3.1. Array Data Types¶

Array can have only one data type (dtype)
Type can be "non-primitive" - any class

3.1.1. SetUp¶

>>> import numpy as np

3.1.2. Bits and Bytes¶

Signed and unsigned
Unsigned cannot be negative
For negative signed numbers "Two's complement" is used

1       # unsigned

+1      # signed
-1      # signed

3 bit unsigned integers. Values: 8, minimal: 0, maximal: 8:

3 bit signed integers. Values: 8, minimal: -4, maximal: 3:

+0      000
+1      001
+2      010
+3      011
−4      100
−3      101
−2      110
−1      111

8 bit signed integers. Values: 256, minimal: -128, maximal: 127:

+0      00000000
+1      00000001
+2      00000010
+126    01111110
+127    01111111
−128    10000000
−127    10000001
−126    10000010
−2      11111110
−1      11111111

32 bit unsigned int. Values: 2,147,483,647, minimal: 0, maximal: 2,147,483,647:

     0000000000000000000000000000000000
     0000000000000000000000000000000001
     0000000000000000000000000000000010
     0000000000000000000000000000000011
     0000000000000000000000000000000100
     0000000000000000000000000000000101
     0000000000000000000000000000000110
     0000000000000000000000000000000111

Calculates a two's complement integer from the given input value's bits:

>>> def twos_complement(value: int, num_bits: int) -> int:
...     mask = 2 ** (num_bits - 1)
...     return -(value & mask) + (value & ~mask)

>>> np.binary_repr(0, 8)  # number 0 using 8-bit integer representation
'00000000'
>>>
>>> np.binary_repr(1, 8)  # number 1 using 8-bit integer representation
'00000001'
>>>
>>> np.binary_repr(2, 8)  # number 2 using 8-bit integer representation
'00000010'
>>>
>>> np.binary_repr(3, 8)  # number 3 using 8-bit integer representation
'00000011'
>>>
>>> np.binary_repr(-1, 8)  # number -1 using 8-bit integer representation
'11111111'
>>>
>>> np.binary_repr(-2, 8)  # number -2 using 8-bit integer representation
'11111110'
>>>
>>> np.binary_repr(-3, 8)  # number -3 using 8-bit integer representation
'11111101'

3.1.3. Comparison¶

>>> data = 69
>>>
>>> np.binary_repr(data, 8)  # np.int8
'01000101'
>>>
>>> np.binary_repr(data, 16)  # np.int16
'0000000001000101'
>>>
>>> np.binary_repr(data, 32)  # np.int32
'00000000000000000000000001000101'
>>>
>>> np.binary_repr(data, 64)  # np.int64
'0000000000000000000000000000000000000000000000000000000001000101'

3.1.4. Signed int¶

Signed (positive and negative)
np.int alias for np.int64
np.int0 alias for np.int64 - Integer used for indexing
np.int8
np.int16
np.int32
np.int64

Table 3.19. Number of values is calculated with `2 ** bytes`¶
Type	Bits	Number of Values	Minimal	Maximal
`np.int8`	8	256	-128	127
`np.int16`	16	65,536	-32,768	32,767
`np.int32`	32	4,294,967,296	-2,147,483,648	2,147,483,646
`np.int64`	64	18,446,744,073,709,551,616	-9,223,372,036,854,775,808	9,223,372,036,854,775,807

>>> a = np.array([1, 2, 3])
>>>
>>> type(a)
<class 'numpy.ndarray'>
>>>
>>> a.dtype
dtype('int64')

>>> a = np.array([[1., 2., 3.],
...               [4., 5., 6.]])
>>>
>>> a.astype(int)
array([[1, 2, 3],
       [4, 5, 6]])
>>>
>>> a.astype(np.int8)
array([[1, 2, 3],
       [4, 5, 6]], dtype=int8)
>>>
>>> a.astype(np.int64)
array([[1, 2, 3],
       [4, 5, 6]])

3.1.5. Unsigned int¶

Unsigned (non-negative only)
np.uint0
np.uint8
np.uint16
np.uint32
np.uint64

Table 3.20. Number of values is calculated with `2 ** bytes`¶
Type	Bits	Number of Values	Maximal
`np.uint8`	8	256	255
`np.uint16`	16	65,536	65,535
`np.uint32`	32	4,294,967,296	4,294,967,295
`np.uint64`	64	18,446,744,073,709,551,616	18,446,744,073,709,551,615

>>> a = np.array([-1, 0, 1])
>>>
>>> type(a)
<class 'numpy.ndarray'>
>>>
>>> a.dtype
dtype('int64')

