Available dtypes: Difference between revisions

Latest revision as of 16:43, 17 October 2025

A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions.

Please use numpy.int8, numpy.int16, numpy.int32, and numpy.int64 !!!

numpy.int8:
- 8-bit signed integer (-128 to 127). Compatible with C char.
- numpy.byte
- Character code ‘b’
numpy.int16:
- 16-bit signed integer (-32 768 to 32 767). Compatible with C short
- numpy.short
- Character code ‘h’
numpy.int32:
- 32-bit signed integer (-2 147 483 648 to 2 147 483 647). Compatible with C int
- numpy.intc
- Character code ‘i’
numpy.int64:
- 64-bit signed integer (-9 223 372 036 854 775 808 to 9 223 372 036 854 775 807). Compatible with Python int and C long
- numpy.intp
- numpy.int_
- Character code ‘l’
- numpy.longlong: Compatible with C long long
- Character code ‘q’

Please use numpy.uint8, numpy.uint16, numpy.uint32, and numpy.uint64 !!!

numpy.uint8:
- 8-bit unsigned integer (0 to 255). Compatible with C unsigned char.
- numpy.ubyte
- Character code ‘B’
numpy.uint16:
- 16-bit unsigned integer (0 to 65 535). Compatible with C unsigned short.
- numpy.ushort
- Character code ‘H’
numpy.uint32:
- 32-bit unsigned integer (0 to 4 294 967 295). Compatible with C unsigned int.
- numpy.uintc
- Character code ‘I’
numpy.uint64:
- 64-bit unsigned integer (0 to 18 446 744 073 709 551 615). Compatible with C unsigned long
- numpy.uint
- numpy.uintp
- Character code ‘L’
- numpy.ulonglong: Compatible with C unsigned long long
- Character code ‘Q’

Please use numpy.float32 and numpy.float64!!! (And in very special cases numpy.float128)

numpy.float16:
- 16-bit-precision.
- numpy.half
- floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.
- Character code ‘e’

numpy.float32:
- 32-bit-precision. Compatible with C float
- numpy.single
- floating-point number type: sign bit, 8 bits exponent, 23 bits mantissa.
- Character code ‘f’
numpy.float64:
- 64-bit precision. Compatible with Python float and C double.
- numpy.double
- numpy.float_
- floating-point number type: sign bit, 11 bits exponent, 52 bits mantissa.
- Character code ‘d’
numpy.float128:
- 128-bit extended-precision floating-point number type.
- numpy.longdouble
- numpy.longfloat
- Compatible with C long double but not necessarily with IEEE 754 quadruple-precision.
- Character code ‘g’

Please use numpy.complex64 and numpy.complex128 !!! (And in very special cases numpy.complex256)

numpy.complex64:
- Composed of 2 32-bit-precision floating-point numbers.
- numpy.singlecomplex
- numpy.csingle
- Character code ‘F’
numpy.complex128:
- Composed of 2 64-bit-precision floating-point numbers. Compatible with Python complex.
- numpy.cdouble
- numpy.cfloat
- numpy.complex_
- Character code ‘D’
numpy.complex256:
- Composed of 2 128-bit extended-precision floating-point numbers.
- numpy.clongdouble
- numpy.clongfloat
- numpy.longcomplex
- Character code ‘G’

@@ Line 1: / Line 1: @@
-== The goal ==
 A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions.
@@ Line 51: / Line 50: @@
 == [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Floating-point types] ==
-'''Please use numpy.float32 and numpy.float64 !!! (And in very special cases numpy.float128)'''  * ''numpy.float16'': * 16-bit-precision.  * ''numpy.half'' * floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa. * Character code ‘e’
+'''Please use numpy.float32 and numpy.float64!!! (And in very special cases numpy.float128)'''
+* ''numpy.float16'':
+** 16-bit-precision.
+** ''numpy.half''
+** floating-point number type: sign bit, 5 bits exponent, 10 bits mantissa.
+** Character code ‘e’
 * ''numpy.float32'':