3.3.1. SetUp

3.3.2. 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]])

3.3.3. 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.]])

3.3.4. 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]])

3.3.5. 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.]])

3.3.6. 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.]])

3.3.7. Empty Like

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

3.3.8. Full

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

3.3.9. 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]])

3.3.10. 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]])

3.3.11. 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.]])

3.3.12. 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)

3.3.13. References

3.3.14. Assignments

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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

# %% Data

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

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

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

# %% Data

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

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

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

# %% Data

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

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

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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)
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

# %% Data

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

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

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

# %% Data

# %% Result
result = ...


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

# %% 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

# %% 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

# %% Doctests
"""
>>> import sys; sys.tracebacklimit = 0

>>> assert sys.version_info >= (3, 9), \
'Python has an is invalid version; expected: `3.9` or newer.'

>>> assert 'result' in globals(), \
'Variable `result` is not defined; assign result of your program to it.'

>>> assert result is not Ellipsis, \
'Variable `result` has an invalid value; assign result of your program to it.'

>>> assert type(result) is np.ndarray, \
'Variable `result` has an 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]])
"""

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

# %% Imports
import numpy as np

# %% Types
result: np.ndarray

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

# %% Result
result = ...