We Keep Coding

Add list as list element Python weird behaviour

I am trying to store the file indexes from a directory in a list variable using Python. This list will be used for a recursive function that will change the content of files, but the list is storing incorrect values.
My directory has more than 5k files with the format: ccc-ppp.htm where ccc is the chapter number (from 0 to 24) and ppp is page number, that varies from chapter to chapter. For example:
0-1.htm
0-2.htm
0-3.htm
1-1.htm
1-2.htm
...
1-10.htm
2-1.htm
...
2.158.htm
...
24-100.htm
Some chapters have more than 500 pages.
My intention is to store in a list of lists:
Dir = [[1, 2, 3], [1, 2, 3, ..., 10], [1, 2, ..., 158], ..., [0, 1, 2, ..., 100]]
(Note that some chapters start with ...-0.htm
My code:
import os.path
Folder = "C:\MyFoler\\"
Ch = 0
Pg = 0
Chapter = []
Dir = []
while Ch <= 24:
File = Folder + str(Ch) + "-" + str(Pg) + ".htm"
if os.path.exists(File):
Chapter.append(Pg)
else:
print(f"\nChapter {Ch}: {Chapter}") # OBS(1)
Dir.append(Chapter)
print(f"\nDir: {Dir}") # OBS(2)
Chapter.clear()
Ch += 1
Pg = -1
print(f"\nDir: {Dir}") # OBS(3)
Pg += 1
OBS(1): It prints the list of pages in a chapter (working OK). For example to Chapter 24:
Chapter 24: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]
OBS(2): It prints a list, with Ch elements, and each element is the Chapter, but it repeats the chapter in all list elements. For example to Ch = 24:
Dir: [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100],
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]]
The problem is that it is storing Chapter in all Dir elements, which is unwanted. The wanted result is:
Dir[0] = [1, 2, 3]
Dir[1] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
...
Dir[24] =[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100]
OBS(3): I don't know why the result of this line is:
Dir: [[], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], [], []]
It seems that when I clear the Chapter with Chapter.clear(), it clear the contents of Dir. Why?
Then, can someone help me to fix the code, please? Thanks in advance for your attention.

You are not properly appending the chapters to the list Dir.
I would recommend to do this, first create a dictionary where the number of the chapter is the key and then set he value of each key to be and empty list. Then use a loop to parse the dictionary and append the the page correctly to the corresponding list.
dir = {}
for chapter in range(0,25):
dir[chapter]=[]

You are continuing to append the same list (Chapter) to Dir each time through. Consider the below very simple example, and what happens when we go from using the same list, vs. creating a new one on each loop iteration.
>>> a = []
>>> b = []
>>> for i in range(3):
... for j in range(i):
... a.append(j)
... b.append(a)
...
>>> b
[[0, 0, 1], [0, 0, 1], [0, 0, 1]]
>>>
>>> b = []
>>> for i in range(3):
... a = []
... for j in range(i):
... a.append(j)
... b.append(a)
...
>>> b
[[], [0], [0, 1]]
>>>

Select values from two different dataset in python

i have a trouble when i'm dealing with my 2 dataset, i explain my problem:
I have 2 different dataset:
training_df = pd.read_csv('.../train.csv')
test_df = pd.read_csv('.../test.csv')
I have to take values from some columns from train.csv and take other columns in test.csv, i tried like this:
num_attrib = pd.DataFrame(training_df, columns=[0, 2, 3, 15, 16, 17, 18, 24, 32, 34, 35, 36, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 54, 57, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72])
cat_attrib = pd.DataFrame(training_df, columns=[1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 37, 38, 39, 40, 51, 53, 55, 56, 58, 61, 62, 63, 73, 74])
num_attrib_test = pd.DataFrame(test_df, columns=[0, 2, 3, 15, 16, 17, 18, 24, 32, 34, 35, 36, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 54, 57, 59, 60, 64, 65, 66, 67, 68, 69, 70, 71, 72])
cat_attrib_test = pd.DataFrame(test_df, columns=[1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 37, 38, 39, 40, 51, 53, 55, 56, 58, 61, 62, 63, 73, 74])
Both datasets have numerical and categorial datas. I have to select and separate categorical from numerical datas for each datasets, but my way is wrong.
I have this trouble because i have to make the Columntransformer() on training_df and test_df.
Any suggestion?
Thank you so much

