GusPI
This open-source python package aims to provide statistical support in supply chain analytics and finance/accounting analytics. We welcome everyone to use this python package for personal or professional projects. Please let us know any feedback you have. We'd love to improve the package and add feature enhancements to benefit researchers.
Quick start
$ python3 -m pip install -U plotly
$ python3 -m pip install -U scikit-learn
$ python3 -m pip install -U GusPI
Templates
Templates for suPY
Templates for fiPY
income_statement.csv income_statement_yr.csv income_statement_m.csv balancesheet.csv balance_sheet_yr.csv cashflow.csv
Demo notebook
GusPI.suPY
from GusPI import suPY
metrics
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)
Read sales data from csv file and calculate basic safety sock and reorder point.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
#safety days: 5
#leadtime in days: 7
suPy.basicSafetyStock('SalesData.csv','12LS',5,7)
Read sales data from csv file and calculate basic safety sock and reorder point for all products.
#Example
#sales data from a csv file: salesData.csv
#safety days: 5
#leadtime in days: 7
suPy.basicSafetyStockList('SalesData.csv',5,7)
Read sales data from csv file and calculate safety sock and reorder point.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
#service rate: 0.95
#leadtime in days: 7
suPy.safetyStockwtServiceRate('SalesData.csv','12LS',0.95,7)
Read sales data from csv file and calculate basic safety sock and reorder point for all products.
#Example
#sales data from a csv file: salesData.csv
#service rate: 0.95
#leadtime in days: 7
suPy.safetyStockwtServiceRateList('SalesData.csv',0.95,7)
Read sales data from csv file and calculate coefficient of variation of a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
#CV is non-negative and higher CV indicates higher volatility
suPy.cvPerProduct('SalesData.csv','12LS')
Read sales data from csv file and calculate 'Intercept', 'Slope', 'Mean Absolute Error', 'Mean Squared Error', 'Root Mean Squared Error' of a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
suPy.linearRegressionPerProduct('SalesData.csv','12LS')
Read sales data from csv file and calculate EOQ of a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
#Setup cost: 2000
#Holding cost: 1000
suPy.eoqPerProduct('SalesData.csv','12LS',2000,1000)
Read sales data from csv file and create a list of average quantity sold per year for products.
#Example
#sales data from a csv file: salesData.csv
suPy.avgQtySoldList('SalesData.csv')
Read sales data from csv file and calculate the seasonality index of a product for a given year.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 12LS
#year: 2018
suPy.seasonalityIndexPerProduct('SalesData.csv','22LS',2018)
graphs
Read sales data from csv file and print out a line plot of a product quantity sold.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#print the line plot
suPy.line plotQtyByMonth('salesData.csv','22LS')
Read sales data from csv file and print out a line plot of a product's total cost sold.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#print the line plot
suPy.line plotTotalCostByMonth('salesData.csv','22LS')
Read sales data from csv file and print out a line plot of a product's total sales.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#print the line plot
suPy.line plotTotalSalesByMonth('salesData.csv','22LS')
Read sales data from csv file and print out a line plot of a product's average cost.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#print the line plot
suPy.line plotAverageCostByMonth('salesData.csv','22LS')
Read sales data from csv file and print out a line plot of a product's average sales.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#print the line plot
suPy.line plotAverageSalesPriceByMonth('salesData.csv','22LS')
Read sales data from csv file and print out sales forecast for a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#length in month for the prediction: 12
#print the metrics and line plot
suPy.forecastQtyMonthlySales('SalesData.csv','12LS',12)
Read sales data from csv file and print out pricing forecast for a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#length in month for the prediction: 12
#print the metrics and line plot
suPy.forecastMonthlyPrice('SalesData.csv','12LS',12)
Read sales data from csv file and print out cost forecast for a product.
#Example
#sales data from a csv file: salesData.csv
#product number to perform analysis on: 22LS
#length in month for the prediction: 12
#print the metrics and line plot
suPy.forecastMonthlyCost('SalesData.csv','12LS',12)
GusPI.finPy
from GusPI import finPy
Read financial statements from csv file and print them out as a data frame.
#Example
#balancesheet from a csv file: balance_sheet_yr.csv
#print the statement in a data frame
finPy.printStatement('balance_sheet_yr.csv')
Read financial statements from csv files and provide a single line chart for analysis.
#Example
#income statement from a csv file: income_statement_m.csv
#print a single line chart
finPy.line plot('income_statement_m.csv','total_revenue')
Read financial statements from csv files and provide multiple line charts for analysis.
#Example
#balancesheet from a csv file: balance_sheet_yr.csv
#print multiple line plots
finPy.multiline plots('balance_sheet_yr.csv', '3 year BalanceSheet Graph')
Read financial statements from csv files and provide a bullet chart for analysis.
#Example
#balancesheet from a csv file: balance_sheet_yr.csv
#print financial metrics
finPy.bulletChart('balance_sheet_yr.csv','inventory')
Read financial statements from csv files and provide financial metrics for analysis.
#Example
#balancesheet from a csv file: balance_sheet_yr.csv
#incomeStatement from a csv file: income_statement_3yr.csv
#print financial metrics
finPy.calculateMetrics('balance_sheet_yr.csv','income_statement_12m.csv')
Read financial statements from csv files and provide horizontal analysis for the last two periods.
#Example
#balancesheet from a csv file: balance_sheet_yr.csv
#print financial metrics
finPy.horizontalAnalysisLastTwo('balance_sheet_yr.csv')
GusPI.scraper
The scrape package provides an easy way to scrape Yelp business info and Yelp reviews for a 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)
