Metadata-Version: 1.0
Name: MultiProcessingBenchmark
Version: 0.1.8
Summary: Benchmarking library single core vs multi core for common pandas functions
Home-page: https://github.com/srivassid/MultiProcessingBenchmark
Author: Siddharth Srivastava
Author-email: s.srivas@hotmail.com
License: MIT
Description: # 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)
        
Platform: UNKNOWN
