.astype() is a method within numpy.ndarray, as well as the Pandas Series class, so can be used to convert vectors, matrices and columns within a DataFrame. However, int() is a pure-Python function that can only be applied to scalar values.

For example, you can do int(3.14), but can't do (2.7).astype('int'), because Python native types don't have any such method. However, numpy.array([1.1, 2.2, 3.3]).astype('int') is valid.

(Strictly, it is also possible to define an __int__() method within one's own classes, which would allow int() to be applied to non-native types. Thanks to @juanpa.arrivillaga for pointing this out.)

Pandas uses numpy datatypes under the hood. From the numpy documentation,

The default NumPy behavior is to create arrays in either 32 or 64-bit signed integers (platform dependent and matches C int size) or double precision floating point numbers, int32/int64 and float, respectively. If you expect your integer arrays to be a specific type, then you need to specify the dtype while you create the array.

It is not a bug and you should be specifying dtypes if you have a specific use or want to be platform agnostic. To rephrase your question, what is np.dtype(int) on my platform?

On windows, as some of the comments suggest, it appears to be a C signed long (32 bits). You can even get numpy to throw an overflow error to confirm this.

>>> import numpy as np
>>> np.array([2_147_483_648], dtype=int) 
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
OverflowError: Python int too large to convert to C long