More precise way to convert numpy array to list? - arrays

Question

UPDATE:
I went around the problem with a DataFrame:
import pandas as pd
import numpy as np
dict = {'x0':[1,1,1,1,1],'x1':[2,3,5,7,8],'x2':[1,5,3,6,7], 'y':[3,2,4,5,8]}
df = pd.DataFrame(dict)
# y = β(0) + β1x1 + β2x2
X = df[['x0','x1','x2']].to_numpy()
Y = df[['y']].to_numpy()
X_transpose = (X.transpose())
beta_hats = np.linalg.inv(X_transpose.dot(X)).dot(X_transpose.dot(Y))
print(beta_hats)
df = pd.DataFrame(beta_hats)
df.rename(columns = {0:'Beta_Hats'}, inplace = True)
print(df)
I wrote the following program to find the beta coefficients from a set of matrices via NumPy. When I converted the array to a list, I ran into problems: some of the decimal points were off :
Array output:
[[ 0.5 ]
[ 1. ]
[-0.25]]
list output: [[0.49999999999999784], [1.0000000000000022], [-0.2500000000000009]]
I am aware Python has some limitations with calculations, but I was wondering if anyone has figured a way around this. Any help would be much appreciated! I haven't been coding for too long (since May) so sorry if this may seem a bit simple to some of you:
import pandas as pd
import numpy as np
dict = {'x0':[1,1,1,1,1],'x1':[2,3,5,7,8],'x2':[1,5,3,6,7], 'y':[3,2,4,5,8]}
df = pd.DataFrame(dict)
X = df[['x0','x1','x2']].to_numpy()
Y = df[['y']].to_numpy()
X_transpose = (X.transpose())
beta_hats = np.linalg.inv(X_transpose.dot(X)).dot(X_transpose.dot(Y))
print(beta_hats)
list = beta_hats.tolist()
print(list)