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lcacoffee

script that displays coffees sold by hour at lca2015. Currently it opens a .json file and converts it into a python dict.

It's missing monday data.

sale by hour is: key - the hour (24hr). Value is total paid sales (people who ran out of vouchers). Number in brackets is total number of coffees sold.

Number in brackets - Number not in brackets = amount of coffees sold with vouchers.

Each sale[day].json file contains a json objects. Important keys/values are:

Product : cappuccino/flat white etc...

Tags : These are taged with coffee followed by ; and if its free ;.

count : amount of these coffees sold.

SKU : ? guess its a ID?

Sales ($) : Amount of dollars from selling coffee

Tax ($) : Amount of tax paid from selling the coffee

Revenue : This is Sales + Tax

Costs ($) : Cost to make the coffees.

Revenue ($) : Amount of revenue made from selling the coffee

Margin ($) : Percent of money made from selling coffee.

There are other keys but the value of them is empty. No point dealing with them.

Open up all the sale[day].json files and create a all salesfile. Append all together.

Amount of recenue made for each product.

Create list of all coffees

Reverse Engineer this - Sell coffee rather than looking at sales. count +1 on product count. cost/tax etc goes up.

Add date + time that coffee was sold.

For each count how much cost/tax/revenue.





In [1]:



    


import json
import os
import pandas
import getpass



    











In [2]:



    


theuser = getpass.getuser()

Need to open all the sale[day].json files and append data.





In [3]:



    


jsonfold = ('/home/' + theuser + '/github/lcacoffee/')
alldata = ('salebyhour.json')
tueda = ('saletues.json')
weda = ('saleweds.json')
thura = ('salethurs.json')
fria = ('salefri.json')
salhr = (jsonfold + alldata)
salajs = ('/home/wcmckee/cofres/salesall.json')

Read rows in pandas only to 12.





In [17]:



    


fripan = pandas.read_json('/home/wcmckee/github/lcacoffee/salefri.json')



    











In [29]:



    


fricvs = fripan.to_csv()



    











In [30]:



    


savcsv = open('/home/wcmckee/cofres/saltest.csv', 'w')
savcsv.write(fricvs)
savcsv.close()



    











In [34]:



    


pnrdz = pandas.read_csv('/home/wcmckee/cofres/saltest.csv', index_col=7)



    











In [37]:



    


pnrdz[0:13]



    


















    
Out[37]:










  
    

      
      Unnamed: 0
      Brand
      Cost ($)
      Count
      Discount ($)
      Margin (%)
      Outlet
      Revenue ($)
      SKU
      Sales ($)
      Sales incl. tax ($)
      Supplier
      Supplier Code
      Tags
      Tax ($)
      Type
    

    

      Product
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      Cappuccino
      0
      
      13
      13
      0
      71.24
      LCA
      32.21738
      10001
      45.21738
      52
      
      
      coffee
      6.78262
      
    

    

      Cappuccino FREE
      1
      
      0
      63
      0
      0.00
      NaN
      0.00000
      10009
      0.00000
      0
      
      
      coffee; free
      0.00000
      
    

    

      Chai Latte
      2
      
      1
      1
      0
      71.24
      NaN
      2.47826
      10003
      3.47826
      4
      
      
      coffee
      0.52174
      
    

    

      Chai Latte FREE
      3
      
      0
      9
      0
      0.00
      NaN
      0.00000
      10010
      0.00000
      0
      
      
      coffee; free
      0.00000
      
    

    

      Espresso FREE
      4
      
      0
      14
      0
      0.00
      NaN
      0.00000
      10032
      0.00000
      0
      
      
      
      0.00000
      
    

    

      Flat White
      5
      
      57
      57
      0
      71.24
      NaN
      141.26082
      10000
      198.26082
      228
      
      
      coffee
      29.73918
      
    

    

      Flat White FREE
      6
      
      0
      249
      0
      0.00
      NaN
      0.00000
      10011
      0.00000
      0
      
      
      coffee; free
      0.00000
      
    

    

      Hot Chocolate
      7
      
      7
      7
      0
      71.24
      NaN
      17.34782
      10005
      24.34782
      28
      
      
      coffee
      3.65218
      
    

