how to use melt function in multiple columns in pandas?
I have a data frame which has columns like this
balance,
2016-10-5,
2016-11-8,
2017-3-7,
2018-5-29
and more. my desire output columns like this
balance,
date,
values
I tried using this script:
df= pd.melt(df,id_vars=['balance'], value_vars=['2016-10-5,2016-11-8,2017-3-7,2018-5-29'] var_name='date',value_name='values')
this code worked but columns are too many how to make all date in date columns in pandas?
python pandas
edited Nov 23 at 13:58
user10465355
1,3681411
asked Nov 23 at 5:41
subash poudel
758
add a comment |
I have a data frame which has columns like this
balance,
2016-10-5,
2016-11-8,
2017-3-7,
2018-5-29
and more. my desire output columns like this
balance,
date,
values
I tried using this script:
df= pd.melt(df,id_vars=['balance'], value_vars=['2016-10-5,2016-11-8,2017-3-7,2018-5-29'] var_name='date',value_name='values')
this code worked but columns are too many how to make all date in date columns in pandas?
python pandas
edited Nov 23 at 13:58
user10465355
1,3681411
asked Nov 23 at 5:41
subash poudel
758
add a comment |
I have a data frame which has columns like this
balance,
2016-10-5,
2016-11-8,
2017-3-7,
2018-5-29
and more. my desire output columns like this
balance,
date,
values
I tried using this script:
df= pd.melt(df,id_vars=['balance'], value_vars=['2016-10-5,2016-11-8,2017-3-7,2018-5-29'] var_name='date',value_name='values')
this code worked but columns are too many how to make all date in date columns in pandas?
python pandas
edited Nov 23 at 13:58
user10465355
1,3681411
asked Nov 23 at 5:41
subash poudel
758
I have a data frame which has columns like this
balance,
2016-10-5,
2016-11-8,
2017-3-7,
2018-5-29
and more. my desire output columns like this
balance,
date,
values
I tried using this script:
df= pd.melt(df,id_vars=['balance'], value_vars=['2016-10-5,2016-11-8,2017-3-7,2018-5-29'] var_name='date',value_name='values')
this code worked but columns are too many how to make all date in date columns in pandas?
python pandas
python pandas
edited Nov 23 at 13:58
user10465355
1,3681411
asked Nov 23 at 5:41
subash poudel
758
edited Nov 23 at 13:58
user10465355
1,3681411
asked Nov 23 at 5:41
subash poudel
758
edited Nov 23 at 13:58
user10465355
1,3681411
edited Nov 23 at 13:58
user10465355
1,3681411
edited Nov 23 at 13:58
user10465355
1,3681411
1,3681411
asked Nov 23 at 5:41
subash poudel
758
asked Nov 23 at 5:41
subash poudel
758
asked Nov 23 at 5:41
subash poudel
758
758
add a comment |
add a comment |
1 Answer
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oldest
votes
You can remove parameter value_vars, check function melt:
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
df= pd.melt(df,id_vars='balance', var_name='date',value_name='values')
answered Nov 23 at 6:19
jezrael
319k22258337
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
You can remove parameter value_vars, check function melt:
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
df= pd.melt(df,id_vars='balance', var_name='date',value_name='values')
answered Nov 23 at 6:19
jezrael
319k22258337
add a comment |
You can remove parameter value_vars, check function melt:
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
df= pd.melt(df,id_vars='balance', var_name='date',value_name='values')
answered Nov 23 at 6:19
jezrael
319k22258337
add a comment |
You can remove parameter value_vars, check function melt:
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
df= pd.melt(df,id_vars='balance', var_name='date',value_name='values')
answered Nov 23 at 6:19
jezrael
319k22258337
You can remove parameter value_vars, check function melt:
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
df= pd.melt(df,id_vars='balance', var_name='date',value_name='values')
answered Nov 23 at 6:19
jezrael
319k22258337
answered Nov 23 at 6:19
jezrael
319k22258337
answered Nov 23 at 6:19
jezrael
319k22258337
answered Nov 23 at 6:19
jezrael
319k22258337
319k22258337
add a comment |
add a comment |
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