Category Column Mapping - I have a CSV with image name as a column and 7 other columns for the image category. The category column can have either 0 or 1 as value depending on what category the image belongs to. Create a new column 'category' using pandas to show what is the category of the image

category_col_map.py

import pandas as pd

# Load your CSV file
df = pd.read_csv('your_file.csv')

# Assuming your category columns are named 'cat1', 'cat2', ..., 'cat7'
category_columns = ['cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7']

# Create a new column 'category' based on the category columns
df['category'] = df[category_columns].idxmax(axis=1)

# Replace the index of the category with a more user-friendly name if necessary
# For example, if your categories are named 'Category1', 'Category2', etc.
category_mapping = {
    'cat1': 'Category1',
    'cat2': 'Category2',
    'cat3': 'Category3',
    'cat4': 'Category4',
    'cat5': 'Category5',
    'cat6': 'Category6',
    'cat7': 'Category7'
}

df['category'] = df['category'].map(category_mapping)

# Save the updated DataFrame back to CSV if needed
df.to_csv('updated_file.csv', index=False)

# Display the updated DataFrame
print(df)