Source code for Fast Python (2020) by Chris Conlan

Paperback available for purchase on Amazon.

The following code profiles can be run as stand-alone scripts. They may or may not depend on explanation provided in the accompanying book.

• Binary search: binary_search.py
• Dictionary construction: build_dict.py
• Concatenating strings, string construction: concatenate_strings.py
• Counting the frequency of a value: count_occurrences.py
• Computing a cumulative sum: cumulative_sum.py
• The in operator and early stopping: early_stopping.py
• Time series filters/convolutions: filters.py
• Find largest k values in a list: find_top_k.py
• List construction/declaration/flattening: flatten_lists.py
• Counting lines in a file: line_count.py
• Set intersection, finding matches in a list: match_within.py
• Matrix multiplication: matrix_multiplication.py
• Computing moving averages: moving_averages.py
• Counting frequency of a word in text: occurrences_of.py
• Looping through pd.DataFrame objects: pandas_loops.py
• Sorting algorithms: sorting.py
• Low-level sorting algorithms: sorting_v2.py
• Adding a list of numbers: sum.py

Running them is simple ...

cd fast-python/src python cumulative_sum.py 

All the profiles use a simple profiling module in src/utils/profiler.py. It produces tables and charts like the following.

np_fast_cusum n = 56234132 values t = 201.806 ms n/t = 278653.8114 values per ms np_fast_cusum n = 100000000 values t = 350.611 ms n/t = 285216.7553 values per ms ... function n_values t_milliseconds values_per_ms 0 slow_cusum 1 0.012 85.0196 1 slow_cusum 3 0.005 640.7530 ... 14 slow_cusum 5623 1298.218 4.3313 15 slow_cusum 10000 4140.327 2.4153 ... 30 slow_cusum_expanded 5623 1878.419 2.9935 31 slow_cusum_expanded 10000 5767.316 1.7339 ... 62 python_fast_cusum 56234132 5727.162 9818.8478 63 python_fast_cusum 100000000 10939.993 9140.7733 ... 94 pandas_fast_cusum 56234132 442.652 127039.2437 95 pandas_fast_cusum 100000000 780.461 128129.3962 ... 126 numba_fast_cusum 56234132 139.602 402816.3295 127 numba_fast_cusum 100000000 236.445 422930.9936 ... 158 np_fast_cusum 56234132 201.806 278653.8114 159 np_fast_cusum 100000000 350.611 285216.7553 

I use the profiler frequently in my own work. It allows me to analyze the relationship between computational complexity and raw execution time pretty easily.

I have included a dependencies.txt, but you should be fine with a blank Python 3 environment followed by ...

pip install numpy pandas numba joblib matplotlib pillow