    

      Hot Chocolate FREE
      8
      
      0
      121
      0
      0.00
      NaN
      0.00000
      10012
      0.00000
      0
      
      
      coffee; free
      0.00000
      
    

    

      Long Black
      9
      
      25
      25
      0
      71.24
      NaN
      61.95650
      10006
      86.95650
      100
      
      
      coffee
      13.04350
      
    

    

      Long Black FREE
      10
      
      0
      62
      0
      0.00
      NaN
      0.00000
      10014
      0.00000
      0
      
      
      coffee; free
      0.00000
      
    

    

      Mocha
      11
      
      13
      13
      0
      71.24
      NaN
      32.21738
      10004
      45.21738
      52
      
      
      coffee
      6.78262
      
    

    

      Mocha FREE
      12
      
      0
      81
      0
      0.00
      NaN
      0.00000
      10015
      0.00000
      0
      
      
      coffee; free
      0.00000

What the hell. This was working the other day. the filza = (lcacoffee folder + each saleday file.json)





In [4]:



    


lisz = [tueda, weda, thura, fria]
opaz = open('/home/wcmckee/cofres/salesall.json', 'w')

filza = ('/home/' + theuser + '/github/lcacoffee/' + lis)



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-65dacff08a9a> in <module>()
      2 opaz = open('/home/wcmckee/cofres/salesall.json', 'w')
      3 
----> 4 filza = ('/home/' + theuser + '/github/lcacoffee/' + lis)

NameError: name 'lis' is not defined
















In [16]:



    


for lis in lisz:
    opaz = open(salajs, 'a')
    print ('/home/' + theuser + '/github/lcacoffee/' + lis)
    opdayz = open(filza, 'r')
    opaz.write(str(opdayz.read()))
    opaz.close()
    opdayz.close()



    


















    






/home/wcmckee/github/lcacoffee/saletues.json
/home/wcmckee/github/lcacoffee/saleweds.json
/home/wcmckee/github/lcacoffee/salethurs.json
/home/wcmckee/github/lcacoffee/salefri.json

















In [17]:



    


opaz.close()



    











In [18]:



    


opzall = open(salajs, 'r')

The daysales appended into the allsales file are in fact lists.

Options include removing the lists and turning it all into one big json object

Appending all the data into one day.

eg: Product: "cappuccino" Count: 450 (total of week).

File needs steriozie removing the \n





In [19]:



    


opzall.read()



    


















    
Out[19]:








'[\r\n  {\r\n    "Outlet":"LCA",\r\n    "Product":"Cappuccino",\r\n    "SKU": 10001,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 2,\r\n    "Sales ($)": 6.9565200000,\r\n    "Tax ($)": 1.0434800000,\r\n    "Sales incl. tax ($)": 8.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 2.0000000000,\r\n    "Revenue ($)": 4.9565200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Cappuccino FREE",\r\n    "SKU": 10009,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 69,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Chai Latte FREE",\r\n    "SKU": 10010,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 15,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Espresso FREE",\r\n    "SKU": 10032,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 7,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White",\r\n    "SKU": 10000,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 17,\r\n    "Sales ($)": 59.1304200000,\r\n    "Tax ($)": 8.8695800000,\r\n    "Sales incl. tax ($)": 68.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 17.0000000000,\r\n    "Revenue ($)": 42.1304200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White FREE",\r\n    "SKU": 10011,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 242,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate",\r\n    "SKU": 10005,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 5,\r\n    "Sales ($)": 17.3913000000,\r\n    "Tax ($)": 2.6087000000,\r\n    "Sales incl. tax ($)": 20.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 5.0000000000,\r\n    "Revenue ($)": 12.3913000000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate FREE",\r\n    "SKU": 10012,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 68,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black",\r\n    "SKU": 10006,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 6,\r\n    "Sales ($)": 20.8695600000,\r\n    "Tax ($)": 3.1304400000,\r\n    "Sales incl. tax ($)": 24.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 6.0000000000,\r\n    "Revenue ($)": 14.8695600000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black FREE",\r\n    "SKU": 10014,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 73,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Macchiato FREE",\r\n    "SKU": 10030,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 4,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha",\r\n    "SKU": 10004,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 3,\r\n    "Sales ($)": 10.4347800000,\r\n    "Tax ($)": 1.5652200000,\r\n    "Sales incl. tax ($)": 12.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 3.0000000000,\r\n    "Revenue ($)": 7.4347800000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha FREE",\r\n    "SKU": 10015,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 61,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Grand Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  }\r\n][\r\n  {\r\n    "Outlet":"LCA",\r\n    "Product":"Cappuccino",\r\n    "SKU": 10001,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 2,\r\n    "Sales ($)": 6.9565200000,\r\n    "Tax ($)": 1.0434800000,\r\n    "Sales incl. tax ($)": 8.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 2.0000000000,\r\n    "Revenue ($)": 4.9565200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Cappuccino FREE",\r\n    "SKU": 10009,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 69,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Chai Latte FREE",\r\n    "SKU": 10010,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 15,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Espresso FREE",\r\n    "SKU": 10032,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 7,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White",\r\n    "SKU": 10000,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 17,\r\n    "Sales ($)": 59.1304200000,\r\n    "Tax ($)": 8.8695800000,\r\n    "Sales incl. tax ($)": 68.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 17.0000000000,\r\n    "Revenue ($)": 42.1304200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White FREE",\r\n    "SKU": 10011,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 242,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate",\r\n    "SKU": 10005,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 5,\r\n    "Sales ($)": 17.3913000000,\r\n    "Tax ($)": 2.6087000000,\r\n    "Sales incl. tax ($)": 20.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 5.0000000000,\r\n    "Revenue ($)": 12.3913000000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate FREE",\r\n    "SKU": 10012,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 68,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black",\r\n    "SKU": 10006,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 6,\r\n    "Sales ($)": 20.8695600000,\r\n    "Tax ($)": 3.1304400000,\r\n    "Sales incl. tax ($)": 24.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 6.0000000000,\r\n    "Revenue ($)": 14.8695600000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black FREE",\r\n    "SKU": 10014,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 73,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Macchiato FREE",\r\n    "SKU": 10030,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 4,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha",\r\n    "SKU": 10004,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 3,\r\n    "Sales ($)": 10.4347800000,\r\n    "Tax ($)": 1.5652200000,\r\n    "Sales incl. tax ($)": 12.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 3.0000000000,\r\n    "Revenue ($)": 7.4347800000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha FREE",\r\n    "SKU": 10015,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 61,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Grand Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  }\r\n][\r\n  {\r\n    "Outlet":"LCA",\r\n    "Product":"Cappuccino",\r\n    "SKU": 10001,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 2,\r\n    "Sales ($)": 6.9565200000,\r\n    "Tax ($)": 1.0434800000,\r\n    "Sales incl. tax ($)": 8.