You are looking for iloc. See documentation here.
num_attrib = training_df.iloc[:,[0,2,3,...,15]]
You can also slice:
#even columns
num_attrib = training_df.iloc[:, ::2]
#odd columns
num_attrib = training_df.iloc[:, 1::2]

Getting a syntax error when displaying a matrix in python

here is the code:
#STARTING MESSAGE
print('Any *num* is a numerator, and any *den* is a denominator. *num1* is for the first fraction, and *num2* is for the second fraction. Same thing with the denominators. Please enter num1, saying *num1 = x* with the num1 =. THIS ONLY WORKS WITH NUMBERS 1 THROUGH 100!')
#DEN1
num1 = 3
if num1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 02, 93, 94, 95, 96, 97, 98, 99, 100]:
print(now enter den1)
den1 = 34
if den1 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 02, 93, 94, 95, 96, 97, 98, 99, 100]:
print(now enter num2)
#SEC == 2
num2 = 4
if num2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 02, 93, 94, 95, 96, 97, 98, 99, 100]:
print(now enter den2)
den2 = 33
if den2 = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 02, 93, 94, 95, 96, 97, 98, 99, 100]:
print(CALCULATING...)
#CALCULATING CODE
den1*num2 = secondFrac
den2*num1 = firstFrac
if firstFrac > secondFrac:
print('The first fraction is greater then the second fraction! *First_Fraction > Second_Fraction!*')
if firstFrac < secondFrac:
print('The second fraction is greater then the first fraction! *First_Fraction < Second_Fraction!*')
I am getting a syntax error with the matrixes, just saying "Syntax Error: Invalid Syntax"

Why does my non-normal sample pass the normality tests? [duplicate]

