4.5. Array Logic

4.5.1. SetUp

>>> import numpy as np

4.5.2. Contains

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> 2 in a
True
>>>
>>> 0 in a
False
>>>
>>> [1, 2, 3] in a
True

4.5.3. Is In

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> b = np.array([1, 5, 9])
>>>
>>> np.isin(a, b)
array([[ True, False, False],
       [False,  True, False]])

4.5.4. Scalar Comparison

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a == 2
array([[False,  True, False],
       [False, False, False]])
>>>
>>> a != 2
array([[ True, False,  True],
       [ True,  True,  True]])
>>>
>>> a > 2
array([[False, False,  True],
       [ True,  True,  True]])
>>>
>>> a >= 2
array([[False,  True,  True],
       [ True,  True,  True]])
>>>
>>> a < 2
array([[ True, False, False],
       [False, False, False]])
>>>
>>> a <= 2
array([[ True,  True, False],
       [False, False, False]])

4.5.5. Broadcasting Comparison

>>> import numpy as np
>>>
>>>
>>> a = np.array([1, 2, 3])
>>> b = np.array([3, 2, 1])
>>>
>>> a == b
array([False,  True, False])
>>>
>>> a != b
array([ True, False,  True])
>>>
>>> a > b
array([False, False,  True])
>>>
>>> a >= b
array([False,  True,  True])
>>>
>>> a < b
array([ True, False, False])
>>>
>>> a <= b
array([ True,  True, False])

4.5.6. Any

>>> import numpy as np
>>>
>>>
>>> a = np.array([True, False, False])
>>>
>>> a.any()
True
>>> import numpy as np
>>>
>>>
>>> a = np.array([[True, False, False],
...               [True, True, True]])
>>>
>>> a.any()
True
>>>
>>> a.any(axis=0)
array([ True,  True,  True])
>>>
>>> a.any(axis=1)
array([ True,  True])

4.5.7. All

>>> import numpy as np
>>>
>>>
>>> a = np.array([True, False, False])
>>>
>>> a.all()
False
>>> import numpy as np
>>>
>>>
>>> a = np.array([[True, False, False],
...               [True, True, True]])
>>>
>>> a.all()
False
>>>
>>> a.all(axis=0)
array([ True, False, False])
>>>
>>> a.all(axis=1)
array([False,  True])

4.5.8. Logical NOT

  • np.logical_not(...)

  • ~(...)

>>> import numpy as np
>>>
>>>
>>> a = np.array([[True, False, False],
...               [True, True, True]])
>>>
>>> np.logical_not(a)
array([[False,  True,  True],
       [False, False, False]])
>>>
>>> ~a
array([[False,  True,  True],
       [False, False, False]])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.logical_not(a > 2)
array([[ True,  True, False],
       [False, False, False]])
>>>
>>> ~(a > 2)
array([[ True,  True, False],
       [False, False, False]])

4.5.9. Logical AND

  • Meets first and second condition at the same time

  • np.logical_and(..., ...)

  • (...) & (...)

>>> import numpy as np
>>>
>>>
>>> a = np.array([True, False, False])
>>> b = np.array([True, True, False])
>>>
>>> np.logical_and(a, b)
array([ True, False, False])
>>>
>>> a & b
array([ True, False, False])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.logical_and(a > 2, a < 5)
array([[False, False,  True],
       [ True, False, False]])
>>>
>>> (a > 2) & (a < 5)
array([[False, False,  True],
       [ True, False, False]])

4.5.10. Logical OR

  • Meets first or second condition at the same time

  • np.logical_or(..., ...)

  • (...) | (...)

>>> import numpy as np
>>>
>>>
>>> a = np.array([True, False, False])
>>> b = np.array([True, True, False])
>>>
>>> np.logical_or(a, b)
array([ True,  True, False])
>>>
>>> a | b
array([ True,  True, False])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.logical_or(a < 2, a > 4)
array([[ True, False, False],
       [False,  True,  True]])
>>>
>>> (a < 2) | (a > 4)
array([[ True, False, False],
       [False,  True,  True]])

4.5.11. Logical XOR

  • Meets first or second condition, but not both at the same time

  • np.logical_xor(..., ...)

  • (...) ^ (...)

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.logical_xor(a < 2, a > 4)
array([[ True, False, False],
       [False,  True,  True]])
>>>
>>> (a < 2) ^ (a > 4)
array([[ True, False, False],
       [False,  True,  True]])

4.5.12. Good Practices

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>>
>>> (a < 2) & (a > 4) | (a == 3)
array([[False, False,  True],
       [False, False, False]])
>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> lower = (a > 2)
>>> upper = (a < 6)
>>> nine = (a == 9)
>>> range = lower & upper
>>>
>>> lower & upper
array([[False, False,  True],
       [ True,  True, False],
       [False, False, False]])
>>>
>>> range | nine
array([[False, False,  True],
       [ True,  True, False],
       [False, False,  True]])
>>>
>>> lower & upper | nine
array([[False, False,  True],
       [ True,  True, False],
       [False, False,  True]])

4.5.13. Assignments

Code 4.58. Solution
"""
* Assignment: Numpy Logic Even
* Complexity: easy
* Lines of code: 3 lines
* Time: 5 min

English:
    1. Set random seed to zero
    3. Check for even numbers of `DATA` which are less than 50 and save result to `result`
    4. Check if all `result` matches this condition, result assing to `result_all`
    5. Check if any `result` matches this condition, result assign to `result_any`
    6. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    3. Sprawdź parzyste elementy `DATA`, które są mniejsze od 50 i wynik zapisz do `result`
    4. Sprawdź czy wszystkie `result` spełniają ten warunek, wynik zapisz do `result_all`
    5. Sprawdź czy jakakolwiek `result` spełnia ten warunek, wynik zapisz do `result_any`
    6. Uruchom doctesty - wszystkie muszą się powieść

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

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

    >>> result
    array([ True, False, False, False, False, False, False, False,  True])

    >>> result_all
    False

    >>> result_any
    True
"""

import numpy as np
np.random.seed(0)

DATA = np.random.randint(0, 100, size=9)

result = ...
result_all = ...
result_any = ...


Code 4.59. Solution
"""
* Assignment: Numpy Logic Isin
* Complexity: easy
* Lines of code: 3 lines
* Time: 5 min

English:
    1. Set random seed to zero
    2. Generate `a: np.ndarray` of 50 random integers from 0 to 100 (exclusive)
    3. Generate `b: np.ndarray` with sequential powers of 2 and exponential from 0 to 6 (inclusive)
    4. Check which elements from `a` are present in `b`
    5. Result assign to `result`
    6. Run doctests - all must succeed

Polish:
    1. Ustaw ziarno losowości na zero
    2. Wygeneruj `a: np.ndarray` z 50 losowymi liczbami całkowitymi od 0 do 100 (rozłącznie)
    3. Wygeneruj `b: np.ndarray` z kolejnymi potęgami liczby 2, wykładnik od 0 do 6 (włącznie)
    4. Sprawdź, które elementy z `a` są obecne w `b`
    5. Wynik przypisz do `result`
    6. Uruchom doctesty - wszystkie muszą się powieść

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

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

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

import numpy as np
np.random.seed(0)


result = ...