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 2.0000000000,\r\n    "Revenue ($)": 4.9565200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Cappuccino FREE",\r\n    "SKU": 10009,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 69,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Chai Latte FREE",\r\n    "SKU": 10010,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 15,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Espresso FREE",\r\n    "SKU": 10032,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 7,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White",\r\n    "SKU": 10000,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 17,\r\n    "Sales ($)": 59.1304200000,\r\n    "Tax ($)": 8.8695800000,\r\n    "Sales incl. tax ($)": 68.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 17.0000000000,\r\n    "Revenue ($)": 42.1304200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White FREE",\r\n    "SKU": 10011,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 242,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate",\r\n    "SKU": 10005,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 5,\r\n    "Sales ($)": 17.3913000000,\r\n    "Tax ($)": 2.6087000000,\r\n    "Sales incl. tax ($)": 20.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 5.0000000000,\r\n    "Revenue ($)": 12.3913000000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate FREE",\r\n    "SKU": 10012,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 68,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black",\r\n    "SKU": 10006,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 6,\r\n    "Sales ($)": 20.8695600000,\r\n    "Tax ($)": 3.1304400000,\r\n    "Sales incl. tax ($)": 24.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 6.0000000000,\r\n    "Revenue ($)": 14.8695600000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black FREE",\r\n    "SKU": 10014,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 73,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Macchiato FREE",\r\n    "SKU": 10030,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 4,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha",\r\n    "SKU": 10004,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 3,\r\n    "Sales ($)": 10.4347800000,\r\n    "Tax ($)": 1.5652200000,\r\n    "Sales incl. tax ($)": 12.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 3.0000000000,\r\n    "Revenue ($)": 7.4347800000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha FREE",\r\n    "SKU": 10015,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 61,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Grand Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  }\r\n][\r\n  {\r\n    "Outlet":"LCA",\r\n    "Product":"Cappuccino",\r\n    "SKU": 10001,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 2,\r\n    "Sales ($)": 6.9565200000,\r\n    "Tax ($)": 1.0434800000,\r\n    "Sales incl. tax ($)": 8.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 2.0000000000,\r\n    "Revenue ($)": 4.9565200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Cappuccino FREE",\r\n    "SKU": 10009,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 69,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Chai Latte FREE",\r\n    "SKU": 10010,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 15,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Espresso FREE",\r\n    "SKU": 10032,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 7,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White",\r\n    "SKU": 10000,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 17,\r\n    "Sales ($)": 59.1304200000,\r\n    "Tax ($)": 8.8695800000,\r\n    "Sales incl. tax ($)": 68.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 17.0000000000,\r\n    "Revenue ($)": 42.1304200000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Flat White FREE",\r\n    "SKU": 10011,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 242,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate",\r\n    "SKU": 10005,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 5,\r\n    "Sales ($)": 17.3913000000,\r\n    "Tax ($)": 2.6087000000,\r\n    "Sales incl. tax ($)": 20.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 5.0000000000,\r\n    "Revenue ($)": 12.3913000000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Hot Chocolate FREE",\r\n    "SKU": 10012,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 68,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black",\r\n    "SKU": 10006,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 6,\r\n    "Sales ($)": 20.8695600000,\r\n    "Tax ($)": 3.1304400000,\r\n    "Sales incl. tax ($)": 24.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 6.0000000000,\r\n    "Revenue ($)": 14.8695600000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Long Black FREE",\r\n    "SKU": 10014,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 73,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Macchiato FREE",\r\n    "SKU": 10030,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" ",\r\n    "Supplier Code":" ",\r\n    "Count": 4,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha",\r\n    "SKU": 10004,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee",\r\n    "Supplier Code":" ",\r\n    "Count": 3,\r\n    "Sales ($)": 10.4347800000,\r\n    "Tax ($)": 1.5652200000,\r\n    "Sales incl. tax ($)": 12.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 3.0000000000,\r\n    "Revenue ($)": 7.4347800000,\r\n    "Margin (%)": 71.24\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Mocha FREE",\r\n    "SKU": 10015,\r\n    "Brand":" ",\r\n    "Supplier":" ",\r\n    "Type":" ",\r\n    "Tags":" coffee; free",\r\n    "Supplier Code":" ",\r\n    "Count": 61,\r\n    "Sales ($)": 0.0000000000,\r\n    "Tax ($)": 0.0000000000,\r\n    "Sales incl. tax ($)": 0.00,\r\n    "Discount ($)": 0,\r\n    "Cost ($)": 0.0000000000,\r\n    "Revenue ($)": 0.0000000000,\r\n    "Margin (%)": 0.00\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  },\r\n  {\r\n    "Outlet":"",\r\n    "Product":"Grand Total",\r\n    "SKU":null,\r\n    "Brand":"",\r\n    "Supplier":"",\r\n    "Type":"",\r\n    "Tags":"",\r\n    "Supplier Code":"",\r\n    "Count":572.00,\r\n    "Sales ($)": 114.76,\r\n    "Tax ($)": 17.19,\r\n    "Sales incl. tax ($)": 131.95,\r\n    "Discount ($)": 0.00,\r\n    "Cost ($)": 33.00,\r\n    "Revenue ($)": 81.76,\r\n    "Margin (%)": 71.24433\r\n  }\r\n]'

















