Is there a dummy comparison variable in python and pandas-CodePudding

I have a dataframe df as follows:

C1  C2  C3
a1  a   d
b1  b   e
c1  c   f

If I do df[df['C1'] == 'a1'] I get

C1  C2  C3
a1  a   d

Is there some way of comparing things so that if I do df[df['C1'] == 'what should I use here'], I get all rows. i.e. I am looking for a dummy comparison variable so that comparison statement is ignored and all rows are returned.

C1  C2  C3
a1  a   d
b1  b   e
c1  c   f

Edit: As the code is grandfathered, I need to use only == comparison statement. I can't use !=. The comparison variable is coming through a for loop and I can insert a dummy variable in the for loop if available.

This is what the original code is which I can't modify:

dfNew = []
for ii in ['a1', 'a2']:
 for jj in ['a', 'b']:
  dfNew.append(df[(df['C1'] == ii) & (df['C2'] == jj)])

What I am trying to achieve in dfNew is to also append values where if there was a dummy variable in ['a1', 'a2', 'dummy'] then only the other comparison operation is done. The number of columns is close to 50 so it will be impossible to code every pair to ignore in conditional operation.

Edit2: How do you pass the dummy inside ['a1', 'a2', 'dummy'] ?

CodePudding user response：

If you want a real Dummy variable, the official and cleanest way would be to create a class where __eq__ (equality check) always returns True:

class Dummy:
    def __eq__(self, other):
        return True

And now:

>>> df[df['C1'] == Dummy()]
   C1 C2 C3
0  a1  a  d
1  b1  b  e
2  c1  c  f
>>>

Always gives True.

The Dummy class returns True for all equality checks.

For the edit.

Just do:

['a1', 'a2', Dummy()]

CodePudding user response：

Why not this?

df[df['C1'] == df['C1']]

That will work for all values except for NaN, because NaN != NaN.