The DuplicateLabelError in Pandas is raised when you have duplicate labels in your DataFrame. This can happen when you try to assign a new column or index with a label that already exists in the DataFrame.
Here are a few ways to solve the DuplicateLabelError in Pandas:
- Check for duplicate labels: You can use the duplicated() method to check for duplicate labels in the DataFrame. For example, to check for duplicate column labels, you can use df.columns.duplicated(). If this returns True for any label, it means that the label is duplicated.
- Rename the columns/index: You can rename the columns/index to ensure that they are unique. You can use the rename() method to rename the columns/index. For example, to rename the column “A” to “B”, you can use df.rename(columns={‘A’: ‘B’}, inplace=True).
- Drop the duplicated labels: If you have duplicate labels that are not important, you can drop them using the drop() method. For example, to drop the duplicate columns, you can use df = df.loc[:, ~df.columns.duplicated()].
- Use the append() method: If you are adding new rows or columns to the DataFrame and want to avoid duplicate labels, you can use the append() method. For example, to append a new row to the DataFrame with a unique index, you can use df = df.append({‘A’: 1, ‘B’: 2}, ignore_index=True).
By following these steps, you can solve the DuplicateLabelError in Pandas.