In [20]:



    


opcvs = open(salhr, 'r')



    











In [21]:



    


opzrd = opcvs.read()



    











In [22]:



    


jdunp = json.loads(opzrd)



    











In [23]:



    


valia = []



    











In [24]:



    


#pandas.read_json(jdunp)



    











In [25]:



    


jdunp.count(int)



    


















    
Out[25]:








0

















In [26]:



    


len(jdunp)



    


















    
Out[26]:








6

ok if i cycle through jdunp between 0 and 23 i get the results.

cycle through ints but as a string. must add ' '

Break down coffee sales by hour.

Ignore/delete hours with zero sales. Need to create new dict with this data.

How would it look?





In [27]:



    


for numtwn in range(0,24):
    print "'" + str(numtwn) + "'"



    


















    






'0'
'1'
'2'
'3'
'4'
'5'
'6'
'7'
'8'
'9'
'10'
'11'
'12'
'13'
'14'
'15'
'16'
'17'
'18'
'19'
'20'
'21'
'22'
'23'

















In [28]:



    


for jdr in jdunp:
    print jdr['0']



    


















    






- -
- -
- -
- -
- -
- -

















In [29]:



    


for numtwn in range(0,24):
        print "'" + str(numtwn) + "'"



    


















    






'0'
'1'
'2'
'3'
'4'
'5'
'6'
'7'
'8'
'9'
'10'
'11'
'12'
'13'
'14'
'15'
'16'
'17'
'18'
'19'
'20'
'21'
'22'
'23'

















In [30]:



    


for dej in jdunp:
    print dej.values()
    valia.append(dej.values())



    


















    






[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u' 11 Jan 2015', u'', u'Sun', u'- -', u'- -', u'- -', u'4 (1)', u'0 (9)', u'12 (30)', u'0 (3)', u'8 (9)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'8 (9)', u'- -', u'40 (31)', u'44 (123)', u' 12 Jan 2015', u'', u'Mon', u'4 (13)', u'20 (118)', u'8 (52)', u'12 (34)', u'12 (46)', u'4 (33)', u'- -', u'0 (1)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'4 (17)', u'- -', u'0 (23)', u'24 (109)', u' 13 Jan 2015', u'', u'Tue', u'8 (25)', u'32 (101)', u'12 (55)', u'16 (43)', u'20 (100)', u'12 (43)', u'- -', u'4 (3)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'4 (19)', u'- -', u'8 (24)', u'20 (132)', u' 14 Jan 2015', u'', u'Wed', u'12 (39)', u'32 (133)', u'56 (64)', u'16 (36)', u'52 (83)', u'44 (38)', u'- -', u'- -', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'8 (20)', u'- -', u'20 (25)', u'68 (115)', u' 15 Jan 2015', u'', u'Thu', u'40 (40)', u'132 (136)', u'52 (55)', u'36 (50)', u'72 (103)', u'32 (49)', u'- -', u'4 (6)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'12 (21)', u'- -', u'8 (15)', u'104 (128)', u' 16 Jan 2015', u'', u'Fri', u'28 (41)', u'112 (131)', u'40 (76)', u'40 (54)', u'92 (102)', u'28 (64)', u'- -', u'- -', u'- -', u'- -']

















In [30]:



    






    











In [31]:



    


dezrand = len(valia)



    











In [32]:



    


azlis = []



    











In [33]:



    


for vals in valia:    
    print vals
    azlis.append(vals)



    


















    






[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u' 11 Jan 2015', u'', u'Sun', u'- -', u'- -', u'- -', u'4 (1)', u'0 (9)', u'12 (30)', u'0 (3)', u'8 (9)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'8 (9)', u'- -', u'40 (31)', u'44 (123)', u' 12 Jan 2015', u'', u'Mon', u'4 (13)', u'20 (118)', u'8 (52)', u'12 (34)', u'12 (46)', u'4 (33)', u'- -', u'0 (1)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'4 (17)', u'- -', u'0 (23)', u'24 (109)', u' 13 Jan 2015', u'', u'Tue', u'8 (25)', u'32 (101)', u'12 (55)', u'16 (43)', u'20 (100)', u'12 (43)', u'- -', u'4 (3)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'4 (19)', u'- -', u'8 (24)', u'20 (132)', u' 14 Jan 2015', u'', u'Wed', u'12 (39)', u'32 (133)', u'56 (64)', u'16 (36)', u'52 (83)', u'44 (38)', u'- -', u'- -', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'8 (20)', u'- -', u'20 (25)', u'68 (115)', u' 15 Jan 2015', u'', u'Thu', u'40 (40)', u'132 (136)', u'52 (55)', u'36 (50)', u'72 (103)', u'32 (49)', u'- -', u'4 (6)', u'- -', u'- -']
[u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'- -', u'12 (21)', u'- -', u'8 (15)', u'104 (128)', u' 16 Jan 2015', u'', u'Fri', u'28 (41)', u'112 (131)', u'40 (76)', u'40 (54)', u'92 (102)', u'28 (64)', u'- -', u'- -', u'- -', u'- -']

