Which columns are binary in a Pandas DataFrame?

Question

I have a pandas dataframe with a large number of columns and I need to find which columns are binary (with values 0 or 1 only) without looking at the data. Which function should be used?

Answer 1

To my knowledge, there is no direct function to test for this. Rather, you need to build something based on how the data was encoded (e.g. 1/0, T/F, True/False, etc.). In addition, if your column has a missing value, the entire column will be encoded as a float instead of an int.

In the example below, I test whether all unique non null values are either '1' or '0'. It returns a list of all such columns.

df = pd.DataFrame({'bool': [1, 0, 1, None], 
                   'floats': [1.2, 3.1, 4.4, 5.5], 
                   'ints': [1, 2, 3, 4], 
                   'str': ['a', 'b', 'c', 'd']})

bool_cols = [col for col in df 
             if df[[col]].dropna().unique().isin([0, 1]).all().values]

# 2019-09-10 EDIT (per Hardik Gupta)
bool_cols = [col for col in df 
             if np.isin(df[col].dropna().unique(), [0, 1]).all()]

>>> bool_cols
['bool']

>>> df[bool_cols]
   bool
0     1
1     0
2     1
3   NaN