# 4.1. Array Getitem¶

• int

• list[int]

• list[bool]

>>> import numpy as np
>>>
>>>
>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a[ 0 ]  # int
array([1, 2, 3])
>>>
>>> a[ [0,2] ]  # list[int]
array([[1, 2, 3],
[7, 8, 9]])
>>>
>>> a[ [True,False,True] ]  # list[bool]
array([[1, 2, 3],
[7, 8, 9]])


## 4.1.1. SetUp¶

>>> import numpy as np


## 4.1.2. Index¶

>>> a = np.array([1, 2, 3])
>>>
>>>
>>> a.flat[0]
1
>>> a.flat[1]
2
>>> a.flat[2]
3
>>> a.flat[4]
Traceback (most recent call last):
IndexError: index 4 is out of bounds for axis 0 with size 3


Flat:

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


Multidimensional:

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a[0][0]
1
>>> a[0][1]
2
>>> a[0][2]
3
>>> a[1][0]
4
>>> a[1][1]
5
>>> a[1][2]
6
>>> a[2]
Traceback (most recent call last):
IndexError: index 2 is out of bounds for axis 0 with size 2
>>>
>>> a[-1][-1]
6
>>> a[-3]
Traceback (most recent call last):
IndexError: index -3 is out of bounds for axis 0 with size 2
>>>
>>> a[0,0]
1
>>> a[0,1]
2
>>> a[0,2]
3
>>> a[1,0]
4
>>> a[1,1]
5
>>> a[1,2]
6


## 4.1.3. Selecting items¶

1-dimensional Array:

>>> a = np.array([1, 2, 3])
>>>
>>> a[0]
1
>>> a[1]
2
>>> a[2]
3
>>> a[3]
Traceback (most recent call last):
IndexError: index 3 is out of bounds for axis 0 with size 3
>>> a[-1]
3


2-dimensional Array:

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a[0]
array([1, 2, 3])
>>> a[1]
array([4, 5, 6])
>>> a[2]
Traceback (most recent call last):
IndexError: index 2 is out of bounds for axis 0 with size 2
>>>
>>> a[0,0]
1
>>> a[0,1]
2
>>> a[0,2]
3
>>>
>>> a[1,0]
4
>>> a[1,1]
5
>>> a[1,2]
6
>>>
>>> a[2,0]
Traceback (most recent call last):
IndexError: index 2 is out of bounds for axis 0 with size 2

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a[0]
array([1, 2, 3])
>>> a[1]
array([4, 5, 6])
>>> a[2]
array([7, 8, 9])
>>>
>>> a[0,0]
1
>>> a[0,1]
2
>>> a[0,2]
3
>>>
>>> a[1,0]
4
>>> a[1,1]
5
>>> a[1,2]
6
>>>
>>> a[2,0]
7
>>> a[2,1]
8
>>> a[2,2]
9


3-dimensional Array:

>>> a = np.array([[[ 1,  2,  3],
...                [ 4,  5,  6],
...                [ 5,  6,  7]],
...               [[11, 22, 33],
...                [44, 55, 66],
...                [77, 88, 99]]])
>>>
>>> a[0,0,0]
1
>>> a[0,0,1]
2
>>> a[0,0,2]
3
>>> a[0,0,3]
Traceback (most recent call last):
IndexError: index 3 is out of bounds for axis 2 with size 3
>>>
>>> a[0,1,2]
6
>>> a[0,2,1]
6
>>> a[2,1,0]
Traceback (most recent call last):
IndexError: index 2 is out of bounds for axis 0 with size 2


## 4.1.4. Substituting items¶

1-dimensional Array:

• Will type cast values to np.ndarray.dtype

>>> a = np.array([1, 2, 3])
>>>
>>> a[0] = 99
>>> a
array([99,  2,  3])
>>>
>>> a[-1] = 11
>>> a
array([99,  2, 11])

>>> a = np.array([1, 2, 3], float)
>>>
>>> a[0] = 99.9
>>> a
array([99.9,  2. ,  3. ])
>>>
>>> a[-1] = 11.1
>>> a
array([99.9,  2. , 11.1])

>>> a = np.array([1, 2, 3], int)
>>>
>>> a[0] = 99.9
>>> a
array([99,  2,  3])
>>>
>>> a[-1] = 11.1
>>> a
array([99,  2, 11])


2-dimensional Array:

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6]])
>>>
>>> a[0,0] = 99
>>> a
array([[99,  2,  3],
[ 4,  5,  6]])
>>>
>>> a[1,2] = 11
>>> a
array([[99,  2,  3],
[ 4,  5, 11]])


## 4.1.5. Multi-indexing¶

>>> a = np.array([1, 2, 3])
>>>
>>> a[0], a[2], a[-1]
(1, 3, 3)
>>>
>>> a[[0, 2, -1]]
array([1, 3, 3])
>>>
>>> a[[True, False, True]]
array([1, 3])

>>> a = np.array([[1, 2, 3],
...               [4, 5, 6],
...               [7, 8, 9]])
>>>
>>> a[[0,1]]
array([[1, 2, 3],
[4, 5, 6]])
>>>
>>> a[[0,2,-1]]
array([[1, 2, 3],
[7, 8, 9],
[7, 8, 9]])
>>>
>>> a[[True, False, True]]
array([[1, 2, 3],
[7, 8, 9]])


## 4.1.6. Assignments¶

"""
* Assignment: Numpy Indexing
* Complexity: easy
* Lines of code: 5 lines
* Time: 5 min

English:
1. Create result: np.ndarray
2. Add to result elements from DATA at indexes:
a. row 0, column 2
b. row 2, column 2
c. row 0, column 0
d. row 1, column 0
3. result size must be 2x2
4. result type must be float
5. Run doctests - all must succeed

Polish:
1. Stwórz result: np.ndarray
2. Dodaj do result elementy z DATA o indeksach:
a. wiersz 0, kolumna 2
b. wiersz 2, kolumna 2
c. wiersz 0, kolumna 0
d. wiersz 1, kolumna 0
3. Rozmiar result musi być 2x2
4. Typ result musi być float
5. Uruchom doctesty - wszystkie muszą się powieść

Hints:
* np.zeros(shape, dtype)

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([[3., 9.],
[1., 4.]])
"""

import numpy as np

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

result = ...