How can the issue of poor performance caused by frequent use of `frame.insert` be addressed when DataFrame is significantly fragmented?

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There are a few ways to address the issue of poor performance caused by frequent use of frame.insert when the DataFrame is significantly fragmented:

1. Use pre-allocation: Instead of using frame.insert to add new rows to the DataFrame, pre-allocate the required space for the DataFrame and then fill it in. This way, the DataFrame will not need to be constantly resized, reducing the frequency of memory reallocations.

2. Sort the DataFrame: If possible, sort the DataFrame by one or more columns to reduce fragmentation. This can improve the performance of operations that require contiguous memory access.

3. Use concat instead of frame.insert: Consider using the concat function to add new rows to the DataFrame instead of frame.insert. concat works by concatenating multiple DataFrames together, and can be more efficient than using frame.insert repeatedly.

4. Use a more memory-efficient data structure: If the DataFrame is too large and fragmented to be efficiently operated on using pandas, consider using a more memory-efficient data structure such as a database or distributed computing framework. This can help to improve the performance of operations that require frequent modifications of the DataFrame.