Apply np.array() method to convert a list to a numpy array:
1 import numpy as np 2 mylist = [1, 2, 3] 3 x = np.array(mylist) 4 x
Output: array([1, 2, 3])
Or just pass in a list directly:
y = np.array([4, 5, 6])
y
Output: array([4, 5, 6])
Pass in a list of lists to create a multidimensional array:
m = np.array([[7, 8, 9], [10, 11, 12]])
m
Output:
array([[ 7, 8, 9],
[10, 11, 12]])
arange returns evenly spaced values within a given interval.
n = np.arange(0, 30, 2) # start at 0 count up by 2, stop before 30 n
Output:
array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])
reshape returns an array with the same data with a new shape.
n = n.reshape(3, 5) # reshape array to be 3x5 n
Output:
array([[ 0, 2, 4, 6, 8],
[10, 12, 14, 16, 18],
[20, 22, 24, 26, 28]])
linspace returns evenly spaced numbers over a specified interval.
o = np.linspace(0, 4, 9) # return 9 evenly spaced values from 0 to 4 o
Output:
array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ])
resize changes the shape and size of array in-place.
o.resize(3, 3)
o
Output:
array([[0. , 0.5, 1. ],
[1.5, 2. , 2.5],
[3. , 3.5, 4. ]])
ones returns a new array of given shape and type, filled with ones.
np.ones((3, 2))
Output:
array([[1., 1.],
[1., 1.],
[1., 1.]])
zeros returns a new array of given shape and type, filled with zeros.
np.zeros((2, 3))
Output:
array([[0., 0., 0.],
[0., 0., 0.]])
eye returns a 2-D array with ones on the diagonal and zeros elsewhere.
np.eye(3)
Output:
array([[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]])
diag extracts a diagonal or constructs a diagonal array.
np.diag(y)
Output:
array([[4, 0, 0],
[0, 5, 0],
[0, 0, 6]])
Create an array using repeating list (or see np.tile)
np.array([1, 2, 3] * 3)
Output:
array([1, 2, 3, 1, 2, 3, 1, 2, 3])
Repeat elements of an array using repeat.
np.repeat([1, 2, 3], 3)
Output:
array([1, 1, 1, 2, 2, 2, 3, 3, 3])
The numpy.random subclass provides many methods for random sampling. The following tabels list funtions in the module to generate random numbers.
rand(d0, d1, ..., dn) |
Random values in a given shape. |
randn(d0, d1, ..., dn) |
Return a sample (or samples) from the “standard normal” distribution. |
randint(low[, high, size, dtype]) |
Return random integers from low (inclusive) to high (exclusive). |
random_integers(low[, high, size]) |
Random integers of type np.int between low and high, inclusive. |
random_sample([size]) |
Return random floats in the half-open interval [0.0, 1.0). |
random([size]) |
Return random floats in the half-open interval [0.0, 1.0). |
ranf([size]) |
Return random floats in the half-open interval [0.0, 1.0). |
sample([size]) |
Return random floats in the half-open interval [0.0, 1.0). |
choice(a[, size, replace, p]) |
Generates a random sample from a given 1-D array |
bytes(length) |
Return random bytes. |
Now I will summarize the usage of the first three funtions which I have met frequently.
numpy.random.rand creates an array of the given shape and populate it with random samples from a uniform distribution over [0, 1). Parameters d0, d1, ..., dn define dimentions of returned array.
np.random.rand(2,3)
Output:
array([[0.20544659, 0.23520889, 0.11680902],
[0.56246922, 0.60270525, 0.75224416]])
numpy.random.randn creates an array of the given shape and populate it with random samples from a strandard normal distribution N(0,1). If any of the 
are floats, they are first converted to integers by truncation. A single float randomly sampled from the distribution is returned if no argument is provided.
1 # single random variable 2 print(np.random.randn(),‘\n‘) 3 # N(0,1) 4 print(np.random.randn(2, 4),‘\n‘) 5 # N(3,6.26) 6 print(2.5 * np.random.randn(2, 4) + 3,‘\n‘)
Output:
-1.613647405772221
[[ 1.13147436 0.19641141 -0.62034454 0.61118876]
[ 0.95742223 1.91138142 0.2736291 0.29787331]]
[[ 1.997092 2.6460653 3.2408004 -0.81586404]
[ 0.15120766 1.23676426 6.59249789 -1.04078213]]
numpy. random.randint returns random integers from the “discrete uniform” distribution of the specified dtype in the interval [low, high). If high is None (the default), then results are from [0, low). The specific format is
numpy.random.randint(low, high=None, size=None, dtype=‘l‘)
np.random.seed(10) np.random.randint(100,200,(3,4))
np.random.randint(100,200)
Output:
array([[109, 115, 164, 128],
[189, 193, 129, 108],
[173, 100, 140, 136]])
109
There are another two funtions used for permutations. Both of them can randomly permute an array. The only difference is that shuffle changes the original array but permutation doesn‘t.
shuffle(x) |
Modify a sequence in-place by shuffling its contents. |
permutation(x) |
Randomly permute a sequence, or return a permuted range. |
np.random.permutation([1, 4, 9, 12, 15])
Output: array([ 9, 4, 1, 12, 15])
np.random.permutation(10)
Output: array([3, 7, 4, 6, 8, 2, 1, 5, 0, 9])
Usually, we use the following statements to perform random sampling:
permutation = list(np.random.permutation(m)) #m is the number of samples shuffled_X = X[:, permutation] shuffled_Y = Y[:, permutation].reshape((1,m))
[Python Cookbook] Numpy: Multiple Ways to Create an Array
原文：https://www.cnblogs.com/sherrydatascience/p/10223768.html