Python: how to drop duplicates with duplicates?
1
I have a dataframe like the following
df
Name Y
0 A 1
1 A 0
2 B 0
3 B 0
5 C 1
I want to drop the duplicates of Name and keep the ones that have Y=1 such as:
df
Name Y
0 A 1
1 B 0
2 C 1
python pandas
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
add a comment |
1
I have a dataframe like the following
df
Name Y
0 A 1
1 A 0
2 B 0
3 B 0
5 C 1
I want to drop the duplicates of Name and keep the ones that have Y=1 such as:
df
Name Y
0 A 1
1 B 0
2 C 1
python pandas
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
add a comment |
1
1
1
0
I have a dataframe like the following
df
Name Y
0 A 1
1 A 0
2 B 0
3 B 0
5 C 1
I want to drop the duplicates of Name and keep the ones that have Y=1 such as:
df
Name Y
0 A 1
1 B 0
2 C 1
python pandas
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
I have a dataframe like the following
df
Name Y
0 A 1
1 A 0
2 B 0
3 B 0
5 C 1
I want to drop the duplicates of Name and keep the ones that have Y=1 such as:
df
Name Y
0 A 1
1 B 0
2 C 1
python pandas
python pandas
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
asked Nov 16 '18 at 10:53
emaxemax
1,20531235
1,20531235
add a comment |
add a comment |
4 Answers
4
active
oldest
votes
2
Use drop_duplicates method,
df.sort_values('Y', ascending= False).drop_duplicates(subset=['Name'])
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
add a comment |
2
groupby + max
Assuming your Y series consists only of 0 and 1 values:
res = df.groupby('Name', as_index=False)['Y'].max()
print(res)
Name Y
0 A 1
1 B 0
2 C 1
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
add a comment |
1
Does 'Y' column contain only 0-1? In that case, you can try the following :
df = df.sort_values(['Y'], ascending= False)
df = df.drop_duplicates(['Name'])
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
add a comment |
0
Try this:
In [2358]: df.groupby('Name')['Y'].max()
Out[2358]:
Name
A 1
B 0
C 1
Name: Y, dtype: int64
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
add a comment |
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
Use drop_duplicates method,
df.sort_values('Y', ascending= False).drop_duplicates(subset=['Name'])
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
add a comment |
2
Use drop_duplicates method,
df.sort_values('Y', ascending= False).drop_duplicates(subset=['Name'])
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
add a comment |
2
2
2
Use drop_duplicates method,
df.sort_values('Y', ascending= False).drop_duplicates(subset=['Name'])
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
Use drop_duplicates method,
df.sort_values('Y', ascending= False).drop_duplicates(subset=['Name'])
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
edited Nov 16 '18 at 11:43
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
answered Nov 16 '18 at 10:56
AlessandroAlessandro
480617
480617
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
add a comment |
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
1
1
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
drop_duplicates has by default keep ='first' , so your proposition will keep 0's instead of 1's. You should either sort in descending ordrer , or add a keep='last' argument in drop duplicates
– Matina G
Nov 16 '18 at 11:12
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
Agree, will etit
– Alessandro
Nov 16 '18 at 11:43
add a comment |
2
groupby + max
Assuming your Y series consists only of 0 and 1 values:
res = df.groupby('Name', as_index=False)['Y'].max()
print(res)
Name Y
0 A 1
1 B 0
2 C 1
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
add a comment |
2
groupby + max
Assuming your Y series consists only of 0 and 1 values:
res = df.groupby('Name', as_index=False)['Y'].max()
print(res)
Name Y
0 A 1
1 B 0
2 C 1
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
add a comment |
2
2
2
groupby + max
Assuming your Y series consists only of 0 and 1 values:
res = df.groupby('Name', as_index=False)['Y'].max()
print(res)
Name Y
0 A 1
1 B 0
2 C 1
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
groupby + max
Assuming your Y series consists only of 0 and 1 values:
res = df.groupby('Name', as_index=False)['Y'].max()
print(res)
Name Y
0 A 1
1 B 0
2 C 1
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
answered Nov 16 '18 at 11:07
jppjpp
102k2165116
102k2165116
add a comment |
add a comment |
1
Does 'Y' column contain only 0-1? In that case, you can try the following :
df = df.sort_values(['Y'], ascending= False)
df = df.drop_duplicates(['Name'])
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
add a comment |
1
Does 'Y' column contain only 0-1? In that case, you can try the following :
df = df.sort_values(['Y'], ascending= False)
df = df.drop_duplicates(['Name'])
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
add a comment |
1
1
1
Does 'Y' column contain only 0-1? In that case, you can try the following :
df = df.sort_values(['Y'], ascending= False)
df = df.drop_duplicates(['Name'])
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
Does 'Y' column contain only 0-1? In that case, you can try the following :
df = df.sort_values(['Y'], ascending= False)
df = df.drop_duplicates(['Name'])
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
answered Nov 16 '18 at 11:09
Matina GMatina G
612213
612213
add a comment |
add a comment |
0
Try this:
In [2358]: df.groupby('Name')['Y'].max()
Out[2358]:
Name
A 1
B 0
C 1
Name: Y, dtype: int64
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
add a comment |
0
Try this:
In [2358]: df.groupby('Name')['Y'].max()
Out[2358]:
Name
A 1
B 0
C 1
Name: Y, dtype: int64
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
add a comment |
0
0
0
Try this:
In [2358]: df.groupby('Name')['Y'].max()
Out[2358]:
Name
A 1
B 0
C 1
Name: Y, dtype: int64
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
Try this:
In [2358]: df.groupby('Name')['Y'].max()
Out[2358]:
Name
A 1
B 0
C 1
Name: Y, dtype: int64
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
answered Nov 16 '18 at 11:08
Mayank PorwalMayank Porwal
5,0182725
5,0182725
add a comment |
add a comment |
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