setting up a new layout with unique values of multiple columns from a data frame in python
0
I have the following data frame:
df = pd.DataFrame( 'group' : ["A","A","A","B","B","B"],
'unit_name' : ["BA234","BA744","BA240","BB237","BB740","BB27"],
'value' :["50","10","180","10","10","660"],
'set0' :["Country","Country","Country","Country","Country","Country"],
'set_1' :["size1","size1","size2","size3","size4","size3"],
'set_2' :["size12","size12","size12","size9","size13","size13"],
'set_3' :["size14","size14","size15","NO","NO","NO"],
'set_4' :["NO","NO","NO","Size25","Size25","Size27"],
'set_5' :["NO","NO","NO","NO","NO","NO"]
)
Without hard coding:
I want to get a new resulting data frame/layout so that each value of the "group" column is repeated with the number unique values of "set_" related columns ("NO" values should be disregarded) and align horizontally.
Since I am not able to share any image of the desired output, I am sharing the transposed form of it.
The resulting data frame should look like the transposed form of the following:
df_new = pd.DataFrame( 'Group' : ["A","A","A","A","A","A","B","B","B","B","B","B","B"],
'sets' : ["Country","Size1","Size2","Size12","Size14","Size15","Country","Size3","Size4","Size9","Size13","Size25","Size27"],
)
Could you please help regarding the issue?
python-3.x pandas
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
add a comment |
0
I have the following data frame:
df = pd.DataFrame( 'group' : ["A","A","A","B","B","B"],
'unit_name' : ["BA234","BA744","BA240","BB237","BB740","BB27"],
'value' :["50","10","180","10","10","660"],
'set0' :["Country","Country","Country","Country","Country","Country"],
'set_1' :["size1","size1","size2","size3","size4","size3"],
'set_2' :["size12","size12","size12","size9","size13","size13"],
'set_3' :["size14","size14","size15","NO","NO","NO"],
'set_4' :["NO","NO","NO","Size25","Size25","Size27"],
'set_5' :["NO","NO","NO","NO","NO","NO"]
)
Without hard coding:
I want to get a new resulting data frame/layout so that each value of the "group" column is repeated with the number unique values of "set_" related columns ("NO" values should be disregarded) and align horizontally.
Since I am not able to share any image of the desired output, I am sharing the transposed form of it.
The resulting data frame should look like the transposed form of the following:
df_new = pd.DataFrame( 'Group' : ["A","A","A","A","A","A","B","B","B","B","B","B","B"],
'sets' : ["Country","Size1","Size2","Size12","Size14","Size15","Country","Size3","Size4","Size9","Size13","Size25","Size27"],
)
Could you please help regarding the issue?
python-3.x pandas
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
1
Why not show us the exact desired output rather than a transposed version? Or isdf_newexactly what you want?
– jpp
Nov 12 '18 at 16:59
add a comment |
0
0
0
I have the following data frame:
df = pd.DataFrame( 'group' : ["A","A","A","B","B","B"],
'unit_name' : ["BA234","BA744","BA240","BB237","BB740","BB27"],
'value' :["50","10","180","10","10","660"],
'set0' :["Country","Country","Country","Country","Country","Country"],
'set_1' :["size1","size1","size2","size3","size4","size3"],
'set_2' :["size12","size12","size12","size9","size13","size13"],
'set_3' :["size14","size14","size15","NO","NO","NO"],
'set_4' :["NO","NO","NO","Size25","Size25","Size27"],
'set_5' :["NO","NO","NO","NO","NO","NO"]
)
Without hard coding:
I want to get a new resulting data frame/layout so that each value of the "group" column is repeated with the number unique values of "set_" related columns ("NO" values should be disregarded) and align horizontally.
Since I am not able to share any image of the desired output, I am sharing the transposed form of it.
