# 2.3. Array Generate¶

## 2.3.1. SetUp¶

>>> import numpy as np


## 2.3.2. Zeros¶

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

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


## 2.3.3. Zeros Like¶

>>> 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]], dtype='float')
>>>
>>> np.zeros_like(a)
array([[0., 0., 0.],
[0., 0., 0.]])


## 2.3.4. Ones¶

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

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


## 2.3.5. Ones Like¶

>>> 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]], dtype='float')
>>>
>>> np.ones_like(a)
array([[1., 1., 1.],
[1., 1., 1.]])


## 2.3.6. Empty¶

• Garbage from memory

• Will reuse previous if given shape was already created

>>> np.empty(shape=(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]])


Will reuse previous if given shape was already created:

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


## 2.3.7. Empty Like¶

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


## 2.3.8. Full¶

>>> np.full(shape=(2, 3), fill_value=2)
array([[2, 2, 2],
[2, 2, 2]])


## 2.3.9. Full Like¶

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> np.full_like(a, fill_value=2.0)
array([[2, 2, 2],
[2, 2, 2]])


## 2.3.10. 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, dtype='int')
array([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]])


## 2.3.11. Identity Like¶

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


## 2.3.12. 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)


## 2.3.14. Assignments¶

"""
* Assignment: Numpy Create Array
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: do not change, leave default
b. values: from 0 to 10 (without 10)
c. use: np.array()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: nie zmieniaj, pozostaw domyślny
b. wartości: od 0 do 10 (bez 10)
c. użyj: np.array()
2. 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, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""

import numpy as np

# dtype: do not change, leave default
# values: from 0 to 10 (without 10)
# use: np.array()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create ArrayDtype
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: float
b. values: from 0 to 10 (without 10)
c. use: np.array()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: float
b. wartości: od 0 do 10 (bez 10)
c. użyj: np.array()
2. 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., 1., 2., 3., 4., 5., 6., 7., 8., 9.])
"""

import numpy as np

# dtype: float
# values: from 0 to 10 (without 10)
# use: np.array()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create Arange
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: do not change, leave default
b. values: from 0 to 10 (without 10)
c. use: np.arange()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: nie zmieniaj, pozostaw domyślny
b. wartości: od 0 do 10 (bez 10)
c. użyj: np.arange()
2. 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, 1, 2, 3, 4, 5, 6, 7, 8, 9])
"""

import numpy as np

# dtype: do not change, leave default
# values: from 0 to 10 (without 10)
# use: np.arange()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create ArangeStep
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: do not change, leave default
b. values: from 0 to 10 step 2 (even numbers)
c. use: np.arange()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: nie zmieniaj, pozostaw domyślny
b. wartości: od 0 do 10 krok 2 (liczby parzyste)
c. użyj: np.arange()
2. 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])
"""

import numpy as np

# dtype: do not change, leave default
# values: from 0 to 10 step 2 (even numbers)
# use: np.arange()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create ArangeDtype
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: float
b. values: from 0 to 10 step 2 (even numbers)
c. use: np.arange()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: float
b. wartości: od 0 do 10 krok 2 (liczby parzyste)
c. użyj: np.arange()
2. 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.])
"""

import numpy as np

# dtype: float
# values: from 0 to 10 step 2 (even numbers)
# use: np.arange()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create Linspace
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: do not change, leave default
a. values: from 0 to 10 (without 10)
b. use: np.linspace()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: nie zmieniaj, pozostaw domyślny
a. wartości: od 0 do 10 (bez 10)
b. użyj: np.linspace()
2. 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.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,
1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,
2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,
3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,
4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,
5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,
6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,
7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,
8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,
9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])
"""

import numpy as np

# dtype: do not change, leave default
# values: from 0 to 10 (without 10)
# use: np.linspace()
# type: np.ndarray
result = ...


"""
* Assignment: Numpy Create LinspaceNum
* Complexity: easy
* Lines of code: 1 lines
* Time: 2 min

English:
1. Define result: np.ndarray:
a. dtype: do not change, leave default
b. values: from 0 to 10 steps 11
c. use: np.linspace()
2. Run doctests - all must succeed

Polish:
1. Zdefiniuj result: np.ndarray:
a. dtype: nie zmieniaj, pozostaw domyślny
b. wartości: od 0 do 10 krków 11
c. użyj: np.linspace()
2. 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.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])
"""

import numpy as np

# dtype: do not change, leave default
# values: from 0 to 10 steps 11
# use: np.linspace()
# type: np.ndarray
result = ...