# 2.5. Array Getitem¶

## 2.5.1. Rationale¶

• int

• list[int]

• list[bool]

import numpy as np

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

a[ 0 ]              # int
a[ [0,1] ]          # list[int]
a[ [True,False] ]   # list[bool]


## 2.5.2. Index¶

import numpy as np

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:

import numpy as np

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:

import numpy as np

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


## 2.5.3. Selecting items¶

1-dimensional Array:

import numpy as np

a = np.array([1, 2, 3])
# 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:

import numpy as np

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

import numpy as np

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]
# 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]
# 7
a[2,1]
# 8
a[2,2]
# 9


3-dimensional Array:

import numpy as np

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


## 2.5.4. Substituting items¶

1-dimensional Array:

• Will type cast values to np.ndarray.dtype

import numpy as np

a = np.array([1, 2, 3])

a[0] = 99
# array([99,  2,  3])

a[-1] = 11
# array([99,  2,  11])

import numpy as np

a = np.array([1, 2, 3], float)

a[0] = 99.9
# array([99.9,  2.,  3.])

a[-1] = 11.1
# array([99.9,  2.,  11.1])

import numpy as np

a = np.array([1, 2, 3], int)

a[0] = 99.9
# array([99,  2,  3])

a[-1] = 11.1
# array([99,  2,  11])


2-dimensional Array:

import numpy as np

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

a[0,0] = 99
# array([[99,  2,  3],
#        [ 4,  5,  6]])

a[1,2] = 11
# array([[99,  2,  3],
#        [ 4,  5, 11]])


## 2.5.5. Multi-indexing¶

import numpy as np

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])

import numpy as np

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]])


## 2.5.6. 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 = ...