Getting "ValueError: data type <class 'numpy.object_'> not inexact" error while trying to linear fit a dataset using uncertainities - numpy

Question

I am very new to python so i am struggling a lot to do what i want to do, so i figured i could ask.
I have an excel sheet with data columns like period, pdot, flux values etc. There are also error columns associated with these. I want to plot these in python, and then do a linear fit while counting in the errors. Then obtain values like standard deviation or p-value to decide the goodness of the fit. Then using this fit i will try to predict values based on a missing parameter. I managed to do it without the errors, but now im trying to do it while propagating my error and its causing me some errors.
My working code that doesnt take errors into consideration is like this:
dist_array1= np.multiply(3.08567758128*10**21,dist_array)
dist_array2 = np.multiply(dist_array1,dist_array1)
e1=np.multiply(4*math.pi,dist_array2)
L_gamma = np.multiply(e1,flux_array)
Gamma_Eff = np.divide(L_gamma,edot_array)
Tau = np.divide(period_array,pdot_array)
constant = 2.94*10**8
t1=np.power(period_array,-5)
t2=np.multiply(t1,pdot_array)
t3=np.power(t2,1/2)
B_LC = np.multiply(constant,t3)
c1=np.multiply(10**15,pdot_array)
c2=np.log(c1)
c3=np.log(period_array)
c4=1-np.multiply(11/7,c3)+np.multiply(4/7,c2)
c5=3.56-c3-c2
Zeta1=1+np.divide(c4,c5)
c6=0.8-np.multiply(2/7,c3)+np.multiply(2/7,c2)
Zeta2=1+np.divide(c6,1.3)
c8=0.6-np.multiply(11/14,c3)+np.multiply(2/7,c2)
Zeta3=1+np.divide(c8,1.3)
#Here i defined my variables that i will work with, now i will try to fit it.
x1 = np.log(period_array)
y1 = np.log(Gamma_Eff)
coef1, V1 = np.polyfit(x1,y1,1, cov=True)
poly1d_fn1 = np.poly1d(coef1)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3,figsize=(30,10))
fig.suptitle('Figure 1')
ax1.plot(x1,y1, 'yo', x1, poly1d_fn1(x1), '-k')
x2 = np.log(Tau)
coef2, V2 = np.polyfit(x2,y1,1, cov=True)
poly1d_fn2 = np.poly1d(coef2)
ax2.plot(x2,y1, 'yo', x2, poly1d_fn2(x2), '-k')
x3= np.log(B_LC)
coef3, V3 = np.polyfit(x3,y1,1, cov=True)
poly1d_fn3 = np.poly1d(coef3)
ax3.plot(x3,y1, 'yo', x3, poly1d_fn3(x3), '-k')
ax1.set(xlabel='log P (s)', ylabel='log η')
ax2.set(xlabel='log τ (yr)', ylabel='log η')
ax3.set(xlabel='log B_LC (G)', ylabel='log η')
#And then obtain the uncertainities
sigma_period_1=np.sqrt(V1[0][0])
sigma_period_2=np.sqrt(V1[1][1])
sigma_Tau_1=np.sqrt(V2[0][0])
sigma_Tau_2=np.sqrt(V2[1][1])
sigma_B_LC_1=np.sqrt(V3[0][0])
sigma_B_LC_2=np.sqrt(V3[1][1])
Now this works well and i can fit it, the problem is i cannot get stuff like p-value or standard deviation from the fit. I think i need to use statsmodels for that. And i also need to put errors into the formulas to be more accurate. What i changed to obtain this so far is as follows:
period_array= unumpy.uarray(period_array,perioderr_array) # Here im combining the error and the value so that i can use it propagates the error.
pdot_array=unumpy.uarray(pdot_array,pdoterr_array) #Same thing for the second value with error
flux_array=unumpy.uarray(flux_array,flux_err_array) #Same thing for third
c2=unumpy.log(c1) #Here i had to use unumpy instead of np because it gave me errors when using log function
c3=unumpy.log(period_array) #Same thing
Then i tried to fit using polyfit, to see if it works, then i will try to get the same fit with statsmodels.
x1 = unumpy.log(period_array) #log issue again
y1 = unumpy.log(Gamma_Eff)
coef1, V1 = np.polyfit(x1,y1,1, cov=True)
The last line gives me the error "ValueError: data type <class 'numpy.object_'> not inexact" I did some digging and i understood the problem as "my values are not float, and this is why im getting error, so i need to turn them into float". To do this i tried many things including stuff like x = list(x) but to no avail.
So what am i doing wrong?