I need to filter the - - from the results. I really only need the values that have numbers.

Take number in brackets away from number not in brackets. The number in brackets is total amount of coffees sold. The number not in brackets is amount of volchers used. The number that I get when i take away is the coffee sold without volchers.

New dict that shows only the times that coffee were sold and the amount of coffgfges that were solf. Maybe that would works.





In [34]:



    


betra = []



    











In [35]:



    


for azl in azlis:
    betra.append(azl)



    











In [36]:



    


anoe = []
anez = []



    











In [37]:



    


for betr in betra:
    betr.append(anoe)



    











In [38]:



    


for deta in betr:
    #print deta
    if '- -' in deta:
        print deta
    else:
        anez.append(deta)



    


















    






- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -
- -

















In [39]:



    


fdic = []



    











In [39]:



    






    











In [40]:



    


for resut in anez:
    print resut
    fdic.append(resut)



    


















    






12 (21)
8 (15)
104 (128)
 16 Jan 2015

Fri
28 (41)
112 (131)
40 (76)
40 (54)
92 (102)
28 (64)
[]

How come it is only adding the wednesday data in the results. It needs to have all the datas.

Needs to take the number in brackets away from the number not in brackets.





In [41]:



    


fdic



    


















    
Out[41]:








[u'12 (21)',
 u'8 (15)',
 u'104 (128)',
 u' 16 Jan 2015',
 u'',
 u'Fri',
 u'28 (41)',
 u'112 (131)',
 u'40 (76)',
 u'40 (54)',
 u'92 (102)',
 u'28 (64)',
 []]

















In [42]:



    


optue = open('/home/wcmckee/Downloads/saletues.json', 'r')



    











In [43]:



    


rdtue = optue.read()



    











In [44]:



    


tuejs = json.loads(rdtue)



    











In [45]:



    


tuejs



    


















    
Out[45]:








