at 15:33

1 Answer 1

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You need grouping by years and months only: 

#changed data for better sample print (data) Values Date 2015-07-20 10.0 2015-05-20 20.0 2016-05-20 2.0 2018-05-22 1.0 2018-05-22 3.0 2018-07-22 4.0 df1 = data.groupby([data.index.year, data.index.month])['Values'].sum() print (df1) Date Date 2015 5 20.0 7 10.0 2016 5 2.0 2018 5 4.0 7 4.0 Name: Values, dtype: float64

Your solution grouping by boolean mask, True are filtered only 2018 years and Falses are all non 2018 rows, similar for months:

df2 = data.groupby([(data.index.year==2018),(data.index.month==5)])['Values'].sum() print (df2) False False 10.0 True 22.0 True False 4.0 True 4.0 Name: Values, dtype: float64

If want only filtered values is possible use:

df = data[(data.index.year==2018) & (data.index.month==5)] print (df) Values Date 2018-05-22 1.0 2018-05-22 3.0 out = df.groupby([df.index.year, df.index.month])['Values'].sum() print (out) Date Date 2018 5 4.0 Name: Values, dtype: float64
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2 Comments

Thank you, that's what i ' m looking for
@Math - You are welcome! Also added samples for better understand difference.

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