Davrot at 16:43, 17 October 2025

2025-10-17T16:43:45Z

← Older revision		Revision as of 16:43, 17 October 2025
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	~~== The goal ==~~
	A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions.		A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions.

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	== [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Floating-point types] ==		== [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'':		* ''numpy.float32'':

Davrot at 12:54, 17 October 2025

2025-10-17T12:54:43Z

← Older revision		Revision as of 12:54, 17 October 2025
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	** Character code ‘q’		** Character code ‘q’

	~~<nowiki>##</nowiki>~~ [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Unsigned integer types]		==== [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Unsigned integer types] ====

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

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	** Character code ‘g’		** Character code ‘g’

	~~<nowiki>##</nowiki>~~ [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Complex floating-point types]		==== [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Complex floating-point types] ====

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

Davrot: Created page with "== The goal == A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions. Questions to [mailto:davrot@uni-bremen.de David Rotermund] == [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Signed integer types] == '''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'..."

2025-10-17T12:54:03Z

Created page with "== The goal == A numpy matrix can have differnt dtype or – in other words – differnt types of numbers with different precisions. Questions to [mailto:davrot@uni-bremen.de David Rotermund] == [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Signed integer types] == '''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'..."

New page

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

Questions to [mailto:davrot@uni-bremen.de David Rotermund]

== [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Signed integer types] ==
'''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’

<nowiki>##</nowiki> [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Unsigned integer types]

'''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’

== [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’

* ''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’

<nowiki>##</nowiki> [https://numpy.org/doc/stable/reference/arrays.scalars.html#sized-aliases Complex floating-point types]

'''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’

Available dtypes - Revision history

Davrot at 16:43, 17 October 2025

Davrot at 12:54, 17 October 2025