df.drop

df = pd.DataFrame(np.arange(12).reshape(3, 4),
...                   columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns
>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

>>>df = pd.DataFrame(np.arange(12).reshape(3, 4), 
                     columns=['A', 'B', 'C', 'D'])
>>>df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

OR

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

df = df.drop('column_name', 1)

df.drop(columns=['Unnamed: 0'])

df = df.drop(['B', 'C'], axis=1)

df = df.drop(df.columns[[0, 1, 3]], axis=1)  # df.columns is zero-based pd.Index 

df.drop(df.index[2])

# pandas drop a column with drop function
gapminder_ocean.drop(['pop'], axis=1)

DataFrame.drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]