Compare dataframe columns with conditions
up vote
1
down vote
favorite
I have 2 dataframes as below:
df1:
ID col1 col2
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4
5 A5 B5
6 A6 B6
df2:
col1 col2
A1 B1
A2 O5
H3 B3
A4 B4
A5 66
A6 C6
Expected Result: I would like to generate a result df based on the condition - Each value in col1,col2 of df1 should exist in col1,col2 values of df2
Expected Result df:
ID col1 col2 Error
1 A1 B1 No mismatch with df2
2 A2 B2 col2 mismatch with df2
3 A3 B3 col1 mismatch with df2
4 A4 B4 No mismatch with df2
5 A5 B5 col2 mismatch with df2
6 A6 B6 col2 mismatch with df2
python pandas dataframe
asked Nov 21 at 23:54
Osceria
479
add a comment |
up vote
1
down vote
favorite
I have 2 dataframes as below:
df1:
ID col1 col2
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4
5 A5 B5
6 A6 B6
df2:
col1 col2
A1 B1
A2 O5
H3 B3
A4 B4
A5 66
A6 C6
Expected Result: I would like to generate a result df based on the condition - Each value in col1,col2 of df1 should exist in col1,col2 values of df2
Expected Result df:
ID col1 col2 Error
1 A1 B1 No mismatch with df2
2 A2 B2 col2 mismatch with df2
3 A3 B3 col1 mismatch with df2
4 A4 B4 No mismatch with df2
5 A5 B5 col2 mismatch with df2
6 A6 B6 col2 mismatch with df2
python pandas dataframe
asked Nov 21 at 23:54
Osceria
479
You do not have list in df2
– W-B
Nov 22 at 1:25
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
Edited the Question
– Osceria
Nov 22 at 11:46
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
I have 2 dataframes as below:
df1:
ID col1 col2
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4
5 A5 B5
6 A6 B6
df2:
col1 col2
A1 B1
A2 O5
H3 B3
A4 B4
A5 66
A6 C6
Expected Result: I would like to generate a result df based on the condition - Each value in col1,col2 of df1 should exist in col1,col2 values of df2
Expected Result df:
ID col1 col2 Error
1 A1 B1 No mismatch with df2
2 A2 B2 col2 mismatch with df2
3 A3 B3 col1 mismatch with df2
4 A4 B4 No mismatch with df2
5 A5 B5 col2 mismatch with df2
6 A6 B6 col2 mismatch with df2
python pandas dataframe
asked Nov 21 at 23:54
Osceria
479
I have 2 dataframes as below:
df1:
ID col1 col2
1 A1 B1
2 A2 B2
3 A3 B3
4 A4 B4
5 A5 B5
6 A6 B6
df2:
col1 col2
A1 B1
A2 O5
H3 B3
A4 B4
A5 66
A6 C6
Expected Result: I would like to generate a result df based on the condition - Each value in col1,col2 of df1 should exist in col1,col2 values of df2
Expected Result df:
ID col1 col2 Error
1 A1 B1 No mismatch with df2
2 A2 B2 col2 mismatch with df2
3 A3 B3 col1 mismatch with df2
4 A4 B4 No mismatch with df2
5 A5 B5 col2 mismatch with df2
6 A6 B6 col2 mismatch with df2
python pandas dataframe
python pandas dataframe
asked Nov 21 at 23:54
Osceria
479
asked Nov 21 at 23:54
Osceria
479
edited Nov 22 at 11:45
asked Nov 21 at 23:54
Osceria
479
asked Nov 21 at 23:54
Osceria
479
asked Nov 21 at 23:54
Osceria
479
479
You do not have list in df2
– W-B
Nov 22 at 1:25
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
Edited the Question
– Osceria
Nov 22 at 11:46
add a comment |
You do not have list in df2
– W-B
Nov 22 at 1:25
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
Edited the Question
– Osceria
Nov 22 at 11:46
You do not have list in df2
– W-B
Nov 22 at 1:25
You do not have list in df2
– W-B
Nov 22 at 1:25
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
Edited the Question
– Osceria
Nov 22 at 11:46
Edited the Question
– Osceria
Nov 22 at 11:46
add a comment |
2 Answers
2
active
oldest
votes
up vote
0
down vote
accepted
Create helper DataFrame with dictionary comprehension and comparing with isin:
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']})
print (m)
col1 col2
0 False False
1 False True
2 True False
3 False False
4 False True
5 False True
And then numpy.where with mask by any for test at least one True per rows and dot with matrix multiplication for get column names:
df1['Error'] = np.where(m.any(axis=1),
m.dot(m.columns + ', ').str.rstrip(', ') + ' mismatch with df2',
'No mismatch with df2')
print (df1)
ID col1 col2 Error
0 1 A1 B1 No mismatch with df2
1 2 A2 B2 col2 mismatch with df2
2 3 A3 B3 col1 mismatch with df2
3 4 A4 B4 No mismatch with df2
4 5 A5 B5 col2 mismatch with df2
5 6 A6 B6 col2 mismatch with df2
answered Nov 22 at 12:08
jezrael
316k22256333
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension likem = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})
– jezrael
Nov 22 at 14:43
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
add a comment |
up vote
0
down vote
Something like this should do the trick but there may be an easier way.
diff = pd.concat([df1[col] == df2[col] for col in df1], axis=1)
def m(row):
mismatches =
for col in diff.columns:
if not row[col]:
mismatches.append(col)
if mismatches == :
return 'No mismatch'
return 'Mismatches: ' + ', '.join(mismatches)
df1['Error'] = diff.apply(m, axis=1)
answered Nov 22 at 0:20
lieblos
1029
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})
– leoburgy
Nov 22 at 12:01
It's because yourdf1anddf2had different columns, right? I noticed you edited the question now, does it work with those dataframes?
– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
|
show 1 more comment
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
accepted
Create helper DataFrame with dictionary comprehension and comparing with isin:
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']})
print (m)
col1 col2
0 False False
1 False True
2 True False
3 False False
4 False True
5 False True
And then numpy.where with mask by any for test at least one True per rows and dot with matrix multiplication for get column names:
df1['Error'] = np.where(m.any(axis=1),
m.dot(m.columns + ', ').str.rstrip(', ') + ' mismatch with df2',
'No mismatch with df2')
print (df1)
ID col1 col2 Error
0 1 A1 B1 No mismatch with df2
1 2 A2 B2 col2 mismatch with df2
2 3 A3 B3 col1 mismatch with df2
3 4 A4 B4 No mismatch with df2
4 5 A5 B5 col2 mismatch with df2
5 6 A6 B6 col2 mismatch with df2
answered Nov 22 at 12:08
jezrael
316k22256333
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension likem = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})
– jezrael
Nov 22 at 14:43
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
add a comment |
up vote
0
down vote
accepted
Create helper DataFrame with dictionary comprehension and comparing with isin:
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']})
print (m)
col1 col2
0 False False
1 False True
2 True False
3 False False
4 False True
5 False True
And then numpy.where with mask by any for test at least one True per rows and dot with matrix multiplication for get column names:
df1['Error'] = np.where(m.any(axis=1),
m.dot(m.columns + ', ').str.rstrip(', ') + ' mismatch with df2',
'No mismatch with df2')
print (df1)
ID col1 col2 Error
0 1 A1 B1 No mismatch with df2
1 2 A2 B2 col2 mismatch with df2
2 3 A3 B3 col1 mismatch with df2
3 4 A4 B4 No mismatch with df2
4 5 A5 B5 col2 mismatch with df2
5 6 A6 B6 col2 mismatch with df2
answered Nov 22 at 12:08
jezrael
316k22256333
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension likem = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})
– jezrael
Nov 22 at 14:43
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
add a comment |
up vote
0
down vote
accepted
up vote
0
down vote
accepted
Create helper DataFrame with dictionary comprehension and comparing with isin:
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']})
print (m)
col1 col2
0 False False
1 False True
2 True False
3 False False
4 False True
5 False True
And then numpy.where with mask by any for test at least one True per rows and dot with matrix multiplication for get column names:
df1['Error'] = np.where(m.any(axis=1),
m.dot(m.columns + ', ').str.rstrip(', ') + ' mismatch with df2',
'No mismatch with df2')
print (df1)
ID col1 col2 Error
0 1 A1 B1 No mismatch with df2
1 2 A2 B2 col2 mismatch with df2
2 3 A3 B3 col1 mismatch with df2
3 4 A4 B4 No mismatch with df2
4 5 A5 B5 col2 mismatch with df2
5 6 A6 B6 col2 mismatch with df2
answered Nov 22 at 12:08
jezrael
316k22256333
Create helper DataFrame with dictionary comprehension and comparing with isin:
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']})
print (m)
col1 col2
0 False False
1 False True
2 True False
3 False False
4 False True
5 False True
And then numpy.where with mask by any for test at least one True per rows and dot with matrix multiplication for get column names:
df1['Error'] = np.where(m.any(axis=1),
m.dot(m.columns + ', ').str.rstrip(', ') + ' mismatch with df2',
'No mismatch with df2')
print (df1)
ID col1 col2 Error
0 1 A1 B1 No mismatch with df2
1 2 A2 B2 col2 mismatch with df2
2 3 A3 B3 col1 mismatch with df2
3 4 A4 B4 No mismatch with df2
4 5 A5 B5 col2 mismatch with df2
5 6 A6 B6 col2 mismatch with df2
answered Nov 22 at 12:08
jezrael
316k22256333
answered Nov 22 at 12:08
jezrael
316k22256333
answered Nov 22 at 12:08
jezrael
316k22256333
answered Nov 22 at 12:08
jezrael
316k22256333
316k22256333
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension likem = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})
– jezrael
Nov 22 at 14:43
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
add a comment |
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension likem = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})
– jezrael
Nov 22 at 14:43
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in ['col1','col2']}) - col1 and col2 are hard-coded. When I try to pass the column names directly from the dataframe using df.cols, it says the below error "ValueError: Must pass DataFrame with boolean values only" - Any help with this?
