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How to find all combinations from a list of elements in python.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  8 15:48:20 2020
+https://moonbooks.org/Articles/How-to-find-all-combinations-from-a-list-of-elements-in-python-/
+@author: yaron
+"""
+from sklearn.metrics import mean_squared_error
+import numpy as np
+import scipy.stats
+# Import pandas package  
+import pandas as pd  
+from itertools import combinations
+from sklearn.metrics import r2_score
+
+def index_f(B1,B2):
+  result=(B1-B2)/(B1+B2)
+  return  result 
+
+
+
+
+
+
+L = ['a','b','c','d',"e","f"]
+L2 = ['a','b','c','d',"e","X"]
+L3 = ['a','b','c','d',"C","X"]
+
+
+for i in combinations(L2,2):
+    print(i)
+    index  =i[0]+i[1] 
+
+c2 = [i for i in combinations(L,2)]
+
+print(c2)
+
+#נמצא את כל הצירופים ברשימה הראשונה ויצור רשימה ארוכה שכוללת
+#את כל הצריפים אפשרים
+#ואז נקח כל צירוף ונעשה ממנו סדרת זמן תשווה למשתנה המסביר
+
+def calculateSquare(n):
+    return n*n
+
+B1 = [9,8]
+B2 = [9,9]
+
+numbers = (1, 2, 3, 4)
+result = map(index_f, B1,B2)
+numbersSquare = set(result)
+print(numbersSquare)
+
+# making data frame  
+data = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv")  
+data = pd.read_csv('C:/Users/yaron/Desktop/TEST_BAND.csv')  
+Y = data["Y"].tolist()    
+# list(data) or 
+L=list(data.columns)     
+del L[0]
+combinations_vale = [i for i in combinations(L,2)]
+# calling head() method   
+# storing in new variable  
+data_top = data.head()  
+R2 =[]
+MSE =[]
+B1 =[]
+B2 =[]
+
+
+number = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]#index of nubmer of combisne
+for band in number:
+    print (band)
+    a=combinations_vale[band][0]
+    print(a)
+    b=combinations_vale[band][1]  
+    x = data[[a,b]]#here we have a list of of two band that need to turn indo index   
+    index_list_vale=list(map(index_f, x[a].tolist(), x[b].tolist())) 
+   # RR = r2_score([1,1,1,1,1], index_list_vale)
+    #make the list that wil store the new datafreame
+
+    R2.append(r2_score(Y, index_list_vale))
+    MSE.append(mean_squared_error(Y, index_list_vale))
+
+
+    B1.append(a)
+    B2.append(b)
+
+    #MSE =
+
+
+
+
+
+    #GET the data to new datafrme
+
+df = pd.DataFrame(list(zip(R2,MSE,B1, B2)), 
+               columns =['R2',"MSE",'B1','B2']) 
+
+
+df.to_csv('C:/INCA MEAN/test.csv')    
+
+
+mean_squared_error(y_true, y_pred)
+
+# display  
+data_top  
+ages = titanic["Age"]
+
+
+#עוובר על כל שורה ולוקח את כל ערכים והפוך לרשימה ,מוציא את ערך המסביר משם
+#כל ערך יצטרך לשמור גם על מספר ערוץ שלו