Difference between dtype = np.int and int when creating array

Question

I am confused with the difference between integer types.

For example, here is a numpy.array with dtype of np.int.

>>> arr_ = np.array([1,2], dtype = np.int)

Through the below code, it represents true that int is the same as np.int:

>>> int is np.int
Out[1]: True

However, when I select the first value of the array which is created with dtype of np.int, the below code outputs false.

>>> type(arr_[0]) is int
Out[2]: False

Why does the code output false, not true?

It seems like that dtype = np.int dose not applied on the arr_.

Why np.int dose not applied as a dtype on the array?

I've looked into this, but couldn't get what I need.

@Mahrkeenerh It returns numpy.int32, even I set a dtype np.int. — Peacepieceonepiece
– Peacepieceonepiece, Commented Oct 8, 2021 at 7:11
I'm not sure I understand what you're asking for. The question you linked describes how int (or np.int which is another name for the same type) get converted to np.int_ when used as an array's dtype. That explains the behavior you're seeing exactly. What more do you need to know? — Blckknght
– Blckknght, Commented Oct 8, 2021 at 7:15
The thing you pass as dtype is usually not going to be the type object of elements retrieved from the array. After all, you can do dtype='int32', and the string 'int32' can't be type(anything). — user2357112
– user2357112, Commented Oct 8, 2021 at 7:21

Serge Ballesta · Accepted Answer · 2021-10-08 07:46:45Z

6

In Python the types int, np.int32 and np.int64 are 3 different types:

int is the native Python multiprecision integer type able to represent any integer value (the only limit being available memory). For example 2**128 is a valid int value
np.int32 in the int32_t C integer type that can represent values using up to 32 bits, that is values between -2147483648 and 2147483647
np.int64 is the int64_t C integer type that can represent values using up to 64 bits, that is values between -9223372036854775808 and 9223372036854775807

And np.int is a (deprecated alias) for the native Python int type, the reason why int is np.int is true. But numpy integer arrays even with dtype=int receive an actual type of np.int32 or np.int64 because they have to be processed by C code so they have to by coerced to a fixed size integer type. If you really need to store true int values, you will have to use dtype=object but operations will no longer be vectorizable.

answered Oct 8, 2021 at 7:46

Serge Ballesta

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2 Comments

user2357112 Over a year ago

Note that none of these are an actual dtype. dtypes are represented by numpy.dtype objects rather than by Python types, because dtypes and types represent different things. A dtype's job is to represent stuff like endianness and structured array padding and other memory layout information, while a Python type object's job is to represent information about inheritance hierarchies and how to dispatch methods and operators.

user2357112 Over a year ago

numpy.int32 and numpy.int64 are the types Numpy uses for array scalars of int32 and int64 dtype.

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