cProfile helps locate performance costs in Python programs by running a function and recording timing statistics for every involved method. A Huffman encoder provides a concrete target: profile repeated executions, then isolate heap-related functions instead of interpreting the entire report. section 2.10.1
The collected statistics support several levels of analysis:
- Per-call time measures the cost associated with individual calls. section 2.10.1
- Total and cumulative time expose costs across repeated calls and nested execution. section 2.10.1
- pStats.Stats can print, process, or expose results as a dictionary, enabling filters for selected functions and CSV output. section 2.10.1
Profiling Huffman encoding
Profile huffman.create_encoding repeatedly, then examine _frequency_table_to_heap, _heap_to_tree, _heapify, top, and insert. Filtering these entries reduces report noise and supports more reliable comparisons. section 2.10.1
Use the profiling output figure to inspect the tabular shape of collected execution statistics. section 2.10.1
