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¶
"""
* 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 = ...
"""
* 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 = ...