How do I validate attribute values based on given map?

user_12241197 0
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So, I have a set of pairs, let's call it map X, that looks this:

{'a':(a_1, a_2, a_3),
'b':(b_1, b_2, b_3),
'c':(c_1, c_2, c_3)}

and a table that looks something like this:

Attribute 1 Attribute 2
a a_1
a a_2
c c_3
b a_1
a a_2
b b_3

We can see that the fourth record associates b and a_1. However, a_1 does not belong to the list of values associated with b in map X. Generally speaking, I want to flag when something like this happens.

Using python (pandas, preferably, but plain python is okay), how do I confirm that each attribute 1 value is paired with a member of the collection of values associated with it in map X?

pandas python
2 Answers

First flatten dictionary, so possible mapping second column by Series.map and compare by first column if not equal:

d = {x: k for k, v in X.items() for x in v}

df['test'] = df['Attribute 2'].map(d).ne(df['Attribute 1'])
print (df)
  Attribute 1 Attribute 2   test
0           a         a_1  False
1           a         a_2  False
2           c         c_3  False
3           b         a_1   True
4           a         a_2  False
5           b         b_3  False
user_29010020

You can try apply on rows

df['isin'] = df.apply(lambda row: row['Attribute 2'] not in d.get(row['Attribute 1']), axis=1)

print(df)

  Attribute 1 Attribute 2   isin
0           a         a_1  False
1           a         a_2  False
2           c         c_3  False
3           b         a_1   True
4           a         a_2  False
5           b         b_3  False
user_103151630
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