def ent(df,attribute):
    target_variables = df.play.unique()  #This gives all 'Yes' and 'No'
    variables = df[attribute].unique()    #This gives different features in that attribute (like 'Sweet')


    entropy_attribute = 0
    for variable in variables:
        entropy_each_feature = 0
        for target_variable in target_variables:
            num = len(df[attribute][df[attribute]==variable][df.play ==target_variable]) #numerator
            den = len(df[attribute][df[attribute]==variable])  #denominator
            fraction = num/(den+eps)  #pi
            entropy_each_feature += -fraction*log(fraction+eps) #This calculates entropy for one feature like 'Sweet'
        fraction2 = den/len(df)
        entropy_attribute += -fraction2*entropy_each_feature   #Sums up all the entropy ETaste

    return(abs(entropy_attribute))