Mixing
Compare column values based on list in Pandas dataframe
2
I have 2 dataframes as below:
df1:
ID list value listA valueA listB valueB
1 list1 D1 list1 D1 list1 D1
2 list2 D1
3 list1 D3 list2 D3
4 list2 D1 list2 D1 list1 D3
5 list2 D2 list2 D2 list2 D2
6 list2 D3 list1 D3 list1 D4
df2:
list1 list2
D1 456
D2 D2
33 D31
D4 245
EE D5
D5 D6
I'm trying to generate a final df based on the below validations
-> If value of list in df1 is list1, then the corresponding data of "value" in df1 should be one among the values of the list1 column in df2 3.
-> If value of list in df1 is list2, then the corresponding data of "value" in df1 should be one among the values of the list2 column in df2.
Expected Result df:
ID list value listA valueA listB valueB Error
1 list1 D1 list1 D1 list1 D1 no mismatch
2 list2 D1 valueA mismatch
3 list1 D3 list2 D3 value, valueB mismatch
4 list2 D1 list2 D1 list1 D3 value,valueA&valueB mismatch
5 list2 D2 list2 D2 list2 D2 no mismatch
6 list2 D3 list1 D3 list1 D4 value, valueA mismatch
pandas dataframe
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
add a comment |
2
I have 2 dataframes as below:
df1:
ID list value listA valueA listB valueB
1 list1 D1 list1 D1 list1 D1
2 list2 D1
3 list1 D3 list2 D3
4 list2 D1 list2 D1 list1 D3
5 list2 D2 list2 D2 list2 D2
6 list2 D3 list1 D3 list1 D4
df2:
list1 list2
D1 456
D2 D2
33 D31
D4 245
EE D5
D5 D6
I'm trying to generate a final df based on the below validations
-> If value of list in df1 is list1, then the corresponding data of "value" in df1 should be one among the values of the list1 column in df2 3.
-> If value of list in df1 is list2, then the corresponding data of "value" in df1 should be one among the values of the list2 column in df2.
Expected Result df:
ID list value listA valueA listB valueB Error
1 list1 D1 list1 D1 list1 D1 no mismatch
2 list2 D1 valueA mismatch
3 list1 D3 list2 D3 value, valueB mismatch
4 list2 D1 list2 D1 list1 D3 value,valueA&valueB mismatch
5 list2 D2 list2 D2 list2 D2 no mismatch
6 list2 D3 list1 D3 list1 D4 value, valueA mismatch
pandas dataframe
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
1
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55
add a comment |
2
2
2
I have 2 dataframes as below:
df1:
ID list value listA valueA listB valueB
1 list1 D1 list1 D1 list1 D1
2 list2 D1
3 list1 D3 list2 D3
4 list2 D1 list2 D1 list1 D3
5 list2 D2 list2 D2 list2 D2
6 list2 D3 list1 D3 list1 D4
df2:
list1 list2
D1 456
D2 D2
33 D31
D4 245
EE D5
D5 D6
I'm trying to generate a final df based on the below validations
-> If value of list in df1 is list1, then the corresponding data of "value" in df1 should be one among the values of the list1 column in df2 3.
-> If value of list in df1 is list2, then the corresponding data of "value" in df1 should be one among the values of the list2 column in df2.
Expected Result df:
ID list value listA valueA listB valueB Error
1 list1 D1 list1 D1 list1 D1 no mismatch
2 list2 D1 valueA mismatch
3 list1 D3 list2 D3 value, valueB mismatch
4 list2 D1 list2 D1 list1 D3 value,valueA&valueB mismatch
5 list2 D2 list2 D2 list2 D2 no mismatch
6 list2 D3 list1 D3 list1 D4 value, valueA mismatch
pandas dataframe
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
I have 2 dataframes as below:
df1:
ID list value listA valueA listB valueB
1 list1 D1 list1 D1 list1 D1
2 list2 D1
3 list1 D3 list2 D3
4 list2 D1 list2 D1 list1 D3
5 list2 D2 list2 D2 list2 D2
6 list2 D3 list1 D3 list1 D4
df2:
list1 list2
D1 456
D2 D2
33 D31
D4 245
EE D5
D5 D6
I'm trying to generate a final df based on the below validations
-> If value of list in df1 is list1, then the corresponding data of "value" in df1 should be one among the values of the list1 column in df2 3.
-> If value of list in df1 is list2, then the corresponding data of "value" in df1 should be one among the values of the list2 column in df2.
Expected Result df:
ID list value listA valueA listB valueB Error
1 list1 D1 list1 D1 list1 D1 no mismatch
2 list2 D1 valueA mismatch
3 list1 D3 list2 D3 value, valueB mismatch
4 list2 D1 list2 D1 list1 D3 value,valueA&valueB mismatch
5 list2 D2 list2 D2 list2 D2 no mismatch
6 list2 D3 list1 D3 list1 D4 value, valueA mismatch
pandas dataframe
pandas dataframe
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
edited Dec 5 '18 at 14:30
Osceria
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
asked Nov 22 '18 at 11:35
OsceriaOsceria
599
599
Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
1
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55
add a comment |
Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
1
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55
Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
1
1
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55
add a comment |
2 Answers
2
active
oldest
votes
0
Use GroupBy.transform with custom function with isin for boolean mask and set values by numpy.where:
m = df1.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name]))
df1['Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')
print (df1)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution isdf2 = df2.assign(missing=np.nan)andm = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))
– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you checkm1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')?
