pandas get dummies for column with list
0
Input:-
empNo name
1234 [ AB, DE ]
5678 [ FG, IJ ]
Command:-
dataFrame = dataFrame.join(dataFrame.name.str.join('|').str.get_dummies().add_prefix('dummy_name_'))
The above command brings dummy "for each character of the column name"
Output:-
empNo name dummy_name_A dummy_name_B dummy_name_D dummy_name_E dummy_name_F dummy_name_G dummy_name_I dummy_name_J
1234 [ AB, DE ] 1 1 1 1 0 0 0 0
5678 [ FG, IJ ] 0 0 0 0 1 1 1 1
Expected:-
empNo name dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
1234 [ AB, DE ] 1 1 0 0
5678 [ FG, IJ ] 0 0 1 1
python pandas dataframe
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
add a comment |
0
Input:-
empNo name
1234 [ AB, DE ]
5678 [ FG, IJ ]
Command:-
dataFrame = dataFrame.join(dataFrame.name.str.join('|').str.get_dummies().add_prefix('dummy_name_'))
The above command brings dummy "for each character of the column name"
Output:-
empNo name dummy_name_A dummy_name_B dummy_name_D dummy_name_E dummy_name_F dummy_name_G dummy_name_I dummy_name_J
1234 [ AB, DE ] 1 1 1 1 0 0 0 0
5678 [ FG, IJ ] 0 0 0 0 1 1 1 1
Expected:-
empNo name dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
1234 [ AB, DE ] 1 1 0 0
5678 [ FG, IJ ] 0 0 1 1
python pandas dataframe
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
4
Strange. Can you share your dataframe like this:print(dataFrame.to_dict())and post result here.
– Anton vBR
Nov 25 '18 at 19:03
4
If it is huge then use:print(dataFrame.head(2).to_dict())to limit it to two rows.
– Anton vBR
Nov 25 '18 at 19:35
1
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46
add a comment |
0
0
0
Input:-
empNo name
1234 [ AB, DE ]
5678 [ FG, IJ ]
Command:-
dataFrame = dataFrame.join(dataFrame.name.str.join('|').str.get_dummies().add_prefix('dummy_name_'))
The above command brings dummy "for each character of the column name"
Output:-
empNo name dummy_name_A dummy_name_B dummy_name_D dummy_name_E dummy_name_F dummy_name_G dummy_name_I dummy_name_J
1234 [ AB, DE ] 1 1 1 1 0 0 0 0
5678 [ FG, IJ ] 0 0 0 0 1 1 1 1
Expected:-
empNo name dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
1234 [ AB, DE ] 1 1 0 0
5678 [ FG, IJ ] 0 0 1 1
python pandas dataframe
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
Input:-
empNo name
1234 [ AB, DE ]
5678 [ FG, IJ ]
Command:-
dataFrame = dataFrame.join(dataFrame.name.str.join('|').str.get_dummies().add_prefix('dummy_name_'))
The above command brings dummy "for each character of the column name"
Output:-
empNo name dummy_name_A dummy_name_B dummy_name_D dummy_name_E dummy_name_F dummy_name_G dummy_name_I dummy_name_J
1234 [ AB, DE ] 1 1 1 1 0 0 0 0
5678 [ FG, IJ ] 0 0 0 0 1 1 1 1
Expected:-
empNo name dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
1234 [ AB, DE ] 1 1 0 0
5678 [ FG, IJ ] 0 0 1 1
python pandas dataframe
python pandas dataframe
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
edited Nov 25 '18 at 19:00
Mayank Porwal
4,9802725
4,9802725
asked Nov 25 '18 at 18:58
JennyJenny
166
asked Nov 25 '18 at 18:58
JennyJenny
166
asked Nov 25 '18 at 18:58
JennyJenny
166
166
4
Strange. Can you share your dataframe like this:print(dataFrame.to_dict())and post result here.
– Anton vBR
Nov 25 '18 at 19:03
4
If it is huge then use:print(dataFrame.head(2).to_dict())to limit it to two rows.
– Anton vBR
Nov 25 '18 at 19:35
1
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46
add a comment |
4
Strange. Can you share your dataframe like this:print(dataFrame.to_dict())and post result here.
– Anton vBR
Nov 25 '18 at 19:03
4
If it is huge then use:print(dataFrame.head(2).to_dict())to limit it to two rows.