>>> a = np.array([-1, 0, 1])
>>>
>>> a.astype(int)
array([-1,  0,  1])
>>>
>>> a.astype(np.uint8)
array([255,   0,   1], dtype=uint8)
>>>
>>> a.astype(np.uint64)  
array([18446744073709551615, 0, 1], dtype=uint64)

3.1.6. float¶

np.float
np.float16
np.float32
np.float64
np.float128

Table 3.21. Number of values is calculated with `2 ** bytes`¶
Type	Bits	Minimal	Maximal
`np.float16`	16	-65,504	65,504
`np.float32`	32	±0.000000×10−95	±9.999999×1096
`np.float64`	64	±0.000000000000000×10−383	±9.999999999999999×10384
`np.float128`	64	±0.000000000000000000000000000000000×10−6143	±9.999999999999999999999999999999999×106144

>>> a = np.array([1., 2., 3.])
>>>
>>> type(a)
<class 'numpy.ndarray'>
>>>
>>> a.dtype
dtype('float64')

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a.astype(float)
array([[1., 2., 3.],
       [4., 5., 6.]])
>>>
>>> a.astype(np.float16)
array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float16)
>>>
>>> a.astype(np.float32)
array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float32)
>>>
>>> a.astype(np.float64)
array([[1., 2., 3.],
       [4., 5., 6.]])
>>>
>>> a.astype(np.float128)
array([[1., 2., 3.],
       [4., 5., 6.]], dtype=float128)

3.1.7. complex¶

np.complex
np.complex64
np.complex128
np.complex256

>>> a = np.array([1+2j])
>>>
>>> a.dtype
dtype('complex128')

>>> a = np.array([1.1+2.2j])
>>>
>>> a.dtype
dtype('complex128')

3.1.8. bool¶

>>> a = np.array([True, False, True])
>>>
>>> a.dtype
dtype('bool')

>>> a = np.array([1, 0, 1], bool)
>>>
>>> a.dtype
dtype('bool')
>>>
>>> a
array([ True, False,  True])

3.1.9. str¶

>>> np.array(['a', 'b', 'c'])
array(['a', 'b', 'c'], dtype='<U1')
>>>
>>> np.array(['one', 'two', 'three'])
array(['one', 'two', 'three'], dtype='<U5')

3.1.10. Comparison¶

>>> a = np.array([[1, 2, 3],
...              [4, 5, 6],
...              [7, 8, 9]], dtype='int8')
>>> a.itemsize
1
>>> a.size
9
>>> a.nbytes
9

>>> b = np.array([[1, 2, 3],
...              [4, 5, 6],
...              [7, 8, 9]], dtype='int32')
>>> b.itemsize
4
>>> b.size
9
>>> b.nbytes
36

>>> c = np.array([[1, 2, 3],
...              [4, 5, 6],
...              [7, 8, 9]], dtype='int64')
>>> c.itemsize
8
>>> c.size
9
>>> c.nbytes
72

3.1.11. Assignments¶

Code 3.38. Solution¶

"""
* Assignment: Numpy Dtype Astype
* Complexity: easy
* Lines of code: 2 lines
* Time: 3 min

English:
    1. Given `DATA: np.ndarray` (see below)
    2. Convert to `int` and save result as `result_int`
    3. Convert to `bool` and save result as `result_bool`
    4. What happened in each of those steps?
    5. Run doctests - all must succeed

Polish:
    1. Dany `DATA: np.ndarray` (patrz sekcja input)
    2. Przekonwertuj do typu `int` i wynik zapisz jako `result_int`
    3. Przekonwertuj do typu `bool` i wynik zapisz jako `result_bool`
    4. Co się stało w każdym z tych kroków?
    5. Uruchom doctesty - wszystkie muszą się powieść

Tests:
    >>> import sys; sys.tracebacklimit = 0

    >>> assert result_int is not Ellipsis, \
    'Assign result to variable: `result_int`'
    >>> assert result_bool is not Ellipsis, \
    'Assign result to variable: `result_bool`'
    >>> assert type(result_int) is np.ndarray, \
    'Variable `result_int` has invalid type, expected: np.ndarray'
    >>> assert type(result_bool) is np.ndarray, \
    'Variable `result_bool` has invalid type, expected: np.ndarray'

    >>> result_int
    array([[-1,  0,  1],
           [ 2,  3,  4]])

    >>> result_bool
    array([[ True, False,  True],
           [ True,  True,  True]])
"""

import numpy as np


DATA = np.array([[-1.1, 0.0, 1.1],
                 [2.2, 3.3, 4.4]])


# DATA converted to int
# type: np.ndarray
result_int = ...

# DATA converted to bool
# type: np.ndarray
result_bool = ...