2.6. Array Slice¶
2.6.1. Recap¶
import numpy as np
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
data[start:stop:step]
# start = 0 # default: 0
# stop = len(data) # default: len(data)
# step = 1 # default: 1
import numpy as np
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
data[1:5:2]
# start = 1
# stop = 5
# step = 2
data[:5:2]
# start = 0
# stop = 5
# step = 2
data[1::2]
# start = 1
# stop = len(data)
# step = 2
data[1:5]
# start = 1
# stop = 5
# step = 1
data[::2]
# start = 0
# stop = len(data)
# step = 2
data[:]
# start = 0
# stop = len(data)
# step = 1
# a[1:5, 2]
# a[1:5:2, 2:6:3]
2.6.2. Rationale¶
a[ 0 ]
a[ [0,1] ]
a[ [True,False] ]
a[ 0:1 ]
a[ 0:1:2 ]
a[ 0 ] # int
a[ [0,1] ] # list[int]
a[ [True,False] ] # list[bool]
a[ [[True,False], [True, False]] ] # list[list[bool]]
a[ 0:1 ] # slice(start,stop)
a[ 0:1:2 ] # slice(start,stop,step)
a[ 0,1 ] # tuple[int]
a[ (0,1) ] # tuple[int]
a[ [0,1],[2,3] ] # tuple[list[int]]
a[ :,: ] # tuple[slice]
a[ [True,False],[False,True] ] # tuple[list[bool]]
1-dimensional Array:
int
list[int]
list[bool]
list[list[bool]]
slice(start,stop)
slice(start,stop,step)
2-dimensional Array:
tuple[int]
tuple[list[int]]
tuple[slice]
tuple[list[bool]]
2.6.3. 1-dimensional Array¶
1-dimensional Array:
import numpy as np
a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
a[1:5]
# array([2, 3, 4, 5])
a[3:8]
# array([4, 5, 6, 7, 8])
a[0:5]
# array([1, 2, 3, 4, 5])
a[:5]
# array([1, 2, 3, 4, 5])
a[5:9]
# array([6, 7, 8, 9])
a[5:len(a)]
# array([6, 7, 8, 9])
a[5:]
# array([6, 7, 8, 9])
a[-2:]
# array([8, 9])
a[-5:]
# array([5, 6, 7, 8, 9])
a[-6:-2]
# array([4, 5, 6, 7])
a[3:8:2]
# array([4, 6, 8])
a[-8:-3:2]
# array([2, 4, 6])
a[::2]
# array([1, 3, 5, 7, 9])
a[1::2]
# array([2, 4, 6, 8])
a[0:len(a)]
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])
a[0:]
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])
a[:len(a)]
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])
a[:]
# array([1, 2, 3, 4, 5, 6, 7, 8, 9])
2.6.4. 2-dimensional Array¶
Rows:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
a[:]
# array([[1, 2, 3],
# [4, 5, 6],
# [7, 8, 9]])
a[1:]
# array([[4, 5, 6],
# [7, 8, 9]])
a[:1]
# array([[1, 2, 3]])
a[1:3]
# array([[4, 5, 6],
# [7, 8, 9]])
a[::2]
# array([[1, 2, 3],
# [7, 8, 9]])
a[1::2]
# array([[4, 5, 6]])
Columns:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
a[:, 0]
# array([1, 4, 7])
a[:, 1]
# array([2, 5, 8])
a[:, 2]
# array([3, 6, 9])
a[:, -1]
# array([3, 6, 9])
a[:, 0:1]
# array([[1],
# [4],
# [7]])
a[:, 0:2]
# array([[1, 2],
# [4, 5],
# [7, 8]])
a[:, :2]
# array([[1, 2],
# [4, 5],
# [7, 8]])
a[:, ::2]
# array([[1, 3],
# [4, 6],
# [7, 9]])
a[:, 1::2]
# array([[2],
# [5],
# [8]])
Rows and Columns:
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
a[0:1, 0:1]
# array([[1]])
a[0:1, 0:2]
# array([[1, 2]])
a[0:1, 0:3]
# array([[1, 2, 3]])
a[0:2, 0:2]
# array([[1, 2],
# [4, 5]])
a[-1:, -2:]
# array([[8, 9]])
a[::2, ::2]
# array([[1, 3],
# [7, 9]])
a[1::2, 1::2]
# array([[5]])
a[[2,1], ::2]
# array([[7, 9],
# [4, 6]])
2.6.5. Assignments¶
"""
* Assignment: Numpy Slice 1
* Complexity: easy
* Lines of code: 3 lines
* Time: 3 min
English:
1. Use data from "Given" section (see below)
2. Print inner 2x2 elements
3. Run doctests - all must succeed
Polish:
1. Użyj danych z sekcji "Given" (patrz poniżej)
2. Wybierz wewnętrzne 2x2 elementy
3. Uruchom doctesty - wszystkie muszą się powieść
Tests:
>>> import sys; sys.tracebacklimit = 0
>>> type(result) is np.ndarray
True
>>> result
array([[8, 4],
[5, 2]])
"""
# Given
import numpy as np
DATA = np.array([
[2, 8, 1, 5],
[8, 8, 4, 4],
[5, 5, 2, 5],
[1, 0, 6, 0],
])
result = ...
