Metadata-Version: 2.1
Name: GusPI
Version: 0.0.15
Summary: A Statistical Support package
Home-page: https://github.com/ygeszvain/GusPI
Author: Randy Geszvain
Author-email: ygeszvain@gmail.com
License: UNKNOWN
Description: ## GusPI
        A package to include statistical supports.
        
        Quick start
        
        ```
        $ python3 -m pip install -U GusPI
        ```
        
        ## GusPI.scraper
        
        The scrape package provides an easy way to scrape Yelp business info and Yelp reviews for specific business.
        
        ```
        from GusPI import scraper
        ```
        
        YelpBizInfo
        The function collects business info and save it into a csv file.
        
        ```
        #Example
        
        #declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
        CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
        
        #scrape the business info
        scraper.YelpBizInfo(CUISINES)
        ```
        
        YelpReview
        The function collects reviews for respective business and save them into separate files by business names.
        ```
        #Example
        
        #declare a list: https://www.yelp.com/biz/`artisan-ramen-milwaukee`
        CUISINES = ['artisan-ramen-milwaukee','red-light-ramen-milwaukee-5']
        
        #scrape the business info
        scraper.YelpReview(CUISINES)
        ```
        
        ## GusPI.suPY
        
        ```
        from GusPI import suPY
        ```
        
        This package provides several analytical formulas to support supply chain analytics.
        
        Economic order quantity
        EOQ(demand, mean, STD, C, Ce, Cs, Ct)
        
        Perfect Order Measurement
        POM(TotalOrders, ErrorOrders)
        
        Fill Rate
        FR(TotalItems, ShippedItems)
        
        Inventory Days of Supply
        IDS(InventoryOnHand,AvgDailyUsage)
        
        Freight cost per unit
        FCU(TotalFreightCost,NumberOfItems)
        
        Inventory Turnover
        IT(COGS,AvgInventory)
        
        Days of Supply (DOS)
        DOS(AvgInventory,MonthlyDemand)
        
        Gross Margin Return on Investment (GMROI)
        GMROI(GrossProfit, OpeningStock, ClosingStock)
        
        Inventory Accuracy
        IA(ItemCounts, TotalItemCounts)
        
        Storage Utilization Rate
        SUR(InventoryCube, TotalWarehouseCube)
        
        Total Order Cycle Time
        TOCT(TimeOrderReceivedbyCustomer, TimeOrderPlaced,TotalNumberofOrdersShipped)
        
        Internal Order Cycle Time
        IOCT(TimeOrderShipped, TimeOrderReceived, NumberofOrdersShipped)
        
        ## GusPI.finPy
        
        ```
        from GusPI import finPy
        ```
        
        Read financial statements from csv file and print them out as a dataframe.
        
        ```
        #Example
        
        #balancesheet from a csv file: balance_sheet_3yr.csv
        
        #print the statement in a dataframe
        finPy.printStatement('balance_sheet_3yr.csv')
        ```
        
        Read financial statements from csv files and provide multiple line charts for analysis.
        
        ```
        #Example
        
        #balancesheet from a csv file: balance_sheet_3yr.csv
        
        #print multiple multiple line charts
        finPy.multiLinecharts('balance_sheet_3yr.csv', '3 year BalanceSheet Graph')
        ```
        
        Read financial statements from csv files and provide financial metrics for analysis.
        
        ```
        #Example
        
        #balancesheet from a csv file: balance_sheet_3yr.csv
        #incomeStatement from a csv file: income_statement_3yr.csv
        
        #print financial metrics
        finPy.calculateMetrics('balance_sheet_3yr.csv','income_statement_12m.csv')
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
