NumPy: Data types

Last update on August 19 2022 21:51:10 (UTC/GMT +8 hours)

Data types

NumPy supports following numerical types:

Data type	Description	Value/Range
bool_	Boolean stored as a byte.	True or False
int_	Default integer type.	Same as C long; normally either int64 or int32.
intc	Identical to C int.	Normally int32 or int64.
intp	Integer used for indexing.	Same as C ssize_t; 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.
float16	Half precision float.	sign bit, 5 bits exponent, 10 bits mantissa
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,	two 32-bit floats (real and imaginary components)
complex128	Complex number,	two 64-bit floats (real and imaginary components)

There are 5 basic numerical types representing booleans (bool), integers (int), unsigned integers (uint) floating point (float) and complex. Some types, such as int and intp, have differing bitsizes, dependent on the platforms (e.g. 32-bit vs. 64-bit machines).

Here are some examples:

>>> import numpy as np
>>> a = np.float32(2.0)
>>> a
2.0
>>> b = np.int_([3, 5, 7])
>>> b
array([3, 5, 7])

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:

>>> import numpy as np
>>> np.array([3, 5, 7], dtype='f')
array([3., 5., 7.], dtype=float32)

To convert the type of an array, use the .astype() method (preferred) or the type itself as a function. For example:

>> import numpy as np
>>> c = np.arange(5, dtype=np.uint8)
>>> (c.astype(float))
array([ 0.,  1.,  2.,  3.,  4.])
>>> (np.int8(c))
array([0, 1, 2, 3, 4], dtype=int8)

To determine the type of an array, look at the dtype attribute:

>>> import numpy as np
>>> c = np.arange(5, dtype=np.uint8)
>>> print(c.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:

>>> import numpy as np
>>> np.int8(c)
array([0, 1, 2, 3, 4], dtype=int8)
>>> x = np.dtype(int)
>>> x
dtype('int32')
>>> np.issubdtype(x, np.integer)
True
>>> np.issubdtype(x, np.floating)
False

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