Mixing
change column value based on multiple conditions
I've seen a lot of posts similar but none seem to answer this question:
I have a data frame with multiple columns. Lets say A, B and C
I want to change column A's value based on conditions on A, B and C
I've got this so far but not working.
df=df.loc[(df['A']=='Harry')
& (df['B']=='George')
& (df['C']>'2019'),'A']=='Matt'
So if A is equal to Harry, and B is equal to George and C is greater than 2019, then change A to Matt
Anyone see what I've done wrong?
pandas
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
add a comment |
I've seen a lot of posts similar but none seem to answer this question:
I have a data frame with multiple columns. Lets say A, B and C
I want to change column A's value based on conditions on A, B and C
I've got this so far but not working.
df=df.loc[(df['A']=='Harry')
& (df['B']=='George')
& (df['C']>'2019'),'A']=='Matt'
So if A is equal to Harry, and B is equal to George and C is greater than 2019, then change A to Matt
Anyone see what I've done wrong?
pandas
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
add a comment |
I've seen a lot of posts similar but none seem to answer this question:
I have a data frame with multiple columns. Lets say A, B and C
I want to change column A's value based on conditions on A, B and C
I've got this so far but not working.
df=df.loc[(df['A']=='Harry')
& (df['B']=='George')
& (df['C']>'2019'),'A']=='Matt'
So if A is equal to Harry, and B is equal to George and C is greater than 2019, then change A to Matt
Anyone see what I've done wrong?
pandas
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
I've seen a lot of posts similar but none seem to answer this question:
I have a data frame with multiple columns. Lets say A, B and C
I want to change column A's value based on conditions on A, B and C
I've got this so far but not working.
df=df.loc[(df['A']=='Harry')
& (df['B']=='George')
& (df['C']>'2019'),'A']=='Matt'
So if A is equal to Harry, and B is equal to George and C is greater than 2019, then change A to Matt
Anyone see what I've done wrong?
pandas
pandas
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
asked Nov 21 '18 at 11:05
fred.schwartzfred.schwartz
3328
3328
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You are really close, assign value Matt to filtered A by boolean masks:
df.loc[(df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'),'A'] = 'Matt'
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
add a comment |
You can also use np.where
df['A'] = np.where((df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'), 'Matt', df['A'])
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
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
You are really close, assign value Matt to filtered A by boolean masks:
df.loc[(df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'),'A'] = 'Matt'
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
add a comment |
You are really close, assign value Matt to filtered A by boolean masks:
df.loc[(df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'),'A'] = 'Matt'
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
add a comment |
You are really close, assign value Matt to filtered A by boolean masks:
df.loc[(df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'),'A'] = 'Matt'
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
You are really close, assign value Matt to filtered A by boolean masks:
df.loc[(df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'),'A'] = 'Matt'
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
answered Nov 21 '18 at 11:06
jezraeljezrael
334k25277353
334k25277353
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
add a comment |
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
1
1
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
It worked!!! thanks a lot for helping. I've been staring at that for hours. I'll accept answer once the time limit has passed. Many thanks!!
– fred.schwartz
Nov 21 '18 at 11:10
add a comment |
You can also use np.where
df['A'] = np.where((df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'), 'Matt', df['A'])
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
add a comment |
You can also use np.where
df['A'] = np.where((df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'), 'Matt', df['A'])
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
add a comment |
You can also use np.where
df['A'] = np.where((df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'), 'Matt', df['A'])
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
You can also use np.where
df['A'] = np.where((df['A']=='Harry') & (df['B']=='George') & (df['C']>'2019'), 'Matt', df['A'])
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
answered Nov 21 '18 at 11:30
Mohit MotwaniMohit Motwani
1,4741522
1,4741522
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
add a comment |
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
1
1
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
Good to know, many thanks for this suggestion
– fred.schwartz
Nov 21 '18 at 11:42
add a comment |
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