import numpy as np
a = [60, 28, 49, 81]
b = [55, 634, 704, 2020]
# create numpy array with list, specifying type as integer
array_single = np.array(a, dtype='i')
array_single
# [60 28 49 81]
# create numpy array with many lists
array_many = np.array([a, b])
array_many
# [[ 60 28 49 81]
# [ 55 634 704 2020]]
# dimension and shape
array_many.ndim # 2
array_many.shape # (2, 4)
# create numpy array with zeros
array_zeros = np.zeros(4)
array_zeros
# [ 0. 0. 0. 0.]
# create 2x3 numpy array with ones
array_ones = np.ones((2, 3))
array_ones
# [[ 1. 1. 1.]
# [ 1. 1. 1.]]
# create numpy array with range from 0 to 50
array_range = np.arange(0, 50)
array_range
# [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
# 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49]
# create numpy array with ten numbers spaced between 0 and 10 (linear)
array_linear = np.linspace(0, 1, 10)
array_linear
# [ 0. 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556
# 0.66666667 0.77777778 0.88888889 1. ]
# create numpy array with ten numbers spaced between 1 and 10 (logarithmic)
array_log = np.logspace(0, 1, 10)
array_log
# [ 1. 1.29154967 1.66810054 2.15443469 2.7825594
# 3.59381366 4.64158883 5.9948425 7.74263683 10. ]
# get second to third value
array_single[1:3] # [28 49]
# reverse order
array_single[::-1] # [81 49 28 60]
# multidimensional slicing (3rd and 4th column, both rows)
array_many[0:2, 2:4]
# [[ 49 81]
# [ 704 2020]]
# array operations
array_range.mean() # 24.5
array_range.sum() # 1225
array_many.min() # 28
array_many.max() # 2020
np.sin(array_many)
# [[-0.30481062 0.27090579 -0.95375265 -0.62988799]
# [-0.99975517 -0.56605794 0.27947339 0.04406199]]
np.multiply(a, b) # [3300 17752 34496 163620]
np.sqrt(b) # [7.41619849 25.17935662 26.53299832 44.94441011]
# minimum value in each column
np.minimum(a, b) # [55 28 49 81]
# sum of axis (columns)
np.sum(array_many, axis=1) # [218 3413]