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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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# dtype
{:.no_toc}
<nav markdown="1" class="toc-class">
* TOC
{:toc}
</nav>
## The goal
A numpy matrix can have differnt dtype or -- in other words -- differnt types of numbers with different precisions.
Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
## 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'​
​
## 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'
​
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