It sounds like you’re in a bit of a pickle trying to add rows to your DataFrame in Pandas! Adding rows one at a time can be a pain if you’re not careful, especially when you think about performance.

So yeah, using `pd.DataFrame.append()` is super easy and it looks really clean, but you’re right—it’s not the best choice if you’re doing a lot of appending. Each time you append, it creates a whole new DataFrame behind the scenes, which can slow things down a lot if you have a lot of data.

A better way to handle this might be to first collect your rows in a list (or even a dictionary, depending on what you’re doing) and then convert that list/dictionary into a DataFrame all at once. It’s kind of like batching your work, which tends to be a lot quicker than adding one row at a time. It feels a bit weird at first, but it really does save time in the long run.

Here’s a simple way to do it:

        data = []  # Create an empty list
        for i in range(num_rows):  # Just an example loop
            new_row = {'column1': value1, 'column2': value2}  # Replace with your values
            data.append(new_row)  # Add new row as a dict to the list
        
        df = pd.DataFrame(data)  # Create DataFrame all at once
    

This method keeps things efficient while still letting you work with real-time input or loops. Once everything is assembled in your list, you just create the DataFrame in one go.

If you really need to add rows as you go and can’t wait, there’s another option too—use the `pd.concat()` function, which is a bit more efficient than `append()`. You can build a new DataFrame for each new row and then concatenate it with your existing DataFrame, but that still works best if you keep your main DataFrame rather static and only update it occasionally.

Just remember, every operation you do that creates a new DataFrame can add overhead, so try to do it in bulk when you can. Hope that helps clear things up a bit!