# 2.3. Array Generate¶

## 2.3.1. SetUp¶

>>> import numpy as np


## 2.3.2. Zeros¶

>>> np.zeros((2, 3))
array([[0., 0., 0.],
[0., 0., 0.]])
>>>
>>> np.zeros(shape=(2, 3))
array([[0., 0., 0.],
[0., 0., 0.]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.zeros_like(a)
array([[0, 0, 0],
[0, 0, 0]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]], float)
>>>
>>> np.zeros_like(a)
array([[0., 0., 0.],
[0., 0., 0.]])


## 2.3.3. Ones¶

>>> np.ones((3, 2))
array([[1., 1.],
[1., 1.],
[1., 1.]])
>>>
>>> np.ones(shape=(3, 2))
array([[1., 1.],
[1., 1.],
[1., 1.]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.ones_like(a)
array([[1, 1, 1],
[1, 1, 1]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]], float)
>>>
>>> np.ones_like(a)
array([[1., 1., 1.],
[1., 1., 1.]])


## 2.3.4. Empty¶

• Garbage from memory

• Will reuse previous if given shape was already created

>>> 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]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.empty((2,3))
array([[1., 2., 3.],
[4., 5., 6.]])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.empty_like(a)
array([[1, 2, 3],
[4, 5, 6]])


## 2.3.5. Full¶

>>> np.full((2, 2), np.inf)
array([[inf, inf],
[inf, inf]])
>>>
>>> np.full((2, 2), 10)
array([[10, 10],
[10, 10]])


## 2.3.6. Identity¶

>>> 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]])


## 2.3.7. Recap¶

>>> a = np.zeros(shape=(2,3))
>>> b = np.zeros_like(a)
>>> c = np.ones(shape=(2,3))
>>> d = np.ones_like(a)
>>> e = np.empty(shape=(2,3))
>>> f = np.empty_like(a)
>>> g = np.full(shape=(2, 2), fill_value=np.nan)
>>> h = np.full_like(a, np.nan)
>>> i = np.identity(4)


## 2.3.9. Assignments¶

"""
* 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 = ...