Reversing 'one-hot' encoding in Pandas

Reversing 'one-hot' encoding in Pandas

Reversing one-hot encoding in Pandas involves converting a set of binary indicator columns back to a single categorical column. You can achieve this by using the idxmax() function along with the apply() function.

Here's how you can reverse one-hot encoding in a Pandas DataFrame:

import pandas as pd

# Create a sample DataFrame with one-hot encoded data
data = {'Category_A': [0, 1, 0],
        'Category_B': [1, 0, 0],
        'Category_C': [0, 0, 1]}
df = pd.DataFrame(data)

# Reverse one-hot encoding
reversed_df = df.apply(lambda row: row.idxmax(), axis=1)

print(reversed_df)

In this example, apply() is used with the axis=1 parameter to apply the idxmax() function row-wise. The idxmax() function returns the column label (category) with the maximum value (1) in each row. This effectively reverses the one-hot encoding by determining which category is "active" (encoded with 1) for each row.

The output will be:

0    Category_B
1    Category_A
2    Category_C
dtype: object

Now, you have a Series containing the reversed one-hot encoded categories for each row.

Keep in mind that this method assumes that each row should have exactly one category set to 1 in the original one-hot encoded DataFrame. If there are cases where multiple categories are set to 1 in a single row, this approach might not work as expected.

Examples

  1. "How to reverse one-hot encoding to original categorical values in Pandas"

    • Description: This query focuses on reversing a one-hot encoded DataFrame back to its original categorical form.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame
df = pd.DataFrame({
    'A': [1, 0, 0],
    'B': [0, 1, 0],
    'C': [0, 0, 1]
})

# Reversing one-hot encoding
df['category'] = df.idxmax(axis=1)

  2. "Reversing one-hot encoding with multiple categories in Pandas"

    • Description: This query addresses how to reverse one-hot encoding when multiple categories are present.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame with multiple categories
df = pd.DataFrame({
    'red': [1, 0, 0],
    'green': [0, 1, 0],
    'blue': [0, 0, 1]
})

# Reversing one-hot encoding to a single categorical column
df['color'] = df.idxmax(axis=1)

  3. "How to handle ties when reversing one-hot encoding in Pandas"

    • Description: This query is about addressing tie situations where there might be multiple '1's in a one-hot encoded DataFrame.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame with ties
df = pd.DataFrame({
    'X': [1, 0, 1],
    'Y': [1, 1, 0]
})

# Handling ties by creating a list of active categories
df['categories'] = df.apply(lambda row: [col for col in df.columns if row[col] == 1], axis=1)

  4. "Reversing one-hot encoding with specific ordering in Pandas"

    • Description: This query focuses on reversing one-hot encoding while considering a specific order for the categories.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame with an order to categories
df = pd.DataFrame({
    'small': [0, 1, 0],
    'medium': [1, 0, 1],
    'large': [0, 0, 0]
})

# Reversing one-hot encoding while keeping a specific order
df['size'] = df.idxmax(axis=1).astype('category', categories=['small', 'medium', 'large'], ordered=True)

  5. "Reversing one-hot encoding with missing data in Pandas"

    • Description: This query is about reversing one-hot encoding in the presence of missing data or NaN values.
    • Code:
      import pandas as pd
import numpy as np

# One-hot encoded DataFrame with NaN values
df = pd.DataFrame({
    'X': [1, 0, np.nan],
    'Y': [0, 1, 0],
    'Z': [0, 0, 1]
})

# Filling NaN values and reversing one-hot encoding
df = df.fillna(0)
df['category'] = df.idxmax(axis=1)

  6. "Reversing one-hot encoding to original multi-label values in Pandas"

    • Description: This query deals with reversing one-hot encoding where multiple labels may be true for a single observation.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame with multiple labels
df = pd.DataFrame({
    'apple': [1, 0, 1],
    'banana': [1, 1, 0],
    'cherry': [0, 1, 0]
})

# Reversing one-hot encoding to get multi-label categories
df['fruits'] = df.apply(lambda row: [col for col in df.columns if row[col] == 1], axis=1)

  7. "Reversing one-hot encoding with custom index in Pandas"

    • Description: This query focuses on reversing one-hot encoding when the DataFrame has a custom index or specific order.
    • Code:
      import pandas as pd

# One-hot encoded DataFrame with custom index
df = pd.DataFrame({
    'Monday': [0, 1, 0],
    'Tuesday': [1, 0, 0],
    'Wednesday': [0, 0, 1]
}, index=['Week 1', 'Week 2', 'Week 3'])

# Reversing one-hot encoding with a custom index
df['day'] = df.idxmax(axis=1)

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