Remove comments

app.py

@@ -6,7 +6,7 @@
 import json
 import pandas as pd
 import pickle
-from put_labels_2 import ratios
+from put_labels import ratios
 
 #########################################################################
 """

firebase/access_util.py

@@ -230,7 +230,6 @@ def setup_ratios(cur, prev):
       rato= pd.DataFrame(ratios.items())
       rato, rato.columns= rato.T, ratios.keys()
       rato.drop(index=rato.index[0],axis=0, inplace=True)
-      #print(rato)
       rato['wce_label']=rato['WorkingCapitalRatio'].apply(rfex.wce)
       rato['eps_label']=rato['EarningPerShare'].apply(rfex.eps)
       rato['de_label']=rato['DebtToEquityRatio'].apply(rfex.de)

firebase/getLabelsFromScrape.py

@@ -4,22 +4,19 @@
 from firebase_admin import credentials, firestore
 from scrape import get_data
 
+# this file gets filing by data scraping and generate data file for ml
+
 import access_util as au
 epsilon = 1e-20
 
 cred = credentials.Certificate('../serviceAccount.json')
 firebase_admin.initialize_app(cred)
 db = firestore.client()
 
-# all collections
-# for doc in db.collections():
-#     print(u'{}'.format(doc.id))
-
 myCollection = db.collection("company")
 company_list = []
 for doc in myCollection.stream():
     company_list.append(doc.id)
-    # print(u'{} => {}'.format(doc.id, doc.to_dict()))
 
 # cik list from csv    
 comps = pd.read_csv('..\csv\GoodCom.csv')
@@ -36,16 +33,12 @@
     if count_to_display == 0: break
     for date in ("2021", "2020"):
         print(f"year: {date}, cik = {cik}")
-        # x = myCollection.document(cik).collection("10k").document(date).get()
         try:
             x = get_data(cik, "10-K", int(date))
             cur = x[(date)]
             keys = x.keys()
-            # print(cur)
-            # print(f"{cik} + {date} has : {cur['MarketableSecurities']}")
             prevDate = int(date) - 1
             try:
-                # y = myCollection.document(cik).collection("10k").document(prevDate).get()
                 y = get_data(cik, "10-K", int(prevDate))
                 prev = y[(prevDate)]
             except:
@@ -56,7 +49,6 @@
         # use cur and prev to get ratios and label company
         rf = au.ratios
 
-        # print(cur, prev)
         ratios, rato = rf.setup_ratios(cur, prev)
 
         ratiodf= pd.DataFrame(cur.items())
@@ -68,84 +60,13 @@
         rato.set_index('rider_provider')
         ratiodf.insert(0,'rider_provider', rider_provider)
         ratiodf.set_index('rider_provider')
-        # print(rato.iloc[:,-13:])
-        # print(rato)
         netcsv.append(rato)
         netvalues.append(ratiodf)
 
     count_to_display -= 1
 
-# print(netcsv)
 result = pd.concat(netcsv)
-# print(result)
 result.to_csv("labels.csv", index = True) 
 
 result = pd.concat(netvalues)
-# print(result)
 result.to_csv("data_values.csv", index = True) 
-
-
-
-
-
-
-'''
-parameters in firebase
-dict_keys(['MRR', 
-'TotalOperatingExpenses', 
-'SalesCost', 
-'MarketableSecurities', 
-'Non-GAAPEarnings', 
-'GAAPRevenue', 
-'CustomerChurn', 
-'CostOfSales', 
-'SharesOutstanding', 
-'Revenues', 
-'NetOperatingExpenses', 
-'StockPrice', 
-'TotalPropertyAndEquipment', 
-'RecurringRevenue', 
-'ARR', 
-'Inventories', 
-'CustomerAcquisitionCosts', 
-'SubscriberChurn', 
-'OperatingIncome', 
-'EBITDA', 
-'SubscriptionRevenue', 
-'RevenueChurn', 
-'GrossProfit'])
-'''
-
-'''
-Needed
-"2020_3": {
-        "CashAndCashEquivalents": 730506000.0,
-        "MarketableSecurities": 1141425000.0,
-        "TotalCurrentAssets": 2624276000.0,
-        "TotalAssets": 3050311000.0,
-        "PropertyAndEquipmentNet": 108077000.0,
-        "Goodwill": 24340000.0,
-        "TotalCurrentLiabilities": 1413948000.0,
-        "SharesOutstanding": 198179809.0,
-        "TotalStockholdersEquity": 1499918000.0,
-        "TotalEquity": 3050311000.0,
-        "GrossProfit": 1214178000.0,
-        "TotalOperatingExpenses": 810447000.0,
-        "NetIncome": 411706000.0,
-        "GrossPropertyAndEquipment": 151727000.0,
-        "StockPrice": "NaN",
-        "SalesAndMarketing": 470886000.0,
-        "TotalDebt": "NaN",
-        "Revenues": 1768883000.0,
-        "CostOfSales": 554705000.0,
-        "OperatingIncome": 403731000.0,
-        "NetLoss": "NaN",
-        "RecurringRevenue": "NaN",
-        "ARR": "NaN",
-        "GAAPRevenue": "NaN",
-        "NonGAAPEarnings": "NaN",
-        "MRR": "NaN",
-        "date": "2020-12-04",
-        "doc_url": "https://www.sec.gov/Archives/edgar/data/1585521/000158552120000299/zm-20201031.htm"
-    },
-'''

ml/putLabels.py

@@ -262,7 +262,6 @@ def chun_rate(r):
       rato= pd.DataFrame(ratios.items())
       rato, rato.columns= rato.T, ratios.keys()
       rato.drop(index=rato.index[0],axis=0, inplace=True)
-      #print(rato)
       rato['wce_label']=rato['WorkingCapitalRatio'].apply(wce)
       rato['eps_label']=rato['EarningPerShare'].apply(eps)
       rato['de_label']=rato['DebtToEquityRatio'].apply(de)