Select pandas dataframe columns which have only one unique value

Question

How to effectively select pandas dataframe columns which have only 1 unique value?

I'm aware of DataFrame and Series.nunique()

Answer 1

I think need DataFrame.nunique for boolean mask and select by loc with boolean indexing :

df = pd.DataFrame({'A':list('abcdef'),
                   'B':[4,5,4,5,5,4],
                   'C':[7,8,9,4,2,3],
                   'D':[1] * 6,
                   'E':[5,3,6,9,2,4],
                   'F':list('aaaaaa')})

print (df)
   A  B  C  D  E  F
0  a  4  7  1  5  a
1  b  5  8  1  3  a
2  c  4  9  1  6  a
3  d  5  4  1  9  a
4  e  5  2  1  2  a
5  f  4  3  1  4  a

df = df.loc[:, df.nunique() == 1]
#alternatives
#df = df.loc[:, df.apply(lambda x: x.nunique()) == 1]
#df = df.loc[:, df.apply(lambda x: len(x.unique())) == 1]
print (df)
   D  F
0  1  a
1  1  a
2  1  a
3  1  a
4  1  a
5  1  a

Answer 2

Use DataFrame.uniques() to count distinct observations over requested axis.

df = pd.DataFrame({'A': list('abcdef'),
               'B': [4, 5, 4, 5, 5, 4],
               'C': [7, 8, 9, 4, 2, 3],
               'D': [1] * 6,
               'E': [5, 3, 6, 9, 2, 4],
               'F': list('aaaaaa')})

print(df)

df.columns[df.nunique() <= 1]