– Anton vBR
Nov 25 '18 at 19:35
1
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46
4
4
Strange. Can you share your dataframe like this:
print(dataFrame.to_dict()) and post result here.– Anton vBR
Nov 25 '18 at 19:03
Strange. Can you share your dataframe like this:
print(dataFrame.to_dict()) and post result here.– Anton vBR
Nov 25 '18 at 19:03
4
4
If it is huge then use:
print(dataFrame.head(2).to_dict()) to limit it to two rows.– Anton vBR
Nov 25 '18 at 19:35
If it is huge then use:
print(dataFrame.head(2).to_dict()) to limit it to two rows.– Anton vBR
Nov 25 '18 at 19:35
1
1
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46
add a comment |
1 Answer
1
active
oldest
votes
1
I think the list is not the list , so we using ast to convert the string type column back to list
import ast
df.name=df.name.apply(ast.literal_eval)
Then using str get_dummies
s=df.name.apply(pd.Series).stack().str.get_dummies().sum(level=0).add_prefix('dummy_name_')
s
dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
0 1 1 0 0
1 0 0 1 1
Then
pd.concat([df[['empNo']],s],axis=1)
The data input
df.to_dict()
{'empNo': {0: 1234, 1: 5678}, 'name': {0: ['AB', 'DE'], 1: ['FG', 'IJ']}}
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
I think the list is not the list , so we using ast to convert the string type column back to list
import ast
df.name=df.name.apply(ast.literal_eval)
Then using str get_dummies
s=df.name.apply(pd.Series).stack().str.get_dummies().sum(level=0).add_prefix('dummy_name_')
s
dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
0 1 1 0 0
1 0 0 1 1
Then
pd.concat([df[['empNo']],s],axis=1)
The data input
df.to_dict()
{'empNo': {0: 1234, 1: 5678}, 'name': {0: ['AB', 'DE'], 1: ['FG', 'IJ']}}
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
add a comment |
1
I think the list is not the list , so we using ast to convert the string type column back to list
import ast
df.name=df.name.apply(ast.literal_eval)
Then using str get_dummies
s=df.name.apply(pd.Series).stack().str.get_dummies().sum(level=0).add_prefix('dummy_name_')
s
dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
0 1 1 0 0
1 0 0 1 1
Then
pd.concat([df[['empNo']],s],axis=1)
The data input
df.to_dict()
{'empNo': {0: 1234, 1: 5678}, 'name': {0: ['AB', 'DE'], 1: ['FG', 'IJ']}}
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
add a comment |
1
1
1
I think the list is not the list , so we using ast to convert the string type column back to list
import ast
df.name=df.name.apply(ast.literal_eval)
Then using str get_dummies
s=df.name.apply(pd.Series).stack().str.get_dummies().sum(level=0).add_prefix('dummy_name_')
s
dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
0 1 1 0 0
1 0 0 1 1
Then
pd.concat([df[['empNo']],s],axis=1)
The data input
df.to_dict()
{'empNo': {0: 1234, 1: 5678}, 'name': {0: ['AB', 'DE'], 1: ['FG', 'IJ']}}
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
I think the list is not the list , so we using ast to convert the string type column back to list
import ast
df.name=df.name.apply(ast.literal_eval)
Then using str get_dummies
s=df.name.apply(pd.Series).stack().str.get_dummies().sum(level=0).add_prefix('dummy_name_')
s
dummy_name_AB dummy_name_DE dummy_name_FG dummy_name_IJ
0 1 1 0 0
1 0 0 1 1
Then
pd.concat([df[['empNo']],s],axis=1)
The data input
df.to_dict()
{'empNo': {0: 1234, 1: 5678}, 'name': {0: ['AB', 'DE'], 1: ['FG', 'IJ']}}
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
edited Nov 25 '18 at 19:32
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
answered Nov 25 '18 at 19:23
Wen-BenWen-Ben
118k83469
118k83469
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
add a comment |
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
Thanks W-B. This command brings the output as dummy_name_['AB'] , dummy_name_['DE'] , dummy_name_[ 'AB','DE' ], dummy_name_['FG'] , dummy_name_['IJ'] , dummy_name_[ 'FG','IJ' ], and one more additional column for an empty list dummy_name_ Can you please help achieving the expected result given above ?
– Jenny
Nov 25 '18 at 19:30
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
@Jenny please check above data input to see the different between mine and yours
– Wen-Ben
Nov 25 '18 at 19:32
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
I got the issue W-B. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:53
add a comment |
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4
Strange. Can you share your dataframe like this:
print(dataFrame.to_dict())and post result here.– Anton vBR
Nov 25 '18 at 19:03
4
If it is huge then use:
print(dataFrame.head(2).to_dict())to limit it to two rows.– Anton vBR
Nov 25 '18 at 19:35
1
I got the issue Anton. Had accidentally converted the datatype of the column to [ .astype(str) ]. I removed it and my earlier command works fine as you had correctly pointed out. Thanks for the help.
– Jenny
Nov 25 '18 at 19:46