[{u'Brand': u' ',
  u'Cost ($)': 2.0,
  u'Count': 2,
  u'Discount ($)': 0,
  u'Margin (%)': 71.24,
  u'Outlet': u'LCA',
  u'Product': u'Cappuccino',
  u'Revenue ($)': 4.95652,
  u'SKU': 10001,
  u'Sales ($)': 6.95652,
  u'Sales incl. tax ($)': 8.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee',
  u'Tax ($)': 1.04348,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 69,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Cappuccino FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10009,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 15,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Chai Latte FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10010,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 7,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Espresso FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10032,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' ',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 17.0,
  u'Count': 17,
  u'Discount ($)': 0,
  u'Margin (%)': 71.24,
  u'Outlet': u'',
  u'Product': u'Flat White',
  u'Revenue ($)': 42.13042,
  u'SKU': 10000,
  u'Sales ($)': 59.13042,
  u'Sales incl. tax ($)': 68.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee',
  u'Tax ($)': 8.86958,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 242,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Flat White FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10011,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 5.0,
  u'Count': 5,
  u'Discount ($)': 0,
  u'Margin (%)': 71.24,
  u'Outlet': u'',
  u'Product': u'Hot Chocolate',
  u'Revenue ($)': 12.3913,
  u'SKU': 10005,
  u'Sales ($)': 17.3913,
  u'Sales incl. tax ($)': 20.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee',
  u'Tax ($)': 2.6087,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 68,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Hot Chocolate FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10012,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 6.0,
  u'Count': 6,
  u'Discount ($)': 0,
  u'Margin (%)': 71.24,
  u'Outlet': u'',
  u'Product': u'Long Black',
  u'Revenue ($)': 14.86956,
  u'SKU': 10006,
  u'Sales ($)': 20.86956,
  u'Sales incl. tax ($)': 24.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee',
  u'Tax ($)': 3.13044,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 73,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Long Black FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10014,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 4,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Macchiato FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10030,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' ',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 3.0,
  u'Count': 3,
  u'Discount ($)': 0,
  u'Margin (%)': 71.24,
  u'Outlet': u'',
  u'Product': u'Mocha',
  u'Revenue ($)': 7.43478,
  u'SKU': 10004,
  u'Sales ($)': 10.43478,
  u'Sales incl. tax ($)': 12.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee',
  u'Tax ($)': 1.56522,
  u'Type': u' '},
 {u'Brand': u' ',
  u'Cost ($)': 0.0,
  u'Count': 61,
  u'Discount ($)': 0,
  u'Margin (%)': 0.0,
  u'Outlet': u'',
  u'Product': u'Mocha FREE',
  u'Revenue ($)': 0.0,
  u'SKU': 10015,
  u'Sales ($)': 0.0,
  u'Sales incl. tax ($)': 0.0,
  u'Supplier': u' ',
  u'Supplier Code': u' ',
  u'Tags': u' coffee; free',
  u'Tax ($)': 0.0,
  u'Type': u' '},
 {u'Brand': u'',
  u'Cost ($)': 33.0,
  u'Count': 572.0,
  u'Discount ($)': 0.0,
  u'Margin (%)': 71.24433,
  u'Outlet': u'',
  u'Product': u'Total',
  u'Revenue ($)': 81.76,
  u'SKU': None,
  u'Sales ($)': 114.76,
  u'Sales incl. tax ($)': 131.95,
  u'Supplier': u'',
  u'Supplier Code': u'',
  u'Tags': u'',
  u'Tax ($)': 17.19,
  u'Type': u''},
 {u'Brand': u'',
  u'Cost ($)': 33.0,
  u'Count': 572.0,
  u'Discount ($)': 0.0,
  u'Margin (%)': 71.24433,
  u'Outlet': u'',
  u'Product': u'Grand Total',
  u'Revenue ($)': 81.76,
  u'SKU': None,
  u'Sales ($)': 114.76,
  u'Sales incl. tax ($)': 131.95,
  u'Supplier': u'',
  u'Supplier Code': u'',
  u'Tags': u'',
  u'Tax ($)': 17.19,
  u'Type': u''}]

json total and grandt total are useless. remove this so it doesn't muck up when I add everything together. Don't want to manualy remove the files. Maybe edit the files with a script to remove the total and grand total.

Checked the json for grand total and total and removes them if they are there.

Search through the json





In [46]:



    


saltax = []
salcoun = []



    











In [47]:



    


for bran in tuejs:
    #print bran['Revenue ($)']
    print bran['Sales incl. tax ($)']
    saltax.append(bran['Sales incl. tax ($)'])
    salcoun.append(bran['Count'])



    


















    






8.0
0.0
0.0
0.0
68.0
0.0
20.0
0.0
24.0
0.0
0.0
12.0
0.0
131.95
131.95

















In [48]:



    


satxtot = sum(saltax)



    











In [49]:



    


satxtot



    


















    
Out[49]:








395.9

















In [50]:



    


for bran in tuejs:
    #print bran['Revenue ($)']
    print bran['Product']



    


















    






Cappuccino
Cappuccino FREE
Chai Latte FREE
Espresso FREE
Flat White
Flat White FREE
Hot Chocolate
Hot Chocolate FREE
Long Black
Long Black FREE
Macchiato FREE
Mocha
Mocha FREE
Total
Grand Total

















In [51]:



    


salcoun



    


















    
Out[51]:








[2, 69, 15, 7, 17, 242, 5, 68, 6, 73, 4, 3, 61, 572.0, 572.0]

















In [ ]:



    






    











In [ ]:

Content source: wcmckee/lcacoffee

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