Metadata-Version: 2.1
Name: keepit
Version: 0.2.2
Summary: advanced memoization/caching of functions with data analytics in mind
Home-page: https://github.com/balkian/keepit
Author: J. Fernando Sanchez
Author-email: balkian@gmail.com
License: Apache License 2.0
Download-URL: https://github.com/balkian/keepit/archive/0.2.2.tar.gz
Description: # KEEP IT
        
        This is a **WORK IN PROGRESS**.
        
        `keepit` provides advanced memoization to disk for functions.
        In other words, it records the results of important functions between executions.
        
        `keepit` saves the results of calling a function to disk, so calling the function with the exact same parameters will re-use the stored copy of the results, leading to much faster times.
        
        
        Example usage:
        
        ```
        import pandas as pd
        from keepit import keepit
        
        @keepit('myresults.tsv')
        def expensive_function(number=1):
            df = pd.DataFrame()
            # Perform a really expensive operation, maybe access to disk?
            return df
        
        # When a results file for the function does not exist
        # this may take a long time
        expensive_function(number=1)
        # Now a myresults.tsv_{some hash) has been generated
        
        # This is almost instantaneous:
        expensive_function(number=1)
        
        # Files are specific to each parameter execution, 
        # so this will again take a long time:
        expensive_function(number=42)
        # After this, we should have two files, one for number=1, 
        # and another one for number=42.
        
        ```
        
        
        
        
        
Keywords: data analysis,memoization,cache
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >3.3
Description-Content-Type: text/markdown
