dataByTimeFrame | Python Fiddle

import pandas as pd
sampleData = [
 {
   "id": 0,
   "Hospital": "SomeHospital",
   "VetcoveItemID": 123,
   "Name": "IDEXX VETCONNECT PLUS",
   "Frequency": "Weekly",
   "hide": True
 },
 {
   "id": 1,
   "Hospital": "SomeHospital",
   "VetcoveItemID": 124,
   "Name": "IDEXX VETLAB WIRELESS ROUTER & SWITCH",
   "Frequency": "EOW",
   "hide": False
 },
 {
   "id": 2,
   "Hospital": "SomeHospital",
   "VetcoveItemID": 125,
   "Name": "Lymph Node Aspirate Transport Tube: 2mL 5 Count",
   "Frequency": "Monthly",
   "hide": False
 },
 {
   "id": 3,
   "Hospital": "SomeHospital",
   "VetcoveItemID": 126,
   "Name": "PROCYTEDX ANALYZER SYSTEM WITHOUT IVLS",
   "Frequency": "Quarterly",
   "hide": False
 },
 {
   "id": 4,
   "Hospital": "SomeHospital",
   "VetcoveItemID": 127,
   "Name": "SAFETY PRECAUTIONS FOR CATALYST ONE",
   "Frequency": "Weekly",
   "hide": False
 }
]
df = pd.DataFrame(sampleData)
weekNumbers = {}
for i in range(0,52):
    weekstring = "week"+str(i)
    weekNumbers[weekstring]="week"
idAndWeeks = pd.DataFrame([weekNumbers])
print(idAndWeeks)

def make_weeks(): #this doesnt do anything fun, but it got me to the solution
    weeksAndFrequency = list(weekNumbers.keys())
    weeksAndFrequency.append("Frequency")
    print(weeksAndFrequency)
    df1 = pd.concat([idAndWeeks,df],axis="columns")
    print("df1 head ",df1.head(20))
    print("df1 weekly just weeks: ",df1.loc[df1.Frequency=="Weekly",weekNumbers.keys()].head())
    df1.loc[df1.Frequency=="Weekly",weekNumbers.keys()] = True

print("df1 weekly just weeks AFTER CHANGE: ",df1.loc[df1.Frequency=="Weekly",weekNumbers.keys()].head())
    print("df1 index isin: ",df1.index.isin(df1.index[:2]))
    print("df1 EOW index frequency: ",df1.loc[(df1.Frequency=="EOW") & (df1.index.isin(df1.index[::2])),list(weekNumbers.keys())[::2]].head())
    frequencyRowSelector = df1.Frequency=="EOW"
    everyNthRowSelector = df1.index.isin(df1.index[::2])
    columnsToSetSelector = list(weekNumbers.keys())[::2]

#df1.loc[(df1.Frequency=="EOW") & (df1.index.isin(df1.index[::2])),list(weekNumbers.keys())[::2]]=True
    df1.loc[frequencyRowSelector & everyNthRowSelector,columnsToSetSelector]=True
    print("df1 EOW just weeks AFTER CHANGE: \n",df1.loc[df1.Frequency=="EOW",weeksAndFrequency].head())
    print("test")
    # each item should have "active" weeks based on frequency and even distribution of items per week
    # process each item, in batches of frequency (ie: EOW all together) and just assign numbers in order.
    # EOW is week on/week off with 50% of items on week 1 and 50% on week 2, for example
    # item appears on main table if week is true (there are 52 weeks and 12 (or 13? cause thats easier in python than excel)
    # weeks of cycle so math is hard)

def make_week_data(): #this WORKS!?!?!
    weeksAndFrequency = list(weekNumbers.keys())
    weeksAndFrequency.append("Frequency")
    print(weeksAndFrequency)
    df1 = pd.concat([idAndWeeks,df],axis="columns")
    frequencies = list(daysLookupDict.keys())
    for freq in frequencies:
        rowsToToggle = int(daysLookupDict[freq]["Days of use"]/7)
        frequencyRowSelector = df1.Frequency==freq
        for row in range(0,rowsToToggle):
            skipNum = rowsToToggle
            startNum = row
            everyNthRowSelector = df1.index.isin(df1.index[startNum::skipNum])
            columnsToSetSelector = list(weekNumbers.keys())[startNum::skipNum]
            print("rows to select \n", everyNthRowSelector)
            print("columns to select \n", columnsToSetSelector)
            df1.loc[frequencyRowSelector & everyNthRowSelector,columnsToSetSelector]=True
        print("df1"+freq+"just weeks and frequencyAFTER CHANGE: \n",df1.loc[df1.Frequency==freq,weeksAndFrequency].head())

def display_this_week():
    print("test")
    # Get today's date
    # get week number of today's date
    # get items that are applicable to today's week.
        # get all weekly items
        # get half of bi weekly items
        # get 1/4 of monthly items
        # get 1/13 of quarterly items
    # get week number of arbitrary date (or each week throughout year)
    # get items applicable. Make sure each item is "Got" on correct cadence

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