"""
* Assignment: Numpy Slice 2
* Complexity: easy
* Lines of code: 3 lines
* Time: 3 min
English:
1. Use data from "Given" section (see below)
2. Print inner 4x4 elements
3. Inner matrix is exactly in the middle of outer
X. Run doctests - all must succeed
Polish:
1. Użyj danych z sekcji "Given" (patrz poniżej)
2. Wypisz środkowe 4x4 elementy
3. Środkowa macierz jest dokładnie w środku większej
X. Uruchom doctesty - wszystkie muszą się powieść
Tests:
>>> import sys; sys.tracebacklimit = 0
>>> type(result) is np.ndarray
True
>>> result
array([[2, 0, 7, 5],
[1, 2, 9, 1],
[8, 8, 8, 2],
[4, 3, 6, 9]])
"""
# Given
import numpy as np
DATA = np.array([[5, 0, 3, 3, 7, 9, 3, 5, 2, 4, 7, 6, 8, 8, 1, 6],
[7, 7, 8, 1, 5, 9, 8, 9, 4, 3, 0, 3, 5, 0, 2, 3],
[8, 1, 3, 3, 3, 7, 0, 1, 9, 9, 0, 4, 7, 3, 2, 7],
[2, 0, 0, 4, 5, 5, 6, 8, 4, 1, 4, 9, 8, 1, 1, 7],
[9, 9, 3, 6, 7, 2, 0, 3, 5, 9, 4, 4, 6, 4, 4, 3],
[4, 4, 8, 4, 3, 7, 5, 5, 0, 1, 5, 9, 3, 0, 5, 0],
[1, 2, 4, 2, 0, 3, 2, 0, 7, 5, 9, 0, 2, 7, 2, 9],
[2, 3, 3, 2, 3, 4, 1, 2, 9, 1, 4, 6, 8, 2, 3, 0],
[0, 6, 0, 6, 3, 3, 8, 8, 8, 2, 3, 2, 0, 8, 8, 3],
[8, 2, 8, 4, 3, 0, 4, 3, 6, 9, 8, 0, 8, 5, 9, 0],
[9, 6, 5, 3, 1, 8, 0, 4, 9, 6, 5, 7, 8, 8, 9, 2],
[8, 6, 6, 9, 1, 6, 8, 8, 3, 2, 3, 6, 3, 6, 5, 7],
[0, 8, 4, 6, 5, 8, 2, 3, 9, 7, 5, 3, 4, 5, 3, 3],
[7, 9, 9, 9, 7, 3, 2, 3, 9, 7, 7, 5, 1, 2, 2, 8],
[1, 5, 8, 4, 0, 2, 5, 5, 0, 8, 1, 1, 0, 3, 8, 8],
[4, 4, 0, 9, 3, 7, 3, 2, 1, 1, 2, 1, 4, 2, 5, 5]])
result = ...
Figure 2.4. Inner 4x4 elements¶