2.1. Array Create

2.1.1. SetUp

>>> import numpy as np

2.1.2. Declare

1-dimensional Array:

>>> 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:

>>> 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]]])

Figure 2.16. Multi layer cake as an analog for n-dim array 1

2.1.3. Stringify

>>> 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]])

2.1.4. Recap

>>> a = np.array([1, 2, 3])
>>> b = np.array(range(0, 10))

2.1.5. References

1

https://i.ytimg.com/vi/iCOhz07Ng6g/maxresdefault.jpg

2.1.6. Assignments

Code 2.25. Solution

"""
* Assignment: Numpy Create Arange
* Complexity: easy
* Lines of code: 1 lines
* Time: 3 min

English:
    1. Create `result: np.ndarray` with even numbers from 0 to 100 (without 100)
    2. Numbers must be `float` type
    3. Run doctests - all must succeed

Polish:
    1. Stwórz `result: np.ndarray` z liczbami parzystymi od 0 do 100 (bez 100)
    2. Liczby muszą być typu `float`
    3. 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([ 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.])
"""

import numpy as np


result = ...