2.1. Array Create

2.1.1. Declare

Code 2.134. 1-dimensional Array
import numpy as np


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

np.array([1.0, 2.0, 3.0])
# array([1., 2., 3.])

np.array([1.1, 2.2, 3.3])
# array([1.1, 2.2, 3.3])

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

np.array([1, 2, 3], dtype=float)
# array([ 1., 2., 3.])
Code 2.135. 2-dimensional Array
import numpy as np


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

# array([[1, 2, 3],
#        [4, 5, 6],
#        [7, 8, 9]])
Code 2.136. 3-dimensional Array
np.array([[[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]],

          [[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]]])

# array([[[1, 2, 3],
#         [4, 5, 6],
#         [7, 8, 9]],
#
#        [[1, 2, 3],
#         [4, 5, 6],
#         [7, 8, 9]]])
../_images/numpy-create-cake.png

Figure 2.8. Multi layer cake as an analog for n-dim array [CAKE]

2.1.2. Range

Code 2.137. Array from Python range()
import numpy as np


np.array(range(5))
# array([0, 1, 2, 3, 4])

np.array(range(5), float)
# array([ 0., 1., 2., 3., 4.])

np.array(range(5, 10))
# array([5, 6, 7, 8, 9])

np.array(range(5, 10), float)
# array([5., 6., 7., 8., 9.])

np.array(range(5, 10, 2))
# array([5, 7, 9])

np.array(range(5, 10, 2), float)
# array([5., 7., 9.])
Code 2.138. Array from Python comprehension
import numpy as np


np.array([x for x in range(5)])
# array([0, 1, 2, 3, 4])

np.array([x for x in range(5)], float)
# array([ 0., 1., 2., 3., 4.])

np.array([x for x in range(5, 10)])
# array([5, 6, 7, 8, 9])

np.array([x for x in range(5, 10)], float)
# array([5., 6., 7., 8., 9.])

np.array([x for x in range(5, 10, 2)])
# array([5, 7, 9])

np.array([x for x in range(5, 10, 2)], float)
# array([5., 7., 9.])
Code 2.139. Array from np.arange()
import numpy as np


np.arange(5)
# array([0, 1, 2, 3, 4])

np.arange(5, dtype=float)
# array([0., 1., 2., 3., 4.])

np.arange(5.0)
# array([0., 1., 2., 3., 4.])

np.arange(5, 10)
# array([5, 6, 7, 8, 9])

np.arange(5, 10, step=2)
# array([5, 7, 9])

np.arange(start=5, stop=10, step=2)
# array([5, 7, 9])

np.arange(start=5, stop=10, step=2, dtype=float)
# array([5., 7., 9.])

np.arange(0.0, 1.0, 0.1)
# array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])

np.arange(0.0, 1.0, 0.2)
# array([0. , 0.2, 0.4, 0.6, 0.8])

np.arange(0.0, 1.0, 0.3)
# array([0. , 0.3, 0.6, 0.9])

2.1.3. Linspace

  • np.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)

  • Return evenly spaced numbers over a specified interval.

np.linspace(2.0, 3.0, num=5)
# array([2.  , 2.25, 2.5 , 2.75, 3.  ])

np.linspace(2.0, 3.0, num=5, endpoint=False)
# array([2. ,  2.2,  2.4,  2.6,  2.8])

np.linspace(2.0, 3.0, num=5, retstep=True)
# (array([2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)

2.1.4. Zeros

import numpy as np


np.zeros((2, 3))
# array([[0., 0., 0.],
#       [0., 0., 0.]])

np.zeros(shape=(2, 3))
# array([[0., 0., 0.],
#        [0., 0., 0.]])

import numpy as np


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

np.zeros_like(a)
# array([[0, 0, 0],
#        [0, 0, 0]])

import numpy as np


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

np.zeros_like(a)
# array([[0., 0., 0.],
#        [0., 0., 0.]])

2.1.5. Ones

import numpy as np


np.ones((3, 2))
# array([[1., 1.],
#        [1., 1.],
#        [1., 1.]])

np.ones(shape=(3, 2))
# array([[1., 1.],
#        [1., 1.],
#        [1., 1.]])

import numpy as np


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

np.ones_like(a)
# array([[1, 1, 1],
#        [1, 1, 1]])

import numpy as np


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

np.ones_like(a)
# array([[1., 1., 1.],
#        [1., 1., 1.]])

2.1.6. Empty

  • Garbage from memory

  • Will reuse previous if given shape was already created

import numpy as np


np.empty((3,4))
# array([[ 2.31584178e+077,  1.29073692e-231,  2.96439388e-323, 0.00000000e+000],
#       [-2.32034891e+077,  2.68678047e+154,  2.18018101e-314, 2.18022275e-314],
#       [ 0.00000000e+000,  2.18023445e-314,  1.38338381e-322, 9.03690495e-309]])

import numpy as np


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

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

import numpy as np


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

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

2.1.7. Full

import numpy as np


np.full((2, 2), np.inf)
# array([[inf, inf],
#        [inf, inf]])

np.full((2, 2), 10)
# array([[10, 10],
#        [10, 10]])

2.1.8. Identity

import numpy as np


np.identity(2)
# array([[1., 0.],
#        [0., 1.]])

np.identity(3)
# array([[1., 0., 0.],
#        [0., 1., 0.],
#        [0., 0., 1.]])

np.identity(4, int)
# array([[1, 0, 0, 0],
#        [0, 1, 0, 0],
#        [0, 0, 1, 0],
#        [0, 0, 0, 1]])

2.1.9. Stringify

import numpy as np


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

str(a)
# '[[1 2 3]\n [4 5 6]\n [7 8 9]]'

print(a)
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]

repr(a)
# 'array([[1, 2, 3],\n       [4, 5, 6],\n       [7, 8, 9]])'

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

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

2.1.10. Assignments

2.1.10.1. Numpy Create Arange

  • Assignment: Numpy Create Arange

  • Last update: 2020-10-01

  • Complexity level: easy

  • Lines of code to write: 1 lines

  • Estimated time of completion: 3 min

  • Filename: solution/numpy_create_arange.py

English:

  1. Create a: np.ndarray with even numbers from 0 to 100 (without 100)

  2. Numbers must be float type

Polish:

  1. Stwórz a: np.ndarray z liczbami parzystymi od 0 do 100 (bez 100)

  2. Liczby muszą być typu float