This question already has answers here:
What does "e" in "1e-5" in Python language mean and what is the name of this notation? [duplicate]
(3 answers)
What is the meaning of number 1e5?
(5 answers)
Closed 4 years ago.
I am running non-normal samples against normality tests, expecting it to fail. However, the p-value {normaltest: 2.64, shapiro: 6.23} is much higher than 0.05 so I cannnot reject the null hypotheses that this was drawn from a normal distribution. Could someone please enlighten me?
import random
from scipy.stats import shapiro
from scipy import stats
x = list()
for y in range(1000):
x.append(random.randrange(1,100))
print(stats.normaltest(x))
print(shapiro(x))
print(x)
NormaltestResult(statistic=500.02063130202464, pvalue=2.641797311523516e-109)
(0.9599024057388306, 6.233162463518298e-16)
[25, 94, 79, 12, 67, 27, 89, 94, 89, 34, 99, 58, 53, 29, 81, 94, 4, 52, 14, 19, 60, 26, 6, 85, 71, 57, 23, 13, 58, 75, 75, 10, 51, 12, 80, 3, 82, 64, 74, 57, 83, 15, 23, 45, 52, 51, 36, 61, 78, 50, 26, 72, 40, 19, 59, 90, 23, 71, 52, 25, 3, 16, 20, 62, 50, 56, 60, 73, 28, 96, 69, 80, 36, 11, 11, 7, 18, 1, 73, 17, 29, 57, 72, 87, 43, 18, 22, 54, 32, 35, 79, 27, 45, 81, 80, 79, 82, 49, 77, 73, 21, 17, 90, 96, 2, 72, 7, 43, 37, 72, 64, 53, 63, 5, 36, 74, 36, 59, 53, 55, 54, 15, 83, 65, 2, 64, 46, 51, 31, 1, 77, 28, 47, 75, 46, 56, 3, 16, 24, 27, 31, 66, 4, 61, 46, 2, 56, 59, 98, 86, 83, 86, 97, 59, 45, 80, 55, 23, 21, 61, 6, 20, 13, 54, 20, 58, 86, 38, 18, 47, 68, 52, 74, 19, 34, 56, 17, 91, 15, 54, 82, 95, 23, 54, 42, 81, 82, 6, 70, 1, 78, 49, 12, 25, 33, 38, 47, 41, 68, 75, 73, 76, 46, 7, 90, 89, 63, 43, 41, 46, 88, 14, 97, 37, 92, 76, 60, 7, 5, 56, 77, 98, 61, 60, 59, 64, 4, 76, 34, 84, 78, 39, 66, 24, 49, 60, 57, 13, 57, 18, 37, 52, 26, 36, 97, 47, 95, 26, 82, 82, 10, 76, 54, 67, 98, 22, 56, 20, 34, 76, 28, 50, 70, 87, 83, 13, 76, 87, 98, 29, 99, 29, 23, 74, 5, 54, 73, 31, 89, 10, 24, 15, 9, 34, 85, 23, 6, 25, 64, 94, 37, 30, 11, 9, 58, 43, 2, 1, 73, 49, 48, 41, 99, 30, 91, 17, 31, 58, 70, 46, 20, 33, 94, 35, 41, 19, 22, 2, 37, 8, 54, 41, 21, 16, 20, 65, 27, 68, 24, 19, 36, 63, 80, 12, 82, 74, 74, 46, 7, 36, 42, 72, 16, 26, 96, 48, 75, 86, 62, 20, 79, 66, 71, 43, 43, 96, 67, 97, 76, 40, 80, 97, 75, 2, 3, 97, 37, 78, 77, 25, 84, 82, 25, 87, 44, 80, 92, 95, 99, 48, 67, 12, 82, 3, 15, 40, 45, 94, 32, 87, 92, 24, 42, 73, 66, 20, 62, 9, 75, 51, 31, 3, 13, 76, 21, 32, 14, 58, 28, 14, 99, 14, 50, 45, 13, 83, 45, 59, 63, 39, 65, 78, 46, 96, 27, 16, 69, 42, 65, 68, 68, 90, 39, 50, 86, 7, 75, 93, 84, 23, 53, 31, 23, 63, 32, 60, 85, 67, 42, 5, 72, 44, 43, 98, 75, 55, 28, 99, 71, 72, 66, 46, 61, 52, 50, 16, 44, 63, 64, 32, 59, 73, 33, 36, 32, 45, 75, 44, 36, 74, 97, 2, 38, 30, 74, 12, 57, 11, 37, 83, 64, 3, 63, 3, 35, 61, 55, 59, 99, 51, 58, 63, 70, 84, 4, 18, 13, 51, 27, 75, 43, 63, 35, 76, 67, 32, 15, 54, 51, 31, 77, 97, 83, 50, 76, 87, 26, 55, 93, 31, 70, 5, 11, 54, 48, 55, 48, 76, 90, 75, 43, 34, 6, 22, 35, 29, 4, 47, 83, 44, 7, 2, 97, 74, 90, 91, 17, 12, 33, 52, 28, 95, 57, 22, 53, 83, 56, 71, 28, 76, 55, 12, 89, 27, 20, 20, 93, 43, 65, 34, 83, 92, 11, 22, 38, 90, 83, 77, 11, 5, 22, 73, 29, 67, 49, 16, 47, 60, 26, 20, 76, 57, 46, 70, 35, 9, 28, 33, 8, 33, 21, 65, 3, 67, 52, 45, 82, 32, 94, 89, 15, 27, 63, 53, 96, 4, 74, 29, 59, 67, 22, 24, 45, 63, 76, 66, 51, 28, 42, 83, 37, 56, 83, 14, 35, 99, 48, 93, 83, 76, 2, 20, 99, 41, 43, 61, 3, 13, 7, 74, 60, 17, 84, 16, 44, 76, 63, 85, 44, 27, 38, 29, 61, 34, 55, 91, 13, 31, 42, 35, 38, 6, 46, 31, 99, 85, 23, 67, 11, 4, 52, 57, 11, 9, 21, 64, 17, 46, 78, 83, 45, 68, 98, 88, 47, 1, 94, 24, 79, 47, 33, 7, 81, 12, 26, 99, 80, 78, 53, 88, 1, 49, 17, 91, 27, 44, 31, 20, 6, 46, 59, 40, 57, 80, 3, 72, 83, 81, 2, 27, 36, 94, 31, 30, 22, 26, 31, 14, 93, 11, 32, 14, 75, 17, 49, 54, 42, 56, 76, 42, 51, 69, 22, 86, 46, 97, 70, 24, 81, 3, 75, 81, 63, 48, 51, 72, 19, 16, 16, 1, 61, 95, 53, 36, 82, 93, 53, 65, 9, 40, 91, 41, 85, 65, 38, 59, 4, 92, 50, 51, 7, 87, 80, 45, 84, 57, 21, 44, 3, 52, 7, 53, 97, 46, 65, 37, 76, 7, 81, 49, 21, 25, 18, 84, 53, 84, 89, 5, 95, 69, 70, 56, 31, 69, 12, 72, 36, 12, 44, 94, 39, 97, 91, 92, 15, 17, 57, 17, 49, 47, 1, 8, 2, 93, 91, 29, 41, 12, 46, 75, 98, 14, 34, 6, 26, 6, 81, 75, 49, 61, 70, 83, 26, 83, 38, 40, 81, 27, 14, 40, 54, 35, 10, 22, 30, 38, 2, 95, 31, 32, 25, 88, 70, 33, 85, 52, 7, 47, 4, 87, 70, 90, 15, 53, 74, 45, 76, 50, 23, 54, 33, 90, 53, 70, 4, 6, 47, 77, 87, 60, 92, 94, 79, 36, 26, 20, 94, 59, 50, 72, 31, 25, 60, 24, 82, 93, 13, 57, 21, 72, 78, 27, 62, 67, 67, 62, 56, 40, 40, 49, 31, 52, 16, 67, 51, 87, 77, 1, 47, 49, 64, 48, 62, 90, 29, 68, 2, 39, 92, 89, 92, 34, 78, 48, 36, 32, 43, 26, 81, 22, 76, 95, 69, 79, 13, 77, 26, 70, 32, 21, 92, 97, 90, 98, 16, 81, 47, 83, 93, 50]

