1. import pandas as pd
  3. def release_date_resources(df, date_columns):
  4. """
  5. Releases resources (e.g., memory) associated with date columns in a Pandas DataFrame.
  7. Args:
  8. df (pd.DataFrame): The DataFrame to process.
  9. date_columns (list): A list of column names containing date values.
  10. """
  11. for col in date_columns:
  12. if col in df.columns: # Check if column exists
  13. df[col] = pd.to_datetime(df[col]).astype('str') # Convert to string to release datetime object
  14. # Alternatively, use df[col] = df[col].dt.strftime('%Y-%m-%d') for string formatting
  16. if __name__ == '__main__':
  17. # Example Usage
  18. data = {'col1': [1, 2, 3], 'date_col': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03'])}
  19. df = pd.DataFrame(data)
  21. date_columns = ['date_col']
  22. release_date_resources(df, date_columns)
  24. print(df.info())

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