Pandas One-Hot Encoding with get_dummies: Avoiding Boolean Output

One-hot encoding is a common technique used in machine learning to convert categorical variables into numerical representations. Pandas provides the get_dummies function for conveniently applying one-hot encoding to DataFrame columns.
However, when using get_dummies on categorical variables, you may encounter a problem where the output consists of Boolean values instead of the expected one-hot encoded columns. This occurs because get_dummies defaults to using dummy variables based on the dtype of the input column. For object dtypes (typically used for categorical data), it creates a Boolean indexer.
To overcome this issue and obtain the desired one-hot encoded columns, you can convert the input column to a specific dtype before applying get_dummies. Here’s an example demonstrating how to do this:

import pandas as pd
# Sample DataFrame with categorical column
data = {'Color': ['Red', 'Blue', 'Green', 'Red', 'Blue']}
df = pd.DataFrame(data)
# Convert categorical column to string dtype
df['Color'] = df['Color'].astype('str')
# Apply get_dummies to perform one-hot encoding
one_hot_encoded = pd.get_dummies(df, columns=['Color'])
# Convert the Boolean indexer to integer values
one_hot_encoded[one_hot_encoded.columns] = one_hot_encoded.astype(int)
print(one_hot_encoded)

In the above example, we first convert the ‘Color’ column to string dtype using astype(‘str’). This ensures that get_dummies treats the column as categorical data and creates one-hot encoded columns based on the unique values in the column.
Next, we use astype(int) to convert the Boolean indexer to integer values. This step is crucial to obtain numerical output instead of Boolean values. The resulting DataFrame will contain one-hot encoded columns with integer values representing each unique category.