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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"

How do I input randomly generated integer values in a 2-d array in Python [duplicate]

This question already has answers here:
Simple way of creating a 2D array with random numbers (Python)
(7 answers)
Closed 2 years ago.
I have written my code like this:
import random
a = []
for i in range(10):
for j in range(10):
a.append(random.randint(0,100))
x = np.shape(a)
print(a)
This is my output:
[[], 79, 46, 29, 48, 88, 43, 57, 53, 70, 55, 89, 19, 49, 11, 79, 41, 76, 82, 90, 91, 21, 86, 67, 80, 93, 13, 38, 51, 27, 43, 50, 79, 87, 23, 27, 1, 64, 43, 81, 67, 48, 35, 9, 50, 48, 70, 73, 94, 58, 75, 60, 43, 73, 88, 51, 12, 74, 88, 72, 83, 100, 7, 10, 50, 13, 64, 74, 37, 76, 44, 37, 46, 42, 20, 20, 100, 81, 11, 83, 27, 76, 29, 15, 3, 18, 81, 5, 34, 85, 99, 88, 53, 75, 53, 12, 19, 62, 1, 51, 44]
It is a 10 x 10 array. The first value is an array and 99 rest of the values. Can anyone help me solve this issue?

Try this:
import random
[[random.randint(0,100) for i in range(10)] for j in range(10)]

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. ]])

numpy.correlate vs numpy documentation - is there a contradiction here ? Why is the resulting list reversed ?

I get the following result using numpy's correlate function:
In [153]: np.correlate([1],np.arange(100))
Out[153]:
array([99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83,
82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66,
65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49,
48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32,
31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15,
14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])
In [154]:
This result seems to be in contradiction with the 90. page of the numpy book :
Based on the formula above I would have expected an increasing array 0..99, however, the result is a decreasing array 99..0.
Can someone explain what is going on here ?
Why does the implementation contradicts the specification ?
Why does it make sense to reverse the list ?

Looks like you are expecting the old_behaviour of numpy.correlate. The book you link to is very old (2006), so it looks like numpy.correlate has changed since it was written (it actually changed in numpy v1.4). From the docs for numpy v1.9:
old_behavior : bool
If True, uses the old behavior from Numeric, (correlate(a,v) == correlate(v,a), and the conjugate is not taken for complex arrays). If False, uses the conventional signal processing definition.
In [2]: np.correlate([1],np.arange(100))
Out[2]:
array([99, 98, 97, 96, 95, 94, 93, 92, 91, 90, 89, 88, 87, 86, 85, 84, 83,
82, 81, 80, 79, 78, 77, 76, 75, 74, 73, 72, 71, 70, 69, 68, 67, 66,
65, 64, 63, 62, 61, 60, 59, 58, 57, 56, 55, 54, 53, 52, 51, 50, 49,
48, 47, 46, 45, 44, 43, 42, 41, 40, 39, 38, 37, 36, 35, 34, 33, 32,
31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15,
14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])
In [3]: np.correlate([1],np.arange(100),old_behavior=True)
Out[3]:
array([ 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])
In [4]: np.correlate(np.arange(100),[1])
Out[4]:
array([ 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])
EDIT
On further inspection, I think the difference is due to this line in the old definition:
K=len(x)-1 and M=len(y)-1, and we assume K ≥ M (without loss of generality because we can interchange the roles of x and y without effect).
So, I believe for your case, in the old definition, it is making y=[1] and x=np.arange(100), because len(x) must be greater than len(y). The new definition does not do that, instead "input arrays are never swapped", so x=[1] and y=np.arange(100). Thus, the differences.