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)

2.3.13. References

[1]

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

2.3.14. Assignments

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate Zeros
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray`:
#    - dtype: int64
#    - values: 0
#    - shape: 3 rows, 3 columns
#    - use: `np.zeros()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray`:
#    - dtype: int64
#    - wartości: 0
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.zeros()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> result
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])
"""

import numpy as np


# dtype: int64
# values: 0
# shape: 3 rows, 3 columns
# use: `np.zeros()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate ZerosLike
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - values: 0
#    - shape: 3 rows, 3 columns
#    - use: `np.zeros_like()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - wartości: 0
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.zeros_like()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> assert result.dtype == DATA.dtype
>>> assert result.itemsize == DATA.itemsize
>>> assert result.shape == DATA.shape
>>> assert result.strides == DATA.strides

>>> result
array([[0, 0, 0],
       [0, 0, 0],
       [0, 0, 0]])
"""

import numpy as np


DATA = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# dtype: int64
# values: 0
# shape: 3 rows, 3 columns
# use: `np.zeros_like()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate Ones
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray`:
#    - dtype: int64
#    - values: 1
#    - shape: 3 rows, 3 columns
#    - use: `np.ones()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray`:
#    - dtype: int64
#    - wartości: 1
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.ones()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> result
array([[1, 1, 1],
       [1, 1, 1],
       [1, 1, 1]])
"""

import numpy as np


# dtype: int64
# values: 1
# shape: 3 rows, 3 columns
# use: `np.ones()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate OnesLike
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - values: 1
#    - shape: 3 rows, 3 columns
#    - use: `np.ones_like()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - wartości: 1
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.ones_like()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> assert result.dtype == DATA.dtype
>>> assert result.itemsize == DATA.itemsize
>>> assert result.shape == DATA.shape
>>> assert result.strides == DATA.strides

>>> result
array([[1, 1, 1],
       [1, 1, 1],
       [1, 1, 1]])
"""

import numpy as np


DATA = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# dtype: int64
# values: 1
# shape: 3 rows, 3 columns
# use: `np.ones_like()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate Full
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray`:
#    - dtype: int64
#    - values: 2
#    - shape: 3 rows, 3 columns
#    - use: `np.full()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray`:
#    - dtype: int64
#    - wartości: 2
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.full()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> result
array([[2, 2, 2],
       [2, 2, 2],
       [2, 2, 2]])
"""

import numpy as np


# dtype: int64
# values: 2
# shape: 3 rows, 3 columns
# use: `np.full()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate FullLike
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - values: 2
#    - shape: 3 rows, 3 columns
#    - use: `np.full_like()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - wartości: 2
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.full_like()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> assert result.dtype == DATA.dtype
>>> assert result.itemsize == DATA.itemsize
>>> assert result.shape == DATA.shape
>>> assert result.strides == DATA.strides

>>> result
array([[2, 2, 2],
       [2, 2, 2],
       [2, 2, 2]])
"""

import numpy as np


DATA = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# dtype: int64
# values: 2
# shape: 3 rows, 3 columns
# use: `np.full_like()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate Empty
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray`:
#    - dtype: int64
#    - values: leave default
#    - shape: 3 rows, 3 columns
#    - use: `np.empty()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray`:
#    - dtype: int64
#    - wartości: pozostaw domyślne
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.empty()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)
"""

import numpy as np


# dtype: int64
# values: leave default
# shape: 3 rows, 3 columns
# use: `np.empty()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate EmptyLike
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - values: leave default
#    - shape: 3 rows, 3 columns
#    - use: `np.empty_like()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - wartości: pozostaw domyślne
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.empty_like()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> assert result.dtype == DATA.dtype
>>> assert result.itemsize == DATA.itemsize
>>> assert result.shape == DATA.shape
>>> assert result.strides == DATA.strides
"""

import numpy as np


DATA = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# dtype: int64
# values: leave default
# shape: 3 rows, 3 columns
# use: `np.empty_like()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate Identity
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray`:
#    - dtype: int64
#    - values: all zeros with ones across
#    - shape: 3 rows, 3 columns
#    - use: `np.identity()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray`:
#    - dtype: int64
#    - wartości: same zera z jedynkami na skos
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.identity()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> result
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1]])
"""

import numpy as np


# dtype: int64
# values: all zeros with ones across
# shape: 3 rows, 3 columns
# use: `np.identity()`
# type: np.ndarray
result = ...

# %% License
# - Copyright 2025, Matt Harasymczuk <matt@python3.info>
# - This code can be used only for learning by humans
# - This code cannot be used for teaching others
# - This code cannot be used for teaching LLMs and AI algorithms
# - This code cannot be used in commercial or proprietary products
# - This code cannot be distributed in any form
# - This code cannot be changed in any form outside of training course
# - This code cannot have its license changed
# - If you use this code in your product, you must open-source it under GPLv2
# - Exception can be granted only by the author

# %% Run
# - PyCharm: right-click in the editor and `Run Doctest in ...`
# - PyCharm: keyboard shortcut `Control + Shift + F10`
# - Terminal: `python -m doctest -v myfile.py`

# %% About
# - Name: Numpy Generate IdentityLike
# - Difficulty: easy
# - Lines: 1
# - Minutes: 2

# %% English
# 1. Define `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - values: all zeros with ones across
#    - shape: 3 rows, 3 columns
#    - use: `np.identity()`
# 2. Run doctests - all must succeed

# %% Polish
# 1. Zdefiniuj `result: np.ndarray` from `DATA`:
#    - dtype: int64
#    - wartości: same zera z jedynkami na skos
#    - shape: 3 wiersze, 3 kolumny
#    - użyj: `np.identity()`
# 2. Uruchom doctesty - wszystkie muszą się powieść

# %% Tests
"""
>>> import sys; sys.tracebacklimit = 0
>>> assert sys.version_info >= (3, 9), \
'Python 3.9+ required'

>>> assert result is not Ellipsis, \
'Assign result to variable: `result`'
>>> assert type(result) is np.ndarray, \
'Variable `result` has invalid type, expected: np.ndarray'

>>> assert result.dtype == np.dtype('int64')
>>> assert result.itemsize == 8
>>> assert result.shape == (3, 3)
>>> assert result.strides == (24, 8)

>>> assert result.dtype == DATA.dtype
>>> assert result.itemsize == DATA.itemsize
>>> assert result.shape == DATA.shape
>>> assert result.strides == DATA.strides

>>> result
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1]])
"""

import numpy as np


DATA = np.array([[1, 2, 3],
                 [4, 5, 6],
                 [7, 8, 9]])

# dtype: int64
# values: all zeros with ones across
# shape: 3 rows, 3 columns
# use: `np.identity()`
# type: np.ndarray
result = ...