2.1. Array Create¶
2.1.1. Declare¶
1-dimensional Array:
import numpy as np
np.array([1, 2, 3])
# array([1, 2, 3])
np.array([1.0, 2.0, 3.0])
# array([1., 2., 3.])
np.array([1.1, 2.2, 3.3])
# array([1.1, 2.2, 3.3])
np.array([1, 2, 3], float)
# array([ 1., 2., 3.])
np.array([1, 2, 3], dtype=float)
# array([ 1., 2., 3.])
2-dimensional Array:
import numpy as np
np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
# array([[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]])
3-dimensional Array:
np.array([[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]],
[[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]])
# array([[[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]],
#
# [[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]]])
2.1.2. Range¶
Array from Python range():
import numpy as np
np.array(range(5))
# array([0, 1, 2, 3, 4])
np.array(range(5), float)
# array([ 0., 1., 2., 3., 4.])
np.array(range(5, 10))
# array([5, 6, 7, 8, 9])
np.array(range(5, 10), float)
# array([5., 6., 7., 8., 9.])
np.array(range(5, 10, 2))
# array([5, 7, 9])
np.array(range(5, 10, 2), float)
# array([5., 7., 9.])
Array from Python comprehension:
import numpy as np
np.array([x for x in range(5)])
# array([0, 1, 2, 3, 4])
np.array([x for x in range(5)], float)
# array([ 0., 1., 2., 3., 4.])
np.array([x for x in range(5, 10)])
# array([5, 6, 7, 8, 9])
np.array([x for x in range(5, 10)], float)
# array([5., 6., 7., 8., 9.])
np.array([x for x in range(5, 10, 2)])
# array([5, 7, 9])
np.array([x for x in range(5, 10, 2)], float)
# array([5., 7., 9.])
Array from np.arange():
import numpy as np
np.arange(5)
# array([0, 1, 2, 3, 4])
np.arange(5, dtype=float)
# array([0., 1., 2., 3., 4.])
np.arange(5.0)
# array([0., 1., 2., 3., 4.])
np.arange(5, 10)
# array([5, 6, 7, 8, 9])
np.arange(5, 10, step=2)
# array([5, 7, 9])
np.arange(start=5, stop=10, step=2)
# array([5, 7, 9])
np.arange(start=5, stop=10, step=2, dtype=float)
# array([5., 7., 9.])
np.arange(0.0, 1.0, 0.1)
# array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
np.arange(0.0, 1.0, 0.2)
# array([0. , 0.2, 0.4, 0.6, 0.8])
np.arange(0.0, 1.0, 0.3)
# array([0. , 0.3, 0.6, 0.9])
2.1.3. Linspace¶
np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)Return evenly spaced numbers over a specified interval.
np.linspace(2.0, 3.0, num=5)
# array([2. , 2.25, 2.5 , 2.75, 3. ])
np.linspace(2.0, 3.0, num=5, endpoint=False)
# array([2. , 2.2, 2.4, 2.6, 2.8])
np.linspace(2.0, 3.0, num=5, retstep=True)
# (array([2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
2.1.4. Zeros¶
import numpy as np
np.zeros((2, 3))
# array([[0., 0., 0.],
# [0., 0., 0.]])
np.zeros(shape=(2, 3))
# array([[0., 0., 0.],
# [0., 0., 0.]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
np.zeros_like(a)
# array([[0, 0, 0],
# [0, 0, 0]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]], float)
np.zeros_like(a)
# array([[0., 0., 0.],
# [0., 0., 0.]])
2.1.5. Ones¶
import numpy as np
np.ones((3, 2))
# array([[1., 1.],
# [1., 1.],
# [1., 1.]])
np.ones(shape=(3, 2))
# array([[1., 1.],
# [1., 1.],
# [1., 1.]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
np.ones_like(a)
# array([[1, 1, 1],
# [1, 1, 1]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]], float)
np.ones_like(a)
# array([[1., 1., 1.],
# [1., 1., 1.]])
2.1.6. Empty¶
Garbage from memory
Will reuse previous if given shape was already created
import numpy as np
np.empty((3,4))
# array([[ 2.31584178e+077, 1.29073692e-231, 2.96439388e-323, 0.00000000e+000],
# [-2.32034891e+077, 2.68678047e+154, 2.18018101e-314, 2.18022275e-314],
# [ 0.00000000e+000, 2.18023445e-314, 1.38338381e-322, 9.03690495e-309]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
np.empty((2,3))
# array([[1., 2., 3.],
# [4., 5., 6.]])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
np.empty_like(a)
# array([[1, 2, 3],
# [4, 5, 6]])
2.1.7. Full¶
import numpy as np
np.full((2, 2), np.inf)
# array([[inf, inf],
# [inf, inf]])
np.full((2, 2), 10)
# array([[10, 10],
# [10, 10]])
2.1.8. Identity¶
import numpy as np
np.identity(2)
# array([[1., 0.],
# [0., 1.]])
np.identity(3)
# array([[1., 0., 0.],
# [0., 1., 0.],
# [0., 0., 1.]])
np.identity(4, int)
# array([[1, 0, 0, 0],
# [0, 1, 0, 0],
# [0, 0, 1, 0],
# [0, 0, 0, 1]])
2.1.9. Stringify¶
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
str(a)
# '[[1 2 3]\n [4 5 6]\n [7 8 9]]'
print(a)
# [[1 2 3]
# [4 5 6]
# [7 8 9]]
repr(a)
# 'array([[1, 2, 3],\n [4, 5, 6],\n [7, 8, 9]])'
a
# array([[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]])
print(repr(a))
# array([[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]])
2.1.10. Performance¶
Results with
%%timeit -n 1_000_000 -r 10
import numpy as np
result = np.arange(0, 100, step=2, dtype=float)
# 756 ns ± 10.3 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
result = np.array(range(0, 100, 2), dtype=float)
# 8.28 µs ± 364 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
result = np.array([x for x in range(0, 100) if x % 2 == 0], dtype=float)
# 9.76 µs ± 324 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
result = np.array([float(x) for x in range(0, 100) if x % 2 == 0])
# 12.7 µs ± 195 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
result = np.array([float(x) for x in range(0, 100, 2)])
# 8.35 µs ± 196 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
result = np.array([x for x in range(0, 100, 2)], dtype=float)
# 5.89 µs ± 77 ns per loop (mean ± std. dev. of 10 runs, 1000000 loops each)
2.1.11. Assignments¶
"""
* Assignment: Numpy Create Arange
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min
English:
1. Create `a: np.ndarray` with even numbers from 0 to 100 (without 100)
2. Numbers must be `float` type
Polish:
1. Stwórz `a: np.ndarray` z liczbami parzystymi od 0 do 100 (bez 100)
2. Liczby muszą być typu `float`
Tests:
>>> type(result) is np.ndarray
True
>>> result
array([ 0., 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24.,
26., 28., 30., 32., 34., 36., 38., 40., 42., 44., 46., 48., 50.,
52., 54., 56., 58., 60., 62., 64., 66., 68., 70., 72., 74., 76.,
78., 80., 82., 84., 86., 88., 90., 92., 94., 96., 98.])
"""
# Given
import numpy as np
result = ...
