How can I use pandas dataframe to_dict with float32 without additional float decimals

Question

I'd like to use dtype='float32' (it is probably a numpy dtype => np.float32) instead of dtype='float64' to reduce memory usage of my pandas dataframe, because I have to handle hugh pandas dataframes.

At one point, I'd like to extract a python list with '.to_dict(orient='records')' in order to get a dictionary for each row.

In this case, I will get additional decimal places, which are probably based on s.th like this:

Is floating point math broken?

How can I cast the date / change the type etc. in order to get the same result, as I get with float64 (see example snippets)?

import pandas as pd

_data = {'col1': [1.45123, 1.64123], 'col2': [0.1, 0.2]}

_test = pd.DataFrame(_data).astype(dtype='float64')

print(f"{_test=}")
print(f"{_test.round(1)=}")
print(f"{_test.to_dict(orient='records')=}")
print(f"{_test.round(1).to_dict(orient='records')=}")

float64 output:

_test=      col1  col2
0  1.45123   0.1
1  1.64123   0.2
_test.round(1)=   col1  col2
0   1.5   0.1
1   1.6   0.2
_test.to_dict(orient='records')=[{'col1': 1.45123, 'col2': 0.1}, {'col1': 1.64123, 'col2': 0.2}]
_test.round(1).to_dict(orient='records')=[{'col1': 1.5, 'col2': 0.1}, {'col1': 1.6, 'col2': 0.2}]

import pandas as pd

_data = {'col1': [1.45123, 1.64123], 'col2': [0.1, 0.2]}

_test = pd.DataFrame(_data).astype(dtype='float32')

print(f"{_test=}")
print(f"{_test.round(1)=}")
print(f"{_test.to_dict(orient='records')=}")
print(f"{_test.round(1).to_dict(orient='records')=}")

float32 output:

_test=      col1  col2
0  1.45123   0.1
1  1.64123   0.2
_test.round(1)=   col1  col2
0   1.5   0.1
1   1.6   0.2
_test.to_dict(orient='records')=[{'col1': 1.4512300491333008, 'col2': 0.10000000149011612}, {'col1': 1.6412299871444702, 'col2': 0.20000000298023224}]
_test.round(1).to_dict(orient='records')=[{'col1': 1.5, 'col2': 0.10000000149011612}, {'col1': 1.600000023841858, 'col2': 0.20000000298023224}]