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How to create a df that gets sum of columns based on a groupby column?

0

$begingroup$

country year gender measure value0 ... value12
 A 2000 1 vaccinated_at_month 2 ... 1
 B 2000 1 vaccinated_at_month 13 ... 12
 A 2000 0 vaccinated_at_month 4 ... 3
 A 2000 9 vaccinated_at_month 5 ... 4
 B 2000 0 walked_at_month 3 ... 13
 C 2001 1 vaccinated_at_month 6 ... 5
 C 2001 0 vaccinated_at_month 3 ... 2

I want to be able to remove the gender column and collapse all values into sums based on the previous categorical columns.

I have tried

df_new = df.groupby(['country', 'year', 'gender', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

However, df_new still preserves the gender column. I am trying to get this outcome:

country year measure value0 ... value12
 A 2000 vaccinated_at_month 11 (=2+4+5) ... 8 (=1+3+4)
 B 2000 vaccinated_at_month 13 ... 12
 B 2000 walked_at_month 3 ... 13
 C 2001 vaccinated_at_month 9 (=6+3) ... 7 (=5+2)
 C 2001 vaccinated_at_month 3 ... 2

python pandas dataframe

share|improve this question

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

$endgroup$

add a comment | 

0

$begingroup$

country year gender measure value0 ... value12
 A 2000 1 vaccinated_at_month 2 ... 1
 B 2000 1 vaccinated_at_month 13 ... 12
 A 2000 0 vaccinated_at_month 4 ... 3
 A 2000 9 vaccinated_at_month 5 ... 4
 B 2000 0 walked_at_month 3 ... 13
 C 2001 1 vaccinated_at_month 6 ... 5
 C 2001 0 vaccinated_at_month 3 ... 2

I want to be able to remove the gender column and collapse all values into sums based on the previous categorical columns.

I have tried

df_new = df.groupby(['country', 'year', 'gender', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

However, df_new still preserves the gender column. I am trying to get this outcome:

country year measure value0 ... value12
 A 2000 vaccinated_at_month 11 (=2+4+5) ... 8 (=1+3+4)
 B 2000 vaccinated_at_month 13 ... 12
 B 2000 walked_at_month 3 ... 13
 C 2001 vaccinated_at_month 9 (=6+3) ... 7 (=5+2)
 C 2001 vaccinated_at_month 3 ... 2

python pandas dataframe

share|improve this question

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

$endgroup$

add a comment | 

$begingroup$

country year gender measure value0 ... value12
 A 2000 1 vaccinated_at_month 2 ... 1
 B 2000 1 vaccinated_at_month 13 ... 12
 A 2000 0 vaccinated_at_month 4 ... 3
 A 2000 9 vaccinated_at_month 5 ... 4
 B 2000 0 walked_at_month 3 ... 13
 C 2001 1 vaccinated_at_month 6 ... 5
 C 2001 0 vaccinated_at_month 3 ... 2

I want to be able to remove the gender column and collapse all values into sums based on the previous categorical columns.

I have tried

df_new = df.groupby(['country', 'year', 'gender', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

However, df_new still preserves the gender column. I am trying to get this outcome:

country year measure value0 ... value12
 A 2000 vaccinated_at_month 11 (=2+4+5) ... 8 (=1+3+4)
 B 2000 vaccinated_at_month 13 ... 12
 B 2000 walked_at_month 3 ... 13
 C 2001 vaccinated_at_month 9 (=6+3) ... 7 (=5+2)
 C 2001 vaccinated_at_month 3 ... 2

python pandas dataframe

share|improve this question

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

$endgroup$

country year gender measure value0 ... value12
 A 2000 1 vaccinated_at_month 2 ... 1
 B 2000 1 vaccinated_at_month 13 ... 12
 A 2000 0 vaccinated_at_month 4 ... 3
 A 2000 9 vaccinated_at_month 5 ... 4
 B 2000 0 walked_at_month 3 ... 13
 C 2001 1 vaccinated_at_month 6 ... 5
 C 2001 0 vaccinated_at_month 3 ... 2

df_new = df.groupby(['country', 'year', 'gender', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

country year measure value0 ... value12
 A 2000 vaccinated_at_month 11 (=2+4+5) ... 8 (=1+3+4)
 B 2000 vaccinated_at_month 13 ... 12
 B 2000 walked_at_month 3 ... 13
 C 2001 vaccinated_at_month 9 (=6+3) ... 7 (=5+2)
 C 2001 vaccinated_at_month 3 ... 2

share|improve this question

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

share|improve this question

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

edited Mar 25 at 18:26

Simon Larsson

588112

edited Mar 25 at 18:26

Simon Larsson

588112

asked Mar 25 at 15:32

user70182user70182

41

asked Mar 25 at 15:32

user70182user70182

41

1 Answer
1

active

oldest

votes

0

$begingroup$

This question is more appropriate at Stackoverflow since it is more of a programming question.

However, I think you almost got it working. Just remove gender from your groupby:

df_new = df.groupby(['country', 'year', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

share|improve this answer

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

$endgroup$

add a comment | 

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0

$begingroup$

This question is more appropriate at Stackoverflow since it is more of a programming question.

However, I think you almost got it working. Just remove gender from your groupby:

df_new = df.groupby(['country', 'year', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

share|improve this answer

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

$endgroup$

add a comment | 

0

$begingroup$

This question is more appropriate at Stackoverflow since it is more of a programming question.

However, I think you almost got it working. Just remove gender from your groupby:

df_new = df.groupby(['country', 'year', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

share|improve this answer

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

$endgroup$

add a comment | 

$begingroup$

This question is more appropriate at Stackoverflow since it is more of a programming question.

However, I think you almost got it working. Just remove gender from your groupby:

df_new = df.groupby(['country', 'year', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

share|improve this answer

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

$endgroup$

df_new = df.groupby(['country', 'year', 'measure'])['value0', ... 'value12'].apply(lambda x : x.astype(float).sum())

share|improve this answer

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112

answered Mar 25 at 16:46

Simon LarssonSimon Larsson

588112