Padded with zero bits, either to 96 or 128 bits. Long double in particular, the 128-bit IEEE quad precisionĭata type (FORTRAN’s REAL*16) is not available.įor efficient memory alignment, np.longdouble is usually stored NumPy does not provide a dtype with more precision than C’s Numpy provides with np.finfo(np.longdouble). NumPy makes theĬompiler’s long double available as np.longdouble (and Long double identical to double (64 bits).
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Long double type, MSVC (standard for Windows builds) makes With 80-bit precision, and while most C compilers provide this as their Is possible in numpy depends on the hardware and on the developmentĮnvironment: specifically, x86 machines provide hardware floating-point Useful to use floating-point numbers with more precision. Python’s floating-point numbers are usually 64-bit floating-point numbers, int64 ) # Incorrect even with 64-bit int 0 > np. Numpy.power evaluates 100 ** 8 correctly for 64-bit integers,īut gives 1874919424 (incorrect) for a 32-bit integer. Requires more memory than available in the data type. The fixed size of NumPy numeric types may cause overflow errors when a value
Identical behaviour between arrays and scalars, irrespective of whether the Therefore, the use of array scalars ensures They preserve the array type (Python may not have a matching scalar typeĪvailable, e.g. The primary advantage of using array scalars is that (e.g., int, float, complex, str, unicode). To Python scalars, using the corresponding Python type function Problems are easily fixed by explicitly converting array scalars Or when it checks specifically whether a value is a Python scalar.
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There are someĮxceptions, such as when code requires very specific attributes of a scalar Scalars cannot act as indices for lists and tuples). Array scalars differ from Python scalars, butįor the most part they can be used interchangeably (the primaryĮxception is for versions of Python older than v2.x, where integer array NumPy generally returns elements of arrays as array scalars (a scalar To arrays of that type, or as arguments to the dtype keyword that many numpyįunctions or methods accept. (see the array scalar section for an explanation), python sequences of numbers With low-level code (such as C or Fortran) where the raw memory is addressed.ĭata-types can be used as functions to convert python numbers to array scalars This should be taken into account when interfacing
Intp, have differing bitsizes, dependent on the platforms (e.g. In their name indicate the bitsize of the type (i.e. Unsigned integers (uint) floating point (float) and complex. There are 5 basic numerical types representing booleans (bool), integers (int), The dtypes are available as np.bool_, np.float32, etc.Īdvanced types, not listed above, are explored in NumPy numerical types are instances of dtype (data-type) objects, each Since many of these have platform-dependent definitions, a set of fixed-sizeĪliases are provided (See Sized aliases). Platform-defined extended-precision floatĬomplex number, represented by two single-precision floats (real and imaginary components)Ĭomplex number, represented by two double-precision floats (real and imaginary components).Ĭomplex number, represented by two extended-precision floats (real and imaginary components). Typically sign bit, 11 bits exponent, 52 bits mantissa. Typically sign bit, 8 bits exponent, 23 bits mantissa Sign bit, 5 bits exponent, 10 bits mantissa The primitive types supported are tied closely to those in C:
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This section shows which are available, and how to modify an array’s data-type. NumPy supports a much greater variety of numerical types than Python does.
Data type objects Array types and conversions between types #
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