Numpy supports a much greater variety of numerical types than Python does. This section shows which are available, and how to modify an array’s data-type.
|bool||Boolean (True or False) stored as a byte|
|int||Platform integer (normally either int32 or int64)|
|int8||Byte (-128 to 127)|
|int16||Integer (-32768 to 32767)|
|int32||Integer (-2147483648 to 2147483647)|
|int64||Integer (9223372036854775808 to 9223372036854775807)|
|uint8||Unsigned integer (0 to 255)|
|uint16||Unsigned integer (0 to 65535)|
|uint32||Unsigned integer (0 to 4294967295)|
|uint64||Unsigned integer (0 to 18446744073709551615)|
|float||Shorthand for float64.|
|float32||Single precision float: sign bit, 8 bits exponent, 23 bits mantissa|
|float64||Double precision float: sign bit, 11 bits exponent, 52 bits mantissa|
|complex||Shorthand for complex128.|
|complex64||Complex number, represented by two 32-bit floats (real and imaginary components)|
|complex128||Complex number, represented by two 64-bit floats (real and imaginary components)|
Numpy numerical types are instances of dtype (data-type) objects, each having unique characteristics. Once you have imported NumPy using
>>> import numpy as np
the dtypes are available as np.bool, np.float32, etc.
Advanced types, not listed in the table above, are explored in section Structured arrays (aka “Record arrays”).
There are 5 basic numerical types representing booleans (bool), integers (int), unsigned integers (uint) floating point (float) and complex. Those with numbers in their name indicate the bitsize of the type (i.e. how many bits are needed to represent a single value in memory). Some types, such as int and intp, have differing bitsizes, dependent on the platforms (e.g. 32-bit vs. 64-bit machines). This should be taken into account when interfacing with low-level code (such as C or Fortran) where the raw memory is addressed.
Data-types can be used as functions to convert python numbers to array scalars (see the array scalar section for an explanation), python sequences of numbers to arrays of that type, or as arguments to the dtype keyword that many numpy functions or methods accept. Some examples:
>>> import numpy as np >>> x = np.float32(1.0) >>> x 1.0 >>> y = np.int_([1,2,4]) >>> y array([1, 2, 4]) >>> z = np.arange(3, dtype=np.uint8) >>> z array([0, 1, 2], dtype=uint8)
Array types can also be referred to by character codes, mostly to retain backward compatibility with older packages such as Numeric. Some documentation may still refer to these, for example:
>>> np.array([1, 2, 3], dtype='f') array([ 1., 2., 3.], dtype=float32)
We recommend using dtype objects instead.
To convert the type of an array, use the .astype() method (preferred) or the type itself as a function. For example:
>>> z.astype(float) array([ 0., 1., 2.]) >>> np.int8(z) array([0, 1, 2], dtype=int8)
Note that, above, we use the Python float object as a dtype. NumPy knows that int refers to np.int, bool means np.bool and that float is np.float. The other data-types do not have Python equivalents.
To determine the type of an array, look at the dtype attribute:
>>> z.dtype dtype('uint8')
dtype objects also contain information about the type, such as its bit-width and its byte-order. The data type can also be used indirectly to query properties of the type, such as whether it is an integer:
>>> d = np.dtype(int) >>> d dtype('int32') >>> np.issubdtype(d, int) True >>> np.issubdtype(d, float) False
Numpy generally returns elements of arrays as array scalars (a scalar with an associated dtype). Array scalars differ from Python scalars, but for the most part they can be used interchangeably (the primary exception is for versions of Python older than v2.x, where integer array scalars cannot act as indices for lists and tuples). There are some exceptions, such as when code requires very specific attributes of a scalar or when it checks specifically whether a value is a Python scalar. Generally, problems are easily fixed by explicitly converting array scalars to Python scalars, using the corresponding Python type function (e.g., int, float, complex, str, unicode).
The primary advantage of using array scalars is that they preserve the array type (Python may not have a matching scalar type available, e.g. int16). Therefore, the use of array scalars ensures identical behaviour between arrays and scalars, irrespective of whether the value is inside an array or not. NumPy scalars also have many of the same methods arrays do.