– jezrael
Dec 3 '18 at 13:36
|
show 6 more comments
0
You can do the following:
def fun(x, df2):
if x['value'] not in df2[x['list']].tolist():
return 'list mismatch with df2'
else:
return 'No mismatch with df2'
df['Error'] = df.apply(lambda x: fun(x, df2), axis = 1)
print(df)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
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
0
Use GroupBy.transform with custom function with isin for boolean mask and set values by numpy.where:
m = df1.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name]))
df1['Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')
print (df1)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution isdf2 = df2.assign(missing=np.nan)andm = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))
– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you checkm1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')?
– jezrael
Dec 3 '18 at 13:36
|
show 6 more comments
0
Use GroupBy.transform with custom function with isin for boolean mask and set values by numpy.where:
m = df1.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name]))
df1['Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')
print (df1)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution isdf2 = df2.assign(missing=np.nan)andm = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))
– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you checkm1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')?
– jezrael
Dec 3 '18 at 13:36
|
show 6 more comments
0
0
0
Use GroupBy.transform with custom function with isin for boolean mask and set values by numpy.where:
m = df1.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name]))
df1['Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')
print (df1)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
Use GroupBy.transform with custom function with isin for boolean mask and set values by numpy.where:
m = df1.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name]))
df1['Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')
print (df1)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
answered Nov 22 '18 at 11:47
jezraeljezrael
342k25297369
342k25297369
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution isdf2 = df2.assign(missing=np.nan)andm = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))
– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you checkm1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')?
– jezrael
Dec 3 '18 at 13:36
|
show 6 more comments
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution isdf2 = df2.assign(missing=np.nan)andm = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))
– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you checkm1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2')?
– jezrael
Dec 3 '18 at 13:36
1
1
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
expected results are satisfied
– Osceria
Nov 22 '18 at 13:45
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
the code throws an error when the values are made empty for any row in df1. error - "Length mismatch: Expected axis has 5 elements, new values have 6 elements". Any help with this? @jezrael
– Osceria
Dec 3 '18 at 5:38
@Osceria - Problem is missing values are excluded in groupby, solution is
df2 = df2.assign(missing=np.nan) and m = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))– jezrael
Dec 3 '18 at 6:14
@Osceria - Problem is missing values are excluded in groupby, solution is
df2 = df2.assign(missing=np.nan) and m = df1['value'].groupby(df1['list'].fillna('missing')).transform(lambda x: x.isin(df2[x.name]))– jezrael
Dec 3 '18 at 6:14
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
This change marks the rows with empty values as "list mismatch". Rather it should ignore the rows from validation that have empty values.- @Jezrael
– Osceria
Dec 3 '18 at 13:20
@Osceria - Can you check
m1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2') ?– jezrael
Dec 3 '18 at 13:36
@Osceria - Can you check
m1 = df1['list'].notnull() df11 = df1[m1] m = df11.groupby('list')['value'].transform(lambda x: x.isin(df2[x.name])) df1.loc[m1, 'Error'] = np.where(m, 'No mismatch with df2','list mismatch with df2') ?– jezrael
Dec 3 '18 at 13:36
|
show 6 more comments
0
You can do the following:
def fun(x, df2):
if x['value'] not in df2[x['list']].tolist():
return 'list mismatch with df2'
else:
return 'No mismatch with df2'
df['Error'] = df.apply(lambda x: fun(x, df2), axis = 1)
print(df)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
add a comment |
0
You can do the following:
def fun(x, df2):
if x['value'] not in df2[x['list']].tolist():
return 'list mismatch with df2'
else:
return 'No mismatch with df2'
df['Error'] = df.apply(lambda x: fun(x, df2), axis = 1)
print(df)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
add a comment |
0
0
0
You can do the following:
def fun(x, df2):
if x['value'] not in df2[x['list']].tolist():
return 'list mismatch with df2'
else:
return 'No mismatch with df2'
df['Error'] = df.apply(lambda x: fun(x, df2), axis = 1)
print(df)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
You can do the following:
def fun(x, df2):
if x['value'] not in df2[x['list']].tolist():
return 'list mismatch with df2'
else:
return 'No mismatch with df2'
df['Error'] = df.apply(lambda x: fun(x, df2), axis = 1)
print(df)
ID list value Error
0 1 list1 D1 No mismatch with df2
1 2 list1 D2 No mismatch with df2
2 3 list1 D3 list mismatch with df2
3 4 list2 D1 list mismatch with df2
4 5 list2 D2 No mismatch with df2
5 6 list2 D3 list mismatch with df2
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
edited Nov 22 '18 at 13:15
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
answered Nov 22 '18 at 11:47
yatuyatu
11.7k31238
11.7k31238
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
add a comment |
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
I get this error - AttributeError: ("'Series' object has no attribute 'list'", 'occurred at index 0')
– Osceria
Nov 22 '18 at 12:25
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Does it worlk now? @Osceria
– yatu
Nov 22 '18 at 12:27
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
Yes Alexandre, the code works fine now.
– Osceria
Nov 22 '18 at 13:44
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Why asking same question twice? Your previous question
– AkshayNevrekar
Nov 22 '18 at 11:49
1
@Sociopath - Yes, I did split the question into two, as they deal with different validations, just to avoid confusion
– Osceria
Nov 22 '18 at 11:55