2.1. Array Create¶
2.1.1. SetUp¶
>>> import numpy as np
2.1.2. Example¶
ndarray- n-dimensional array
>>> a = np.array([1, 2, 3])
>>>
>>> type(a)
<class 'numpy.ndarray'>
2.1.3. From List¶
>>> data = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>>
>>> np.array(data)
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
2.1.4. From Range¶
data = range(0, 10) np.array(data) array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
data range(0, 10) list(data) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
2.1.5. 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 2.17. Multi layer cake as an analog for n-dim array [1]¶
2.1.6. 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]])
2.1.7. Performance¶
Python 3.11.4
Pure Python:
>>>
... %%timeit -n 1000 -r 1000
... data = range(0, 10)
... result = list(data)
279 ns ± 102 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>>
... %%timeit -n 1000 -r 1000
... result = [x for x in range(0, 10)]
520 ns ± 201 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
Python + Numpy:
>>>
... %%timeit -n 1000 -r 1000
... data = range(0, 10)
... result = np.array(data)
2.34 µs ± 249 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
>>>
... %%timeit -n 1000 -r 1000
... result = np.array(range(0, 10))
2.46 µs ± 359 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
Pure Numpy:
>>>
... %%timeit -n 1000 -r 1000
... result = np.arange(0, 10)
559 ns ± 189 ns per loop (mean ± std. dev. of 1000 runs, 1,000 loops each)
2.1.8. Recap¶
>>> a = np.array([1, 2, 3])
>>> b = np.array(range(0, 10))
2.1.9. References¶
2.1.10. 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 number of elements 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 liczba elementów 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 number of elements 11
# use: `np.linspace()`
# type: np.ndarray
result = ...