Pandas reverse one hot encoding

Pandas Reverse One Hot Encoding

In pandas, reverse one hot encoding refers to the process of converting a one-hot encoded feature back to its original categorical representation. One hot encoding is a technique used to transform categorical variables into binary vectors, where each category is represented by a binary column.

Let’s consider an example. Suppose we have a DataFrame with the following one-hot encoded feature:

To reverse this one hot encoding, we can use the idxmax function from pandas. This function returns the column label of the maximum value for each row. By specifying axis=1, we can compute the maximum value across columns for each row. Let’s see how to do it:

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Feature_0': [1, 0, 0],
    'Feature_1': [0, 1, 0],
    'Feature_2': [0, 0, 1]
})

# Reverse one hot encoding
df['Reverse_Encoded'] = df[['Feature_0', 'Feature_1', 'Feature_2']].idxmax(axis=1)

The resulting DataFrame after reversing the one hot encoding will be:

As shown in the example, the idxmax function is used to find the column label with the maximum value. This column label represents the original category of the one-hot encoded feature.

Note that the reverse encoding assumes that only one column out of the binary columns will have a value of 1, and the rest will have 0. If multiple columns have the maximum value, the idxmax function will return the first occurrence.