Iterate over df to compare values

Question

I have a large data frame and I need to compare the first entry in column one to the first entry in column two. Then the first entry in column one to the first entry in column three, etc. Basically I want to see if the two values are >0, then do something. Is there a way to do this using a pandas dataframe?

0  22 0 44 5 6
1  12 3 56 0 0
2  0  0 1  0 0
3  1  2 0  0 0

So I want to see if 22 and 0 are both >0. Then if 22 and 44 are both >0. Then if 22 and 5 are both >0. Then if 22 and 6 are both >0. Then if 12 and 3 are both >0. Then if 12 and 56 are both >0. ...and so on. If a pair has both elements >0 then my code will do something else.

I assume there is an easy way to iterate over rows and columns that I am just missing.

Answer 1

Use DataFrame.rolling per axis=1 - columns, comapre by condition and test if at least one True in both by any or if necessary test if both are True use change any to all.

First column is filled NaNs, so (I hope) is necessary DataFrame.shift for last column NaNs, replace them by DataFrame.fillna and cast to boolean:

print (df)
    a  b   c  d  e
0  22  0  44  5  6
1  12  3  56  0  0
2   0  0   1  0  0
3   1  2   0  0  0


def f(x):
    print (x)
    mask = x > 0
    print (mask)
    return mask.any()


df = df.rolling(2, axis=1).apply(f, raw=True).shift(-1, axis=1).fillna(0).astype(bool)
print (df)
       a     b      c      d      e
0   True  True   True   True  False
1   True  True   True  False  False
2  False  True   True  False  False
3   True  True  False  False  False