--- a
+++ b/dataWrapping.py
@@ -0,0 +1,164 @@
+import pandas
+# from pattern.en import sentiment
+# import HTMLParser
+import re
+import pandas as pd
+from collections import Counter
+from nltk.corpus import stopwords
+import string
+from collections import OrderedDict
+from nltk import bigrams
+from nltk.tokenize import word_tokenize
+import matplotlib.pyplot as plt
+import numpy as np
+# import plotly.plotly as py
+
+# import pandas as pd
+# import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import recall_score, precision_score, accuracy_score
+import math
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.metrics import confusion_matrix
+from sklearn.feature_selection import RFE
+import requests
+from bs4 import BeautifulSoup
+# import numpy as np
+# import matplotlib.pyplot as plt
+# from matplotlib import style
+# style.use("ggplot")
+import os
+
+data_outcome = pd.read_csv("C:\Shashank Reddy\Outcome.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python')
+
+
+
+#print(data_outcome)
+
+data_outcome = data_outcome.fillna("zero")
+
+
+
+
+
+# outcome dictionary
+outcome = {'removed': 1, 'not removed': 2, 'retrieval': 3, 'non retrieval': 4, 'zero' : 0}
+data_outcome["Outcome"] = [outcome[item] for item in data_outcome["Outcome"]]
+
+list_outcome = pd.DataFrame(list(data_outcome["Outcome"]))
+
+list_outcome.to_csv(r"C:\Shashank Reddy\FinalOutcome.csv",sep='\t', index=False)
+
+#print(data_outcome)
+
+
+
+
+#data_outcome.to_dense().to_csv(r"C:\Shashank Reddy\FinalOutcome.csv")
+
+
+sessilelocation = pd.read_csv("C:\Shashank Reddy\SessileLocation.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(sessilelocation.columns.tolist())
+#print(sessilelocation)
+
+location = {'cecal': 1, 'ascending': 2, 'ileum': 3, 'ileocecal': 3, 'hepatic': 4, 'transverse': 5, 'splenic': 6, 'descending': 7, 'sigmoid': 8, 'recto-sigmoid': 9, 'rectal': 10, 'appendix': 11,'zero': 0}
+sessilelocation["PositionA"] = [location[item] for item in sessilelocation["PositionA"]]
+sessilelocation["PositionB"] = [location[item] for item in sessilelocation["PositionB"]]
+sessilelocation["PositionC"] = [location[item] for item in sessilelocation["PositionC"]]
+sessilelocation["PositionD"] = [location[item] for item in sessilelocation["PositionD"]]
+sessilelocation["PositionE"] = [location[item] for item in sessilelocation["PositionE"]]
+sessilelocation["PositionF"] = [location[item] for item in sessilelocation["PositionF"]]
+sessilelocation["PositionG"] = [location[item] for item in sessilelocation["PositionG"]]
+
+#print(sessilelocation)
+
+
+sessileshape = pd.read_csv("C:\Shashank Reddy\SessileShape.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(sessileshape)
+shape = {'zero':0,'sessile':1,'pedunculated':2,'flat':3,'mass':4,'smooth':5,'serrated':6}
+
+sessileshape["Shape"] = [shape[item] for item in sessileshape["Shape"]]
+#print(sessileshape)
+
+
+sessilesize = pd.read_csv("C:\Shashank Reddy\SessileSize.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(sessilesize)
+
+size = {'zero':0,'diminutive':1,'small':2,'medium':3,'large':4}
+sessilesize["Size"] = [size[item] for item in sessilesize["Size"]]
+#print(sessilesize)
+
+
+sessileside = pd.read_csv("C:\Shashank Reddy\Sides.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(sessileside)
+
+side = {'zero':0,'left':1,'right':2}
+sessileside["Sides"] = [side[item] for item in sessileside["Sides"]]
+#print(sessileside)
+
+
+cancer_treatment = pd.read_csv("C:\Shashank Reddy\Treatment.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(cancer_treatment)
+
+treatment = {'zero':0,'piermeal':1,'cold snare':2,'hot snare':3,'snare':4,'electocautery snare':5,'excisional biopsy':6,'biopsy forcep':7,'cold biopsy':8}
+cancer_treatment["Treatment"] = [treatment[item] for item in cancer_treatment["Treatment"]]
+
+list_treatment = pd.DataFrame(list(cancer_treatment["Treatment"]))
+
+list_treatment.to_csv(r"C:\Shashank Reddy\FinalTreatment.csv",sep='\t', index=False)
+#print(cancer_treatment)
+
+
+sessile_number = pd.read_csv("C:\Shashank Reddy\SessileNumber.csv",sep='\s*,\s*',header=0, encoding='ascii', engine='python').fillna("zero")
+#print(sessile_number)
+
+number = {'zero':0,'one':1,'two':2,'three':3,'four':4,'five':5,'six':7,'eight':8,'nine':9,'ten':10}
+
+sessile_number["Number1"] = [number[item] for item in sessile_number["Number1"]]
+sessile_number["Number2"] = [number[item] for item in sessile_number["Number2"]]
+sessile_number["Number3"] = [number[item] for item in sessile_number["Number3"]]
+sessile_number["Number4"] = [number[item] for item in sessile_number["Number4"]]
+
+#print(sessile_number)
+
+#************************************* Data Union *********************************************************************************
+
+list_mat1= pd.DataFrame(list(sessilelocation["PositionA"]))
+list_mat2= pd.DataFrame(list(sessilelocation["PositionB"]))
+list_mat3= pd.DataFrame(list(sessilelocation["PositionC"]))
+list_mat4= pd.DataFrame(list(sessilelocation["PositionD"]))
+list_mat5= pd.DataFrame(list(sessilelocation["PositionE"]))
+list_mat6= pd.DataFrame(list(sessilelocation["PositionF"]))
+list_mat7= pd.DataFrame(list(sessilelocation["PositionG"]))
+
+list_mat8= pd.DataFrame(list(sessileshape["Shape"]))
+list_mat9= pd.DataFrame(list(sessilesize["Size"]))
+list_mat10= pd.DataFrame(list(sessileside["Sides"]))
+
+list_mat11= pd.DataFrame(list(sessile_number["Number1"]))
+list_mat12= pd.DataFrame(list(sessile_number["Number2"]))
+list_mat13= pd.DataFrame(list(sessile_number["Number3"]))
+list_mat14= pd.DataFrame(list(sessile_number["Number4"]))
+
+
+Final_Data = pd.concat([list_mat1,list_mat2,list_mat3,list_mat4,list_mat5,list_mat6,list_mat7,list_mat8,list_mat9,list_mat10,list_mat11
+                        ,list_mat12,list_mat13,list_mat14],axis = 1)
+
+
+print(Final_Data)
+
+
+Final_Data.to_csv(r"C:\Shashank Reddy\DataSet_Final.csv",index = False)
+
+
+#print(Final_Data)
+
+
+
+
+
+
+