– Osceria
Nov 22 at 14:37
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
code should work if I pass all the columns from the dataframe like this lovcols = df2.columns m = pd.DataFrame({c: ~dfCSDataset[c].isin(dfLOVRules[c]) for c in [lovcols]}
– Osceria
Nov 22 at 14:41
@Osceria - yes, you are right. You can also pass columns to dict comprehension like
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})– jezrael
Nov 22 at 14:43
@Osceria - yes, you are right. You can also pass columns to dict comprehension like
m = pd.DataFrame({c: ~df1[c].isin(df2[c]) for c in df2.columns})– jezrael
Nov 22 at 14:43
1
1
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
yeah, it works in this way too
– Osceria
Nov 22 at 15:07
add a comment |
up vote
0
down vote
Something like this should do the trick but there may be an easier way.
diff = pd.concat([df1[col] == df2[col] for col in df1], axis=1)
def m(row):
mismatches =
for col in diff.columns:
if not row[col]:
mismatches.append(col)
if mismatches == :
return 'No mismatch'
return 'Mismatches: ' + ', '.join(mismatches)
df1['Error'] = diff.apply(m, axis=1)
answered Nov 22 at 0:20
lieblos
1029
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})
– leoburgy
Nov 22 at 12:01
It's because yourdf1anddf2had different columns, right? I noticed you edited the question now, does it work with those dataframes?
– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
|
show 1 more comment
up vote
0
down vote
Something like this should do the trick but there may be an easier way.
diff = pd.concat([df1[col] == df2[col] for col in df1], axis=1)
def m(row):
mismatches =
for col in diff.columns:
if not row[col]:
mismatches.append(col)
if mismatches == :
return 'No mismatch'
return 'Mismatches: ' + ', '.join(mismatches)
df1['Error'] = diff.apply(m, axis=1)
answered Nov 22 at 0:20
lieblos
1029
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})
– leoburgy
Nov 22 at 12:01
It's because yourdf1anddf2had different columns, right? I noticed you edited the question now, does it work with those dataframes?
– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
|
show 1 more comment
up vote
0
down vote
up vote
0
down vote
Something like this should do the trick but there may be an easier way.
diff = pd.concat([df1[col] == df2[col] for col in df1], axis=1)
def m(row):
mismatches =
for col in diff.columns:
if not row[col]:
mismatches.append(col)
if mismatches == :
return 'No mismatch'
return 'Mismatches: ' + ', '.join(mismatches)
df1['Error'] = diff.apply(m, axis=1)
answered Nov 22 at 0:20
lieblos
1029
Something like this should do the trick but there may be an easier way.
diff = pd.concat([df1[col] == df2[col] for col in df1], axis=1)
def m(row):
mismatches =
for col in diff.columns:
if not row[col]:
mismatches.append(col)
if mismatches == :
return 'No mismatch'
return 'Mismatches: ' + ', '.join(mismatches)
df1['Error'] = diff.apply(m, axis=1)
answered Nov 22 at 0:20
lieblos
1029
answered Nov 22 at 0:20
lieblos
1029
answered Nov 22 at 0:20
lieblos
1029
answered Nov 22 at 0:20
lieblos
1029
1029
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})
– leoburgy
Nov 22 at 12:01
It's because yourdf1anddf2had different columns, right? I noticed you edited the question now, does it work with those dataframes?
– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
|
show 1 more comment
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})
– leoburgy
Nov 22 at 12:01
It's because yourdf1anddf2had different columns, right? I noticed you edited the question now, does it work with those dataframes?
– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
When I try this, I get the error "ValueError: Can only compare identically-labeled Series objects"
– Osceria
Nov 22 at 10:23
Edited the Question
– Osceria
Nov 22 at 11:46
Edited the Question
– Osceria
Nov 22 at 11:46
@Osceria do you get the same error with the following reproducible datasets:
df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})– leoburgy
Nov 22 at 12:01
@Osceria do you get the same error with the following reproducible datasets:
df1 = pd.DataFrame({'col1': ["A1", "A2", "A3", "A4", "A5", "A6"], 'col2': ["B1", "B2", "B3", "B4", "B5", "B6"]}) df2 = pd.DataFrame({'col1': ["A1", "A2", "H3", "A4", "A5", "A6"], 'col2': ["B1", "O5", "B3", "B4", "66", "C6"]})– leoburgy
Nov 22 at 12:01
It's because your
df1 and df2 had different columns, right? I noticed you edited the question now, does it work with those dataframes?– lieblos
Nov 22 at 12:47
It's because your
df1 and df2 had different columns, right? I noticed you edited the question now, does it work with those dataframes?– lieblos
Nov 22 at 12:47
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
If I run what I answered with the dataframes above, it seems like it works.
– lieblos
Nov 22 at 12:49
|
show 1 more comment
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You do not have list in df2
– W-B
Nov 22 at 1:25
list is a column in df1 and its value list1 and list2 are just dropdownlist names ; the accepted values are given in columns list1,list2 in df2. So, the data from column "value" of df1 based on its list value should be checked with df2 list1 & list2 values.
– Osceria
Nov 22 at 9:54
Edited the Question
– Osceria
Nov 22 at 11:46