Calculate three vectors Pearson Correlation

I want to calculate the Pearson Correlation of three vectors. Like:
np.corrcoef(X,Y,Z)
Anyone knows how to do that in Python? Any library available?
Big thanks!

Create a 2-D array:
import numpy as np
X = np.random.randint(0,100,100)
Y = np.random.randint(0,100,100)
Z = np.random.randint(0,100,100)
spam = np.array([X,Y,Z])
Then run np.corrcoeff on that:
eggs = np.corrcoef(spam)
Which gives
spam
array([[12, 1, 23, 95, 58, 37, 38, 76, 99, 24, 50, 47, 29, 67, 38, 62,
28, 39, 26, 32, 55, 11, 67, 71, 71, 50, 9, 54, 59, 22, 70, 63,
11, 43, 30, 54, 14, 11, 89, 68, 98, 27, 18, 66, 14, 23, 83, 81,
24, 90, 56, 40, 3, 94, 86, 54, 66, 68, 96, 74, 46, 19, 58, 74,
7, 73, 93, 91, 10, 75, 32, 91, 45, 57, 81, 96, 20, 25, 18, 86,
59, 58, 94, 97, 10, 11, 6, 30, 28, 76, 56, 86, 64, 38, 26, 25,
26, 77, 76, 0],
[76, 54, 66, 16, 97, 22, 66, 59, 90, 67, 85, 58, 49, 13, 36, 44,
91, 20, 63, 2, 1, 51, 66, 12, 32, 10, 48, 88, 6, 96, 68, 60,
22, 24, 51, 2, 94, 95, 98, 79, 24, 22, 94, 69, 36, 37, 50, 94,
8, 2, 15, 80, 99, 77, 15, 95, 76, 25, 0, 69, 46, 21, 47, 87,
88, 6, 34, 63, 46, 12, 62, 4, 36, 89, 21, 44, 96, 22, 11, 14,
63, 55, 70, 58, 33, 93, 88, 39, 0, 18, 9, 92, 37, 13, 12, 67,
98, 34, 9, 6],
[56, 21, 61, 55, 82, 60, 13, 36, 17, 52, 93, 88, 65, 29, 67, 81,
65, 59, 33, 89, 58, 99, 78, 1, 75, 39, 10, 75, 94, 57, 42, 32,
19, 7, 77, 82, 96, 61, 94, 50, 61, 13, 21, 51, 70, 14, 60, 79,
32, 29, 22, 93, 31, 19, 74, 37, 85, 74, 50, 50, 37, 5, 33, 39,
75, 32, 43, 10, 74, 39, 74, 26, 57, 38, 79, 90, 63, 13, 53, 57,
44, 4, 13, 39, 14, 97, 21, 81, 2, 49, 72, 5, 72, 32, 63, 37,
53, 31, 8, 12]])
eggs
array([[ 1. , -0.14396307, -0.01107133],
[-0.14396307, 1. , 0.17098877],
[-0.01107133, 0.17098877, 1. ]])