Approach 1: You can use pandas' pd.get_dummies.

Example 1:

import pandas as pd
s = pd.Series(list('abca'))
pd.get_dummies(s)
Out[]: 
     a    b    c
0  1.0  0.0  0.0
1  0.0  1.0  0.0
2  0.0  0.0  1.0
3  1.0  0.0  0.0

Example 2:

The following will transform a given column into one hot. Use prefix to have multiple dummies.

import pandas as pd
        
df = pd.DataFrame({
          'A':['a','b','a'],
          'B':['b','a','c']
        })
df
Out[]: 
   A  B
0  a  b
1  b  a
2  a  c

# Get one hot encoding of columns B
one_hot = pd.get_dummies(df['B'])
# Drop column B as it is now encoded
df = df.drop('B',axis = 1)
# Join the encoded df
df = df.join(one_hot)
df  
Out[]: 
       A  a  b  c
    0  a  0  1  0
    1  b  1  0  0
    2  a  0  0  1

Approach 2: Use Scikit-learn

Using a OneHotEncoder has the advantage of being able to fit on some training data and then transform on some other data using the same instance. We also have handle_unknown to further control what the encoder does with unseen data.

Given a dataset with three features and four samples, we let the encoder find the maximum value per feature and transform the data to a binary one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder
>>> enc = OneHotEncoder()
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])   
OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
   handle_unknown='error', n_values='auto', sparse=True)
>>> enc.n_values_
array([2, 3, 4])
>>> enc.feature_indices_
array([0, 2, 5, 9], dtype=int32)
>>> enc.transform([[0, 1, 1]]).toarray()
array([[ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.]])

Here is the link for this example: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html

IIUC, use get_feature_names_out():

import pandas as pd
from sklearn.preprocessing import OneHotEncoder

df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 1, 0],
                   'C': [0, 2, 2], 'D': [0, 1, 1]})

ohe = OneHotEncoder()
data = ohe.fit_transform(df)
df1 = pd.DataFrame(data.toarray(), columns=ohe.get_feature_names_out(), dtype=int)

Output:

>>> df
   A  B  C  D
0  0  3  0  0
1  1  1  2  1
2  2  0  2  1


>>> df1
   A_0  A_1  A_2  B_0  B_1  B_3  C_0  C_2  D_0  D_1
0    1    0    0    0    0    1    1    0    1    0
1    0    1    0    0    1    0    0    1    0    1
2    0    0    1    1    0    0    0    1    0    1

>>> pd.Series(ohe.get_feature_names_out()).str.rsplit('_', 1).str[0]
0    A
1    A
2    A
3    B
4    B
5    B
6    C
7    C
8    D
9    D
dtype: object