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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Firstly import csv and open the csv file.
Step1:  Create an empty dictionary and for each variable whose absolute value of correlation coefficient with the heating space energy consumption is greater than 0.35 we use the dictionary to store the position of this variable and the correlation coefficient.
Step2:  Sort the dictionary according to their values of correlation coefficient.
Step3:  I will print the indices and correlation coefficient of variables we will use in our model besides materials.
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Python Code:
import csv
file = open('public_layout.csv','r')
reader = csv.reader(file, delimiter=',')
fullcsv = list(reader)
Explanation: Firstly import csv and open the csv file.
End of explanation
dic_1=dict()
print(dic_1)
for i in range(801):
    data = np.genfromtxt('recs2009_public.csv',delimiter=',',skip_header=1,usecols=(i,908))
    coef = np.corrcoef(data[:,0],data[:,1])
    if abs(coef[0][1])>=0.35:
        dic_1[i]=coef[0][1]
print(dic_1)
Explanation: Create an empty dictionary and for each variable whose absolute value of correlation coefficient with the heating space energy consumption is greater than 0.35 we use the dictionary to store the position of this variable and the correlation coefficient.
End of explanation
import operator
sortedDic=sorted(dic_1.items(), key=operator.itemgetter(1))
sortedDic
Explanation: Sort the dictionary according to their values of correlation coefficient.
End of explanation
variables_chosen=[6, 315, 430, 705]
print(sortedDic[-2])
print(sortedDic[8])
print(sortedDic[6])
Explanation: I will print the indices and correlation coefficient of variables we will use in our model besides materials.
End of explanation
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