Python pandas: Why does df.iloc[:, :-1].values for my training data select till only the second last column?

Question

Very simply put,

For the same training data frame df, when I use X = df.iloc[:, :-1].values, it will select till the second last column of the data frame instead of the last column (which is what I want BUT it's a strange behavior I've never seen before), and I know this as the second last column's value and the last column's value for that row is different.

However, using y = df.iloc[:, -1].values gives me the row vector of the last column's values which is exactly what I want.

Why is the negative 1 for X giving me the second last column's value instead?

Answer 1

I think you have only two columns in df, because if there is more columns, iloc select all columns without last:

df = pd.DataFrame({'A':[1,2,3],
                   'B':[4,5,6],
                   'C':[7,8,9],
                   'D':[1,3,5],
                   'E':[5,3,6],
                   'F':[7,4,3]})

print (df)
   A  B  C  D  E  F
0  1  4  7  1  5  7
1  2  5  8  3  3  4
2  3  6  9  5  6  3

print(df.iloc[:, :-1])
   A  B  C  D  E
0  1  4  7  1  5
1  2  5  8  3  3
2  3  6  9  5  6

X = df.iloc[:, :-1].values
print (X)
[[1 4 7 1 5]
 [2 5 8 3 3]
 [3 6 9 5 6]]

print (X.shape)
(3, 5)