There are several major differences. The first is that python integers are flexible-sized (at least in python 3.x). This means they can grow to accommodate any number of any size (within memory constraints, of course). The numpy integers, on the other hand, are fixed-sized. This means there is a maximum value they can hold. This is defined by the number of bytes in the integer (int32 vs. int64), with more bytes holding larger numbers, as well as whether the number is signed or unsigned (int32 vs. uint32), with unsigned being able to hold larger numbers but not able to hold negative number.
So, you might ask, why use the fixed-sized integers? The reason is that modern processors have built-in tools for doing math on fixed-size integers, so calculations on those are much, much, much faster. In fact, python uses fixed-sized integers behind-the-scenes when the number is small enough, only switching to the slower, flexible-sized integers when the number gets too large.
Another advantage of fixed-sized values is that they can be placed into consistently-sized adjacent memory blocks of the same type. This is the format that numpy arrays use to store data. The libraries that numpy relies on are able to do extremely fast computations on data in this format, in fact modern CPUs have built-in features for accelerating this sort of computation. With the variable-sized python integers, this sort of computation is impossible because there is no way to say how big the blocks should be and no consistentcy in the data format.
That being said, numpy is actually able to make arrays of python integers. But rather than arrays containing the values, instead they are arrays containing references to other pieces of memory holding the actual python integers. This cannot be accelerated in the same way, so even if all the python integers fit within the fixed integer size, it still won't be accelerated.
None of this is the case with Python 2. In Python 2, Python integers are fixed integers and thus can be directly translated into numpy integers. For variable-length integers, Python 2 had the long type. But this was confusing and it was decided this confusion wasn't worth the performance gains, especially when people who need performance would be using numpy or something like it anyway.
There are several major differences. The first is that python integers are flexible-sized (at least in python 3.x). This means they can grow to accommodate any number of any size (within memory constraints, of course). The numpy integers, on the other hand, are fixed-sized. This means there is a maximum value they can hold. This is defined by the number of bytes in the integer (int32 vs. int64), with more bytes holding larger numbers, as well as whether the number is signed or unsigned (int32 vs. uint32), with unsigned being able to hold larger numbers but not able to hold negative number.
So, you might ask, why use the fixed-sized integers? The reason is that modern processors have built-in tools for doing math on fixed-size integers, so calculations on those are much, much, much faster. In fact, python uses fixed-sized integers behind-the-scenes when the number is small enough, only switching to the slower, flexible-sized integers when the number gets too large.
Another advantage of fixed-sized values is that they can be placed into consistently-sized adjacent memory blocks of the same type. This is the format that numpy arrays use to store data. The libraries that numpy relies on are able to do extremely fast computations on data in this format, in fact modern CPUs have built-in features for accelerating this sort of computation. With the variable-sized python integers, this sort of computation is impossible because there is no way to say how big the blocks should be and no consistentcy in the data format.
That being said, numpy is actually able to make arrays of python integers. But rather than arrays containing the values, instead they are arrays containing references to other pieces of memory holding the actual python integers. This cannot be accelerated in the same way, so even if all the python integers fit within the fixed integer size, it still won't be accelerated.
None of this is the case with Python 2. In Python 2, Python integers are fixed integers and thus can be directly translated into numpy integers. For variable-length integers, Python 2 had the long type. But this was confusing and it was decided this confusion wasn't worth the performance gains, especially when people who need performance would be using numpy or something like it anyway.
Another way to look at the differences is to ask what methods do the 2 kinds of objects have.
In Ipython I can use tab complete to look at methods:
In [1277]: x=123; y=np.int32(123)
int methods and attributes:
In [1278]: x.<tab>
x.bit_length x.denominator x.imag x.numerator x.to_bytes
x.conjugate x.from_bytes x.real
int 'operators'
In [1278]: x.__<tab>
x.__abs__ x.__init__ x.__rlshift__
x.__add__ x.__int__ x.__rmod__
x.__and__ x.__invert__ x.__rmul__
x.__bool__ x.__le__ x.__ror__
...
x.__gt__ x.__reduce_ex__ x.__xor__
x.__hash__ x.__repr__
x.__index__ x.__rfloordiv__
np.int32 methods and attributes (or properties). Some of the same, but a lot more, basically all the ndarray ones:
In [1278]: y.<tab>
y.T y.denominator y.ndim y.size
y.all y.diagonal y.newbyteorder y.sort
y.any y.dtype y.nonzero y.squeeze
...
y.cumsum y.min y.setflags
y.data y.nbytes y.shape
the y.__ methods look a lot like the int ones. They can do the same math.
In [1278]: y.__<tab>
y.__abs__ y.__getitem__ y.__reduce_ex__
y.__add__ y.__gt__ y.__repr__
...
y.__format__ y.__rand__ y.__subclasshook__
y.__ge__ y.__rdivmod__ y.__truediv__
y.__getattribute__ y.__reduce__ y.__xor__
y is in many ways the same as a 0d array. Not identical, but close.
In [1281]: z=np.array(123,dtype=np.int32)
np.int32 is what I get when I index an array of that type:
In [1300]: A=np.array([0,123,3])
In [1301]: A[1]
Out[1301]: 123
In [1302]: type(A[1])
Out[1302]: numpy.int32
I have to use item to remove all of the numpy wrapping.
In [1303]: type(A[1].item())
Out[1303]: int
As a numpy user, an np.int32 is an int with a numpy wrapper. Or conversely a single element of an ndarray. Usually I don't pay attention as to whether A[0] is giving me the 'native' int or the numpy equivalent. In contrast to some new users, I rarely use np.int32(123); I would use np.array(123) instead.
A = np.array([1,123,0], np.int32)
does not contain 3 np.int32 objects. Rather its data buffer is 3*4=12 bytes long. It's the array overhead that interprets it as 3 ints in a 1d. And view shows me the same databuffer with different interpretations:
In [1307]: A.view(np.int16)
Out[1307]: array([ 1, 0, 123, 0, 0, 0], dtype=int16)
In [1310]: A.view('S4')
Out[1310]: array([b'\x01', b'{', b''], dtype='|S4')
It's only when I index a single element that I get a np.int32 object.
The list L=[1, 123, 0] is different; it's a list of pointers - pointers to int objects else where in memory. Similarly for a dtype=object array.
y do we have int, int16, int32, and int64
python - np.int64 behaves differently from int in math-operations - Stack Overflow
How to convert python int into numpy.int64? - Stack Overflow
New Kid Question - int vs int64?
z_as_int64 = numpy.int64(z)
It's that simple. Make sure you have a good reason, though - there are a few good reasons to do this, but most of the time, you can just use a regular int directly.
import numpy as np
z = 3
z = np.dtype('int64').type(z)
print(type(z))
outputs:
<class 'numpy.int64'>
But i support Juliens question in his comment.
Using Big Query.
I'm learning SQL, but my Google skills have failed me.
I can cast as either int or int64. But for the life of me, I don't understand the difference between the two.
int64 gives me +/- ~9 quintillion numbers to work with. What does int give me?