How to create a df that gets sum of columns based on a groupby column? The Next CEO of Stack Overflow2019 Community Moderator ElectionCreate a new column based on two columns from two different dataframesHow to sum values grouped by two columns in pandasCreate new data frames from existing data frame based on unique column valuesLow silhouette coefficientShould I use pandas get_dummies and create additional columns or use my own encoding code that keeps 1 column?How to calculate Cumulative Sum with Groupby in Python?How to create a new column based on two other columns in Pandas?how to update column in data frame based on conditionPandas merge column duplicate and sum valueMultiple filtering pandas columns based on values in another column
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How to create a df that gets sum of columns based on a groupby column?
The Next CEO of Stack Overflow2019 Community Moderator ElectionCreate a new column based on two columns from two different dataframesHow to sum values grouped by two columns in pandasCreate new data frames from existing data frame based on unique column valuesLow silhouette coefficientShould I use pandas get_dummies and create additional columns or use my own encoding code that keeps 1 column?How to calculate Cumulative Sum with Groupby in Python?How to create a new column based on two other columns in Pandas?how to update column in data frame based on conditionPandas merge column duplicate and sum valueMultiple filtering pandas columns based on values in another column
$begingroup$
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
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
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
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
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
python pandas dataframe
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
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
edited Mar 25 at 18:26
Simon Larsson
588112
588112
asked Mar 25 at 15:32
user70182user70182
41
asked Mar 25 at 15:32
user70182user70182
41
asked Mar 25 at 15:32
user70182user70182
41
41
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
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())
answered Mar 25 at 16:46
Simon LarssonSimon Larsson
588112
$endgroup$
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
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())
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())
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())
answered Mar 25 at 16:46
Simon LarssonSimon Larsson
588112
$endgroup$
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())
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
answered Mar 25 at 16:46
Simon LarssonSimon Larsson
588112
588112
add a comment |
add a comment |
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