diff --git a/data formation.py b/data formation.py
new file mode 100644
index 0000000..a709baf
--- /dev/null
+++ b/data formation.py	
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+from urllib.request import Request, urlopen 
+import urllib,json                            
+import pandas as pd 
+from bs4 import BeautifulSoup
+import requests
+
+ContestID="JUNE19A"
+#def scrapeContestRanks(ContestID):
+##    ContestID = "JUNE19A"
+#    
+#    # get the max page_number 
+#    path1 = 'https://www.codechef.com/api/rankings/'+ContestID+'&itemsPerPage=100'
+#    req1 = Request(path1 , headers = {'User-Agent':'Mozilla/5.0'})
+#    webpage1 = urlopen(req1)
+#    obj1 = json.load(webpage1)
+#    last_pagen = obj1['availablePages']
+#    
+#    # Declaring necessary variables
+#    pages = [str(i) for i in range(1,last_pagen+1)]
+#    
+#    
+#    # declaring the lists to store data in
+#    rank = []
+#    userHandle = []
+#    name = []
+#    
+#    
+#    for page in pages:
+#        # make a request
+#        path = 'https://www.codechef.com/api/rankings/'+ContestID+'?sortBy=score&order=desc&page='+page+'&itemsPerPage=100'
+#        req = Request(path , headers = {'User-Agent':'Mozilla/5.0'})
+#        webpage = urlopen(req)
+#    
+#        # Parse json data
+#        obj = json.load(webpage)
+#        i = obj['list']
+#    
+#        # Extracting data
+#        for j in range(len(i)):
+#            rank.append(i[j]['rank'])
+#            userHandle.append(i[j]['user_handle'])
+#            name.append(i[j]['name'])
+#    
+#    # creating a Dataframe
+#    df = pd.DataFrame({'user_handle':userHandle,'name':name},index = rank).sort_index()
+#    
+#    return df
+
+
+
+    
+
+#function parameter being contest ID
+#df = scrapeContestRanks("JUNE19A")
+#df.to_csv('ContestRanking.csv')
+
+ContestData = pd.read_csv("file:///E:/CC rank predictor/ContestRanking.csv") #data of particular contest
+
+dataOverall = pd.read_csv("file:///E:/CC rank predictor/datainCSV.csv") #data of whole ranklist on codechef
+participants = ContestData["user_handle"]
+#participants=participants.head()
+
+#empty dataframe
+work = pd.DataFrame(columns = ["Current_Rating","Rank_in_contest","Mean_below_ranked","Rating_change"])
+
+#function to get rating graph
+def get_rating_graphs(username):
+    page=requests.get("https://www.codechef.com/users/"+username,headers={'User-Agent': 'Mozilla/5.0'})
+    soup = BeautifulSoup(page.content, 'html.parser')
+    x=str(soup.select('script[type="text/javascript"]')[29]).split(';')[9][19:].strip()
+    Code=[]
+    Year=[]
+    Month=[]
+    Day=[]
+    Reason=[]
+    Penalised_In=[]
+    Rating=[]
+    Rank=[]
+    Name=[]
+    End_Date=[]
+    y=x[1:-2].split('},')
+    null='None'
+    for i in range(len(y)):
+        y[i]=y[i]+'}'
+    for i in range(len(y)):
+        Code.append(eval(y[i])['code'])
+        Year.append(eval(y[i])['getyear'])
+        Month.append(eval(y[i])['getmonth'])
+        Day.append(eval(y[i])['getday'])
+        Reason.append(eval(y[i])['reason'])
+        Penalised_In.append(eval(y[i])['penalised_in'])
+        Rating.append(eval(y[i])['rating'])
+        Rank.append(eval(y[i])['rank'])
+        Name.append(eval(y[i])['name'])
+        End_Date.append(eval(y[i])['end_date'])
+    
+    dict = {'Code':Code , 'Year': Year, 'Month': Month,'Day':Day,'Reason':Reason,'Penalised_In':Penalised_In,'Rating':Rating,'Rank':Rank,'Name':Name,'End_Date':End_Date}  
+    
+    df = pd.DataFrame(dict) 
+    return df
+
+#Function to get present rating(parameter being userhandle)
+def Present_rating(username):
+    row = dataOverall[dataOverall["username"]==username]
+    rat = row["rating"]
+    rat = list(rat)[0]
+    return rat
+
+l1=[]
+l2=[]
+l3=[]
+l4=[]
+
+
+for i in range(len(participants)):
+    username = participants[i]
+#    dataOfthat = dataOverall[dataOverall["username"]==username]
+#    Total_rat_data = dataOverall["Rating"]
+    rating = Present_rating(username)
+    tempSum = 0
+    try:
+        for j in range(i+1,len(participants)):
+            tempSum = tempSum + Present_rating(username)
+    except:
+        pass
+    l1.append(rating)
+    l2.append(i+1)
+    try:
+        nos = len(participants)-i-1
+        l3.append(tempSum/nos)
+    except:
+        l3.append(0)
+    rating_history = get_rating_graphs(username)
+    rating_after = rating_history[rating_history["Code"]==ContestID]["Rating"]
+    index1 = (rating_history.loc[rating_history["Code"]==ContestID].index)[0]
+    rating_after = list(rating_after)
+    rating_after = int(rating_after[0])
+    try:
+        rating_before = rating_history["Rating"] 
+        rating_before = int(rating_before[index1-1])
+    except:
+        rating_before=rating_after
+    change = rating_after - rating_before
+    l4.append(change)
+    
+#  Feeding the empty dataframe    
+work.Current_Rating = l1
+work.Rank_in_contest = l2
+work.Mean_below_ranked = l3
+work.Rating_change = l4
+print(work)
+    
+    
+    
+