MultiProcessingBenchmark 0.1.8

Benchmarking library single core vs multi core for common pandas functions

# MultiProcessingBenchmark A benchmarking library to check how does your system fares with all the cores for simple statistical functions, utility functions and aggregation functions.

Usage:

from MultiProcessingBenchmark import EntryPoint import multiprocessing

bench = EntryPoint.Benchmark() n_cores = multiprocessing.cpu_count() # or specify any number of cores you want to use val = 96.50 # to be used to search for a particular value, enter a value between 1 - 100, in decimal format rows = 375000 # number of rows for the dataset other_df_rows = 375000 # number of rows for the second dataset, to be used in merge and join first_df_start = ‘01-02-2020’ # start of time series data of first dataset second_df_start = ‘02-15-2020’ # # start of time series data of second dataset

# Simple statistical functions used are count, sum, mean, standard deviation, rolling mean bench.SimpleStatistics(n_cores, rows, first_df_start)

# utility functions are merge, merge_asof, join, concat, sort, search #other_df_rows is number of rows to be used in second dataset bench.utilFunctions(val, n_cores, rows, other_df_rows, second_df_start, second_df_start)

#groupby aggregation function used are sum, count, mean, prod, without loops bench.agg_without_loop(n_cores, rows, first_df_start)

#groupby aggregation function used are sum, count, mean, prod, with loops bench.agg_with_loops(n_cores, rows, first_df_start)