The application of pandas' apply and applymap functions is causing considerable delay when processing a sizable dataset.

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There are several possible reasons for why the application of pandas' apply and applymap functions may be causing considerable delay when processing a sizable dataset. Some of these reasons include:

1. Inefficient code: If the code used to apply the functions is not optimized or contains unnecessary loops, calculations, or operations, it can slow down the processing time significantly.

2. Large dataset size: If the dataset being processed is overly large, it can require a significant amount of time and resources to perform the apply and applymap functions, especially if the dataset is not properly optimized for memory usage.

3. Complex operations: If the operations being performed by the apply and applymap functions are too complex or involve multiple nested functions or calculations, it can lead to slow processing times.

4. Resource limitations: If the system running the code does not have sufficient resources such as memory, processing power, or disk space, it can slow down the processing time of apply and applymap functions.

To improve the performance of pandas' apply and applymap functions, you can optimize the code, reduce the dataset size, simplify the operations being performed, or increase the available resources. Additionally, you may also consider using alternative functions such as pandas' map or apply functions with the appropriate axis option, or experimenting with vectorized operations using numpy or pandas' built-in functions.