Fitting a polynomial with numpy changes with dtype, even though actual data values remain the same

I have a dataset comprised of xdata and ydata that I want to fit a polynomial to, but for some reason, the fitting results depend on the dtype of the dataset, even though the actual values of the data remain unchanged. I understand that if you change the dtype e.g. from float to int, that there can be some loss of information, but in this case I am converting from 'f4' to 'f8', thus no information is lost, which is why I am at a loss. What is going on here?

import numpy as np
from numpy.polynomial import polynomial

x32 = np.array([
    1892.8972, 1893.1168, 1893.1626, 1893.4313, 1893.4929, 1895.6392,
    1895.7642, 1896.4286, 1896.5693, 1897.313,  1898.4648
], dtype='f4')

y32 = np.array([
    510.83655, 489.91592, 486.4508,  469.21814, 465.7902,  388.65576,
    385.37637, 369.07236, 365.8301,  349.7118,  327.4062
], dtype='f4')

x64 = x32.astype('f8')
y64 = y32.astype('f8')

a, residuals1, _, _, _ = np.polyfit(x32, y32, 2, full=True)
b, residuals2, _, _, _ = np.polyfit(x64, y64, 2, full=True)

c, (residuals3, _, _, _) = polynomial.polyfit(x32, y32, 2, full=True)
d, (residuals4, _, _, _) = polynomial.polyfit(x64, y64, 2, full=True)

print(residuals1, residuals2, residuals3, residuals4)  # [] [195.86309188] [] [195.86309157]
print(a)        # [ 3.54575804e+00 -1.34738721e+04  1.28004924e+07]
print(b)        # [-8.70836523e-03  7.50419309e-02  3.15525483e+04]
print(c[::-1])  # [ 3.54575804e+00 -1.34738721e+04  1.28004924e+07]
print(d[::-1])  # [-8.7083541e-03   7.5099051e-02   3.1552398e+04 ]

I also only noticed this issue because I'm also interested in the residuals values and they turned up to be empty, which caused my program to crash.

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