how to create a column hierarchical index in pandas data frame
0
I have two data frames which look like following
Datframe A:
dataDate name prediction
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
I want to transform my data frame while creating a hierarchical index in the columns so that I can access a couple of columns at the same time. So, for example:-
Dataframe B:
dataDate prediction
Group pred
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
Data frame B has just two columns 'dataDate' and 'prediction' and prediction has then 'level 1' two columns 'Group' and 'pred'. Now I can access them by just prediction.
Please help me to transform Dataframe B from Dataframe A and vice-versa with pandas?
python pandas pivot
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
add a comment |
0
I have two data frames which look like following
Datframe A:
dataDate name prediction
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
I want to transform my data frame while creating a hierarchical index in the columns so that I can access a couple of columns at the same time. So, for example:-
Dataframe B:
dataDate prediction
Group pred
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
Data frame B has just two columns 'dataDate' and 'prediction' and prediction has then 'level 1' two columns 'Group' and 'pred'. Now I can access them by just prediction.
Please help me to transform Dataframe B from Dataframe A and vice-versa with pandas?
python pandas pivot
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
add a comment |
0
0
0
I have two data frames which look like following
Datframe A:
dataDate name prediction
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
I want to transform my data frame while creating a hierarchical index in the columns so that I can access a couple of columns at the same time. So, for example:-
Dataframe B:
dataDate prediction
Group pred
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
Data frame B has just two columns 'dataDate' and 'prediction' and prediction has then 'level 1' two columns 'Group' and 'pred'. Now I can access them by just prediction.
Please help me to transform Dataframe B from Dataframe A and vice-versa with pandas?
python pandas pivot
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
I have two data frames which look like following
Datframe A:
dataDate name prediction
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
I want to transform my data frame while creating a hierarchical index in the columns so that I can access a couple of columns at the same time. So, for example:-
Dataframe B:
dataDate prediction
Group pred
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
Data frame B has just two columns 'dataDate' and 'prediction' and prediction has then 'level 1' two columns 'Group' and 'pred'. Now I can access them by just prediction.
Please help me to transform Dataframe B from Dataframe A and vice-versa with pandas?
python pandas pivot
python pandas pivot
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
edited Jan 2 at 12:15
Manu Sharma
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
asked Jan 2 at 10:28
Manu SharmaManu Sharma
354517
354517
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
3
Use set_index for first column to DatatimeIndex if necessary and then assign new MultiIndex by MultiIndex.from_product:
df = df.set_index('dataDate')
df.columns = pd.MultiIndex.from_product([['prediction'],['Group','pred']])
#alternative
#df.columns = [['prediction'] * len(df.columns),['Group','pred']]
print (df)
prediction
Group pred
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
For converting I suggest not change columns names in first level, only define new level Group:
df1 = df.set_index('dataDate')
df1.columns = pd.MultiIndex.from_product([['Group'],df1.columns])
print (df1)
Group
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
df = df1['Group'].copy()
print (df)
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible createMultiIndexthis new way?
– jezrael
Jan 2 at 12:19
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
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
3
Use set_index for first column to DatatimeIndex if necessary and then assign new MultiIndex by MultiIndex.from_product:
df = df.set_index('dataDate')
df.columns = pd.MultiIndex.from_product([['prediction'],['Group','pred']])
#alternative
#df.columns = [['prediction'] * len(df.columns),['Group','pred']]
print (df)
prediction
Group pred
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
For converting I suggest not change columns names in first level, only define new level Group:
df1 = df.set_index('dataDate')
df1.columns = pd.MultiIndex.from_product([['Group'],df1.columns])
print (df1)
Group
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
df = df1['Group'].copy()
print (df)
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible createMultiIndexthis new way?
– jezrael
Jan 2 at 12:19
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
add a comment |
3
Use set_index for first column to DatatimeIndex if necessary and then assign new MultiIndex by MultiIndex.from_product:
df = df.set_index('dataDate')
df.columns = pd.MultiIndex.from_product([['prediction'],['Group','pred']])
#alternative
#df.columns = [['prediction'] * len(df.columns),['Group','pred']]
print (df)
prediction
Group pred
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
For converting I suggest not change columns names in first level, only define new level Group:
df1 = df.set_index('dataDate')
df1.columns = pd.MultiIndex.from_product([['Group'],df1.columns])
print (df1)
Group
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
df = df1['Group'].copy()
print (df)
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible createMultiIndexthis new way?
– jezrael
Jan 2 at 12:19
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
add a comment |
3
3
3
Use set_index for first column to DatatimeIndex if necessary and then assign new MultiIndex by MultiIndex.from_product:
df = df.set_index('dataDate')
df.columns = pd.MultiIndex.from_product([['prediction'],['Group','pred']])
#alternative
#df.columns = [['prediction'] * len(df.columns),['Group','pred']]
print (df)
prediction
Group pred
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
For converting I suggest not change columns names in first level, only define new level Group:
df1 = df.set_index('dataDate')
df1.columns = pd.MultiIndex.from_product([['Group'],df1.columns])
print (df1)
Group
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
df = df1['Group'].copy()
print (df)
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
Use set_index for first column to DatatimeIndex if necessary and then assign new MultiIndex by MultiIndex.from_product:
df = df.set_index('dataDate')
df.columns = pd.MultiIndex.from_product([['prediction'],['Group','pred']])
#alternative
#df.columns = [['prediction'] * len(df.columns),['Group','pred']]
print (df)
prediction
Group pred
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
For converting I suggest not change columns names in first level, only define new level Group:
df1 = df.set_index('dataDate')
df1.columns = pd.MultiIndex.from_product([['Group'],df1.columns])
print (df1)
Group
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
df = df1['Group'].copy()
print (df)
name prediction
dataDate
2018-09-30 A 2.30
2018-10-01 A 1.51
2018-10-02 A 2.08
2018-10-03 A 1.82
2018-09-30 B 0.96
2018-10-01 B 6.52
2018-10-02 B 9.21
2018-10-03 B 17.43
2018-09-30 C 6.89
2018-10-01 C 6.10
2018-10-02 C 5.53
2018-10-03 C 1.91
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
edited Jan 2 at 12:19
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
answered Jan 2 at 10:31
jezraeljezrael
345k25300371
345k25300371
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible createMultiIndexthis new way?
– jezrael
Jan 2 at 12:19
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
add a comment |
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible createMultiIndexthis new way?
– jezrael
Jan 2 at 12:19
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
Thank you so much. Also pls help me with, if I have a structure of data frame B, which is 'df' in your answer, how can I transform it in the initial data frame
– Manu Sharma
Jan 2 at 12:18
@ManuSharma - Please check edit, is possible create
MultiIndex this new way?– jezrael
Jan 2 at 12:19
@ManuSharma - Please check edit, is possible create
MultiIndex this new way?– jezrael
Jan 2 at 12:19
1
1
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
It Worked like a charm. Thanks
– Manu Sharma
Jan 2 at 15:27
add a comment |
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