I have a dataframe with 2 columns containing floats. I first excluded rows where columns contain zeros and then wanted to check for each row, if the elements of the columns are equal.

I tried:

```
df.loc[(df['col1'] != 0.0) & (df['col2'] != 0.0), 'Error'] = np.where(assert_almost_equal(df['col1'], df['col2']), 'Not equal', '')

```

result was:

```
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

```

and also tried:

```
np.where(df['col1'] == df['col2'], 'Not equal', '')

```

and

```
np.where(df.col1.eq(df.col2), 'Not equal', '')

```

and result was:

```
ValueError: shape mismatch: value array of shape (24788,) could not be broadcast to indexing result of shape (9576,)

```

and also tried `apply`-function.

How can I compare the floats within two columns row by row? I do really want need equality, not `isclose` or something similar.

Thank you,

MaMo