The resulting data frame should look like the transposed form of the following:
df_new = pd.DataFrame( 'Group' : ["A","A","A","A","A","A","B","B","B","B","B","B","B"],
'sets' : ["Country","Size1","Size2","Size12","Size14","Size15","Country","Size3","Size4","Size9","Size13","Size25","Size27"],
)
Could you please help regarding the issue?
python-3.x pandas
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
I have the following data frame:
df = pd.DataFrame( 'group' : ["A","A","A","B","B","B"],
'unit_name' : ["BA234","BA744","BA240","BB237","BB740","BB27"],
'value' :["50","10","180","10","10","660"],
'set0' :["Country","Country","Country","Country","Country","Country"],
'set_1' :["size1","size1","size2","size3","size4","size3"],
'set_2' :["size12","size12","size12","size9","size13","size13"],
'set_3' :["size14","size14","size15","NO","NO","NO"],
'set_4' :["NO","NO","NO","Size25","Size25","Size27"],
'set_5' :["NO","NO","NO","NO","NO","NO"]
)
Without hard coding:
I want to get a new resulting data frame/layout so that each value of the "group" column is repeated with the number unique values of "set_" related columns ("NO" values should be disregarded) and align horizontally.
Since I am not able to share any image of the desired output, I am sharing the transposed form of it.
The resulting data frame should look like the transposed form of the following:
df_new = pd.DataFrame( 'Group' : ["A","A","A","A","A","A","B","B","B","B","B","B","B"],
'sets' : ["Country","Size1","Size2","Size12","Size14","Size15","Country","Size3","Size4","Size9","Size13","Size25","Size27"],
)
Could you please help regarding the issue?
python-3.x pandas
python-3.x pandas
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
asked Nov 12 '18 at 16:54
nkltkfnkltkf
113
113
1
Why not show us the exact desired output rather than a transposed version? Or isdf_newexactly what you want?
– jpp
Nov 12 '18 at 16:59
add a comment |
1
Why not show us the exact desired output rather than a transposed version? Or isdf_newexactly what you want?
– jpp
Nov 12 '18 at 16:59
1
1
Why not show us the exact desired output rather than a transposed version? Or is
df_new exactly what you want?– jpp
Nov 12 '18 at 16:59
Why not show us the exact desired output rather than a transposed version? Or is
df_new exactly what you want?– jpp
Nov 12 '18 at 16:59
add a comment |
2 Answers
2
active
oldest
votes
2
You can use the following, which stacks your dataframe, filtered where the columns have set in the name, and drops the duplicates:
df_new = (df.set_index('group').replace('NO',np.nan)
.filter(like='set').stack()
.reset_index('group')
.drop_duplicates())
>>> df_new
group 0
set0 A Country
set_1 A size1
set_2 A size12
set_3 A size14
set_1 A size2
set_3 A size15
set0 B Country
set_1 B size3
set_2 B size9
set_4 B Size25
set_1 B size4
set_2 B size13
set_4 B Size27
answered Nov 12 '18 at 17:03
saculsacul
30k41740
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
1
With pd.melt
import pandas as pd
(pd.melt(df,
id_vars='group',
value_name='Country', value_vars=[x for x in df.columns if 'set' in x])
.drop(columns='variable')
.drop_duplicates()
.sort_values('group')
.query('Country != "NO"')
)
Output:
group Country
0 A Country
6 A size1
8 A size2
12 A size12
18 A size14
20 A size15
3 B Country
9 B size3
10 B size4
15 B size9
16 B size13
27 B Size25
29 B Size27
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
You can use the following, which stacks your dataframe, filtered where the columns have set in the name, and drops the duplicates:
df_new = (df.set_index('group').replace('NO',np.nan)
.filter(like='set').stack()
.reset_index('group')
.drop_duplicates())
>>> df_new
group 0
set0 A Country
set_1 A size1
set_2 A size12
set_3 A size14
set_1 A size2
set_3 A size15
set0 B Country
set_1 B size3
set_2 B size9
set_4 B Size25
set_1 B size4
set_2 B size13
set_4 B Size27
answered Nov 12 '18 at 17:03
saculsacul
30k41740
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
2
