3.6. Array Select¶

3.6.1. Unique¶

import numpy as np


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

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

np.unique(a, axis=0)
# array([[1, 2, 3, 1],
#        [1, 4, 5, 6]])

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

3.6.2. Diagonal¶

import numpy as np


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

a.diagonal()
# array([1, 4])

import numpy as np


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

a.diagonal()
# array([1, 5])

import numpy as np


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

a.diagonal()
# array([1, 5, 9])

3.6.3. Nonzero¶

Each element of the tuple contains one of the indices for each nonzero value.
Therefore, the length of each tuple element is the number of nonzeros in the array.
The first element of the tuple is the first index for each of the nonzero values: ([0, 0, 1, 1]).
The second element of the tuple is the second index for each of the nonzero values: ([0, 2, 0, 2]).
Pairs are zipped (first and second tuple):
- 0, 0
- 0, 2
- 1, 0
- 1, 2

import numpy as np


a = np.array([[1, 0, 2],
              [3, 0, 4]])

a.nonzero()
# (array([0, 0, 1, 1]),
#  array([0, 2, 0, 2]))

a[a.nonzero()]
# array([1, 2, 3, 4])

3.6.4. Where¶

3.6.5. Single argument¶

where(boolarray)
indexes of elements

import numpy as np


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

np.where(a != 2)
# (array([0, 2, 3, 4, 5]),)

np.where(a % 2 == 0)
# (array([1, 3, 5]),)

np.where( (a>2) & (a<5) )
# (array([2, 3]),)

import numpy as np


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

np.where(a % 2 == 0)
# (array([0, 0, 1]),
#  array([0, 2, 1]))

np.where( (a>2) & (a<5) )
# (array([0, 1]),
#  array([2, 0]))

3.6.6. Multiple argument¶

where(boolarray, truearray, falsearray):

import numpy as np


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

np.where(a < 5, 'small', 'large')
# array([['small', 'small', 'small'],
#        ['small', 'large', 'large'],
#        ['large', 'large', 'large']], dtype='<U5')

import numpy as np


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

np.where(a % 2 == 0, 'even', 'odd')
# array([['odd', 'even', 'odd'],
#        ['even', 'odd', 'even'],
#        ['odd', 'even', 'odd']], dtype='<U4')

import numpy as np


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

np.where(a % 2 == 0, np.nan, a)
# array([[ 1., nan,  3.],
#        [nan,  5., nan],
#        [ 7., nan,  9.]])

3.6.7. Take¶

import numpy as np


a = np.array([1, 2, 3])
at_index = np.array([0, 0, 1, 2, 2, 1])

a.take(at_index)
# array([1, 1, 2, 3, 3, 2])

import numpy as np


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

at_index = np.array([0, 0, 1])

a.take(at_index, axis=0)
# array([[1, 2, 3],
#        [1, 2, 3],
#        [4, 5, 6]])

a.take(at_index, axis=1)
# array([[1, 1, 2],
#        [4, 4, 5],
#        [7, 7, 8]])

3.6.8. Assignments¶

Code 3.7. Solution¶

"""
* Assignment: Numpy Select Isin
* Complexity: easy
* Lines of code: 6 lines
* Time: 5 min

English:
    1. Set random seed to 0
    2. Generate `a: np.ndarray` of size 50x50
    3. `a` must contains random integers from 0 to 1024 inclusive
    4. Create `result: np.ndarray` with elements selected from `a` which are power of two
    5. Sort `result` in descending order
    6. Print `result`

Polish:
    1. Ustaw ziarno losowości na 0
    2. Wygeneruj `a: np.ndarray` rozmiaru 50x50
    3. `a` musi zawierać losowe liczby całkowite z zakresu od 0 do 1024 włącznie
    4. Stwórz `result: np.ndarray` z elementami wybranymi z `a`, które są potęgami dwójki
    5. Posortuj `result` w kolejności malejącej
    6. Wypisz `result`

Hints:
    * `np.isin(a, b)`
    * `np.flip(a)`

Tests:
    >>> type(result) is np.ndarray
    True
    >>> result
    array([1024, 1024, 1024, 1024, 1024, 1024,  512,  512,  512,  512,  256,
            256,  256,  256,  256,  128,  128,  128,  128,  128,   64,   32,
             32,   32,   32,   32,   16,   16,   16,   16,   16,   16,    8,
              8,    4,    2,    2,    2,    2,    2])
"""


# Given
import numpy as np
np.random.seed(0)


result = ...