3.5. Array Logic¶
3.5.1. 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
[1, 2] in a
# False
[3, 4] in a
# False
3.5.2. 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]])
3.5.3. 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]])
3.5.4. 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])
3.5.5. Any¶
import numpy as np
a = np.array([True, False, False])
# 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])
3.5.6. 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])
3.5.7. 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]])
3.5.8. 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]])
3.5.9. 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]])
3.5.10. 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]])
3.5.11. Readability Counts¶
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]])
3.5.12. 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`
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`
>>> type(result) is np.ndarray
True
>>> result
array([ True, False, False, False, False, False, False, False, True])
>>> result_all
False
>>> result_any
True
"""
# Given
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`
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`
Tests:
>>> type(result) is np.ndarray
True
>>> 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])
"""
# Given
import numpy as np
np.random.seed(0)
result = ...