You can use the following, which stacks your dataframe, filtered where the columns have set in the name, and drops the duplicates:
df_new = (df.set_index('group').replace('NO',np.nan)
.filter(like='set').stack()
.reset_index('group')
.drop_duplicates())
>>> df_new
group 0
set0 A Country
set_1 A size1
set_2 A size12
set_3 A size14
set_1 A size2
set_3 A size15
set0 B Country
set_1 B size3
set_2 B size9
set_4 B Size25
set_1 B size4
set_2 B size13
set_4 B Size27
answered Nov 12 '18 at 17:03
saculsacul
30k41740
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
2
2
2
You can use the following, which stacks your dataframe, filtered where the columns have set in the name, and drops the duplicates:
df_new = (df.set_index('group').replace('NO',np.nan)
.filter(like='set').stack()
.reset_index('group')
.drop_duplicates())
>>> df_new
group 0
set0 A Country
set_1 A size1
set_2 A size12
set_3 A size14
set_1 A size2
set_3 A size15
set0 B Country
set_1 B size3
set_2 B size9
set_4 B Size25
set_1 B size4
set_2 B size13
set_4 B Size27
answered Nov 12 '18 at 17:03
saculsacul
30k41740
You can use the following, which stacks your dataframe, filtered where the columns have set in the name, and drops the duplicates:
df_new = (df.set_index('group').replace('NO',np.nan)
.filter(like='set').stack()
.reset_index('group')
.drop_duplicates())
>>> df_new
group 0
set0 A Country
set_1 A size1
set_2 A size12
set_3 A size14
set_1 A size2
set_3 A size15
set0 B Country
set_1 B size3
set_2 B size9
set_4 B Size25
set_1 B size4
set_2 B size13
set_4 B Size27
answered Nov 12 '18 at 17:03
saculsacul
30k41740
answered Nov 12 '18 at 17:03
saculsacul
30k41740
answered Nov 12 '18 at 17:03
saculsacul
30k41740
answered Nov 12 '18 at 17:03
saculsacul
30k41740
30k41740
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
1
With pd.melt
import pandas as pd
(pd.melt(df,
id_vars='group',
value_name='Country', value_vars=[x for x in df.columns if 'set' in x])
.drop(columns='variable')
.drop_duplicates()
.sort_values('group')
.query('Country != "NO"')
)
Output:
group Country
0 A Country
6 A size1
8 A size2
12 A size12
18 A size14
20 A size15
3 B Country
9 B size3
10 B size4
15 B size9
16 B size13
27 B Size25
29 B Size27
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
1
With pd.melt
import pandas as pd
(pd.melt(df,
id_vars='group',
value_name='Country', value_vars=[x for x in df.columns if 'set' in x])
.drop(columns='variable')
.drop_duplicates()
.sort_values('group')
.query('Country != "NO"')
)
Output:
group Country
0 A Country
6 A size1
8 A size2
12 A size12
18 A size14
20 A size15
3 B Country
9 B size3
10 B size4
15 B size9
16 B size13
27 B Size25
29 B Size27
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
1
1
1
With pd.melt
import pandas as pd
(pd.melt(df,
id_vars='group',
value_name='Country', value_vars=[x for x in df.columns if 'set' in x])
.drop(columns='variable')
.drop_duplicates()
.sort_values('group')
.query('Country != "NO"')
)
Output:
group Country
0 A Country
6 A size1
8 A size2
12 A size12
18 A size14
20 A size15
3 B Country
9 B size3
10 B size4
15 B size9
16 B size13
27 B Size25
29 B Size27
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
With pd.melt
import pandas as pd
(pd.melt(df,
id_vars='group',
value_name='Country', value_vars=[x for x in df.columns if 'set' in x])
.drop(columns='variable')
.drop_duplicates()
.sort_values('group')
.query('Country != "NO"')
)
Output:
group Country
0 A Country
6 A size1
8 A size2
12 A size12
18 A size14
20 A size15
3 B Country
9 B size3
10 B size4
15 B size9
16 B size13
27 B Size25
29 B Size27
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
edited Nov 12 '18 at 17:15
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
answered Nov 12 '18 at 17:04
ALollzALollz
11.7k31436
11.7k31436
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
Thanks a lot,works perfectly!
– nkltkf
Nov 12 '18 at 18:30
add a comment |
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1
Why not show us the exact desired output rather than a transposed version? Or is
df_newexactly what you want?– jpp
Nov 12 '18 at 16:59