How to merge the returned one-hot encoded columns to original dataframe?

Question

I have a banking_dataframe with 21 different columns, one is target, 10 of them are numeric features and 10 of them are categorical features. I have used get_dummies method of pandas to convert categorical data to one-hot encoding. The returned dataframe has 74 columns. Now, I want to merge the encoded dataframe with the original data frame, so my final data should have one-hot encoded values for categorical columns but in the original size of data-frame i.e; 21 columns.

Link to get_dummies function of Pandas:

Code snippet to call get_dummies on categorical features

encoded_features = pd.get_dummies(banking_dataframe[categorical_feature_names])

Answer 1

from sklearn.preprocessing import OneHotEncoder
import pandas as pd

# creating a toy data frame to test
df = pd.DataFrame({'Gender': ['M', 'F', 'M', 'M', 'F', 'F', 'F']})

# instantiating and transforming the 'Gender' column of the df
one_hot = OneHotEncoder()
encoded = one_hot.fit_transform(df[['Gender']])

# one_hot object has an attribute 'categories_', which stores the array
# of categories sequentially, and those categories can serve as 
# new columns in our data frame.

df[one_hot.categories_[0]] = encoded.toarray()

Answer 2

You can try this:

pd.concat([df,encoded_features],axis=1)

If you don't want to increase the dimensions try doing label encoding instead of pd.get_dummies() because pd.get_dummies() add new columns to the dataset while label encoding does the encoding in the column itself.
Try this:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['Categorical column_name'] = le.fit_transform(df['Categorical column_name'])