Why replace treats an integer value 999 as 999.0?

[new_to_python]

Hi, I came across an example to replace sentinel values -999 by NaN. -999 is an integer. How come it can be used to replace those -999.0 elements in the Series?

import pandas as pd

In [39]: data = pd.Series([1., -999., 2., -999., -1000., 3.])                                                         

In [40]: data                                                                                                         
Out[40]: 
0       1.0
1    -999.0
2       2.0
3    -999.0
4   -1000.0
5       3.0
dtype: float64

In [41]: data.replace(-999, np.nan)                                                                                   
Out[41]: 
0       1.0
1       NaN
2       2.0
3       NaN
4   -1000.0
5       3.0
dtype: float64

I did a test by typing:

-999 == -999.

The interpreter gave True. Does the method "replace" automatically did type casting implicitely to convert decimal values to the nearest integer? I can't find this mentioned in

 ?pd.Series.replace 

I have seen somewhere else that an integer (e.g. 3) is used interchangeably with its decimal counterpart (e.g. 3.). Why? integer 3 is not the same as 3.000000000000000000001. Is there a rule here?