https://stackoverflow.com › questions › 43440821
The real difference between float32 and float64 - Stack Overflowfloat32 is a 32 bit number - float64 uses 64 bits. That means that float64’s take up twice as much memory - and doing operations on them may be a lot slower in some machine architectures. However, float64’s can represent numbers much more accurately than 32 bit floats.
https://numpy.org › doc › stable › user › basics.types.html
Data types — NumPy v2.1 ManualLearn how to create and manipulate arrays with different data types in NumPy, including float32 and float64. See the characteristics, conversions and examples of numerical, string and byte data types.
https://stackoverflow.com › questions › 16963956
Difference between Python float and numpy float32Python's standard float type is a C double: http://docs.python.org/2/library/stdtypes.html#typesnumeric. NumPy's standard numpy.float is the same, and is also the same as numpy.float64. answered Jun 6, 2013 at 13:56. John Zwinck.
https://numpy.org › doc › stable › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v2.1 ManualLearn how to create and use data type objects (dtype) to describe the memory layout and interpretation of array items in NumPy. See examples of scalar, structured and sub-array data types, and how to specify byte order and size.
https://inside-machinelearning.com › tenseur-et-dtype-uint8-float32-cest-quoi
Tenseur et dtype (uint8, float32, ...), c'est quoi ? - Comprendre ...Le dtype (uint8, float32, …) Une notion dont nous n’avons pas encore parlé est le type des valeurs contenues dans un tenseur aussi appelé dtype. Le dtype c’est la plage sur laquelle sont codé les valeurs (entier ou réel). Ces valeurs sont codés en bits. Par exemple le dtype int8 sera codé sur 8 bits, le int16 sur 16 bits, etc.
https://pythonspeed.com › articles › float64-float32-precision
The problem with float32: you only get 16 million values - Python⇒SpeedSwitching from numpy.float64 (“double-precision” or 64-bit floats) to numpy.float32 (“single-precision” or 32-bit floats) cuts memory usage in half. But it does so at a cost: float32 can only store a much smaller range of numbers, with less precision.
https://docs.scipy.org › doc › numpy-1.13.0 › reference › arrays.dtypes.html
Data type objects (dtype) — NumPy v1.13 Manual - SciPy.orgData type objects (dtype) ¶. A data type object (an instance of numpy.dtype class) describes how the bytes in the fixed-size block of memory corresponding to an array item should be interpreted. It describes the following aspects of the data: Type of the data (integer, float, Python object, etc.)
https://numpy.org › doc › 1.20 › user › basics.types.html
Data types — NumPy v1.20 Manual>>> 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.
https://note.nkmk.me › en › python-numpy-dtype-astype
NumPy: astype() to change dtype of an array | note.nkmk.me - nkmk noteprint((a_int16 + a_float16).dtype) # float32 print((a_int32 + a_float32).dtype) # float64. source: numpy_implicit_type_conversion.py. Note that when assigning values to elements, the dtype does not change. For example, if a floating-point number is assigned to an array of int, the ndarray data type remains int.
https://axil.github.io › a-comprehensive-guide-to-numpy-data-types.html
A Comprehensive Guide to NumPy Data Types - axil's blogWhen you feed a Python int into NumPy, it gets converted into a native NumPy type called np.int32 (or np.int64 depending on the OS, Python version and the magnitude of the initializers): >>> np.array([1, 2, 3]).dtype dtype('int32') # int32 on Windows, int64 on Linux and MacOS.
