Python Array Element Assignment bug? [Fixed]

Question

I'm trying to assign an array element to a number but it keeps resetting back to 0 once printed or used. The sum and the multiple just become 0.

I'm trying to plot a line as a result of linear regression in 3D

Is it a bug or am I doing something wrong?

Example:

x1[i] = a2D[i][0] - xmean
print(a2D[i][0], xmean, x1[i], a2D[i][0] - xmean)
# Output: 0.1 0.045 0.0 0.05500000000000001
# Expected Output: 0.1 0.045 0.05500000000000001 0.05500000000000001

How to fix it?

Python version: Python 3.9.7

OS: Windows 11

Full Code:

# importing mplot3d toolkits
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

import random

fig = plt.figure()
a1D = np.array([[0,0,0]])
a2D = np.array([[0,0,0]])
pointcount = 10
for i in range(1,pointcount):
    x = i/10
    y = i/10 + random.randint(-2,2)
    z = 0
    
    a2D = np.concatenate((a2D, [[x,y,z]]), axis = 0)
    
xmean, ymean, zmean = 0,0,0
xsum, ysum, zsum = 0,0,0
emp = [0 for i in range(100)]
x1, y1, z1 = emp,emp,emp
xsquare = emp
xymult = emp
xzmult = emp

"""
Fixed
x1, y1, z1 = [0] * 100,[0] * 100,[0] * 100
xsquare = [0] * 100
xymult = [0] * 100
xzmult = [0] * 100
"""
xysum = 0
xzsum = 0
xsquaresum = 0

for i in range(1,pointcount):
    xsum += a2D[i][0]
    ysum += a2D[i][1]
    zsum += a2D[i][2]
xmean, ymean, zmean = xsum/100, ysum/100, zsum/100
print(xmean, ymean, zmean)
for i in range(1,pointcount):
    x1[i] = a2D[i][0] - xmean
    y1[i] = a2D[i][1] - ymean
    z1[i] = a2D[i][2] - zmean
    
    print(a2D[i][0], xmean, x1[i], a2D[i][0] - xmean)
    xsquare[i] = x1[i] * x1[i]
    xymult[i] = x1[i] * y1[i]
    xzmult[i] = x1[i] * z1[i]    
    
    xsquaresum += xsquare[i]
    xysum += xymult[i]
    xzsum += xzmult[i]
    #print(xzmult[i])

print(xysum, xsquaresum)
print(xzsum, xsquaresum)
m1 = xysum/xsquaresum
m2 = xzsum/xsquaresum
c1 = ymean - m1*xmean
c2 = zmean - m2*xmean

print(m1, c1)
print(m2, c2)
    
print(a2D[0])

for i in range(1,100):
    t = i/10
    x = t
    y = m1*t + c1
    z = m2*t + c2
    a1D = np.concatenate((a1D, [[x,y,z]]), axis = 0)
#print(a1D)
# syntax for 3-D projection
ax = fig.add_subplot(111, projection='3d')

ax.plot3D(a1D[:,0], a1D[:,1], a1D[:,2])
ax.plot3D(a2D[:,0], a2D[:,1], a2D[:,2], color='green')
sc = ax.scatter(a2D[:,0], a2D[:,1], a2D[:,2], color='green', s=0.5)
# syntax for plotting
ax.set_title('3d Scatter Plot')
fig.show()
plt.pause(-1)

[Output in Terminal][1] [1]: https://i.stack.imgur.com/zXYX4.png