2.12. Array Methods

2.12.1. Copy

import numpy as np


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

a[0] = 99

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

b
# array([99, 2, 3])

c
# array([1, 2, 3])

2.12.2. Put

2.12.3. One dimensional

import numpy as np


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

a.put([0, 2, 5], 99)
a
# array([99,  2, 99,  4,  5, 99])

import numpy as np


a = np.array([1, 2, 3, 4, 5, 6])
b = np.array([99, 88, 77, 66, 55, 44, 33, 22])

a.put([0, 2, 5], b)
a
# array([99,  2, 88,  4,  5, 77])

2.12.4. Two dimensional

  • Equivalent to a.flat[indexes] = value

import numpy as np


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

b = np.array([99, 88, 77, 66, 55, 44, 33, 22])

a.put([0, 2, 5], b)
a
# array([[99,  2, 88],
#        [ 4,  5, 77],
#        [ 7,  8,  9]])

2.12.5. Fill

  • Modifies inplace

Fill all:

import numpy as np


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

a.fill(0)
a
# array([[0, 0, 0],
#        [0, 0, 0],
#        [0, 0, 0]])

Fill slice:

import numpy as np


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

a[:, 0].fill(0)
a
# array([[0, 2, 3],
#        [0, 5, 6],
#        [0, 8, 9]])

Fill NaN (dtype=np.int):

import numpy as np


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

a[:, 0].fill(np.nan)
a
# array([[-9223372036854775808, 2, 3],
#        [-9223372036854775808, 5, 6],
#        [-9223372036854775808, 8, 9]])

Fill NaN (dtype=np.float):

import numpy as np


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

a[:, 0].fill(np.nan)

a
# array([[nan,  2.,  3.],
#        [nan,  5.,  6.],
#        [nan,  8.,  9.]])

2.12.6. Transpose

  • a.transpose() or a.T

  • a.transpose() is preferred

import numpy as np


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

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

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

import numpy as np


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

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

2.12.7. Signum

../_images/numpy-methods-signum.png 
import numpy as np


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

np.sign(a)
# array([[-1, -1,  0],
#        [ 0,  1,  1]])

import numpy as np

# t1 = 230 lux
# t2 = 218 lux
# t3 = 230 lux
# t4 = 2 lux
# t5 = 0 lux
# t6 = 0 lux
# t7 = 10 lux
# t8 = 0 lux

data = np.array([230, 218, 230, 2, 0, 0, 10, 0])
np.sign(data)
# array([1, 1, 1, 1, 0, 0, 1, 0])

data[data<50] = 0
np.sign(data)
# array([1, 1, 1, 0, 0, 0, 0, 0])

2.12.8. Assignments

Code 2.48. Solution
"""
* Assignment: Numpy Methods
* Complexity: easy
* Lines of code: 4 lines
* Time: 5 min

English:
    1. Use data from "Given" section (see below)
    2. Reshape `result` to 3x4
    3. Fill last column with zeros (0)
    4. Transpose `result`
    5. Convert `result` to float
    6. Fill first row with `np.nan`
    7. Print `result`

Polish:
    1. Użyj danych z sekcji "Given" (patrz poniżej)
    2. Zmień kształt na 3x4
    3. Wypełnij ostatnią kolumnę zerami (0)
    4. Transponuj `result`
    5. Przekonwertuj `result` do float
    6. Wypełnij pierwszy wiersz `np.nan`
    7. Wypisz `result`

Tests:
    >>> type(result) is np.ndarray
    True
    >>> result
    array([[nan, nan, nan],
           [47.,  9., 87.],
           [64., 83., 70.],
           [ 0.,  0.,  0.]])
"""


# Given
import numpy as np

DATA = np.array([[44, 47, 64, 67],
                 [67,  9, 83, 21],
                 [36, 87, 70, 88]])


result = ...