01.Numpy数组的基本应用

>>> import numpy as np

创建一维数组

>>> x = np.arange(10)

>>> x

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

创建二维数组

>>> X = np.arange(10).reshape(2, 5)

>>> X

array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])

查看数组为维度

>>> x.ndim

1

>>> X.ndim

2

查看数组的形状

>>> X.shape

(2, 5)

>>> X

array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])

>>> X[0]

array([0, 1, 2, 3, 4])

>>> X[1,1]

6

>>> X[0:4]

array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])

>>> X[0:1]

array([[0, 1, 2, 3, 4]])

>>> X[0:2]

array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])

>>> X[:2, :2]

array([[0, 1],

       [5, 6]])

>>> X[:, 1]

array([1, 6])

>>> X[1, :]

array([5, 6, 7, 8, 9])

创建子数组

>>> subX = X[:2, :2]

>>> subX

array([[0, 1],

       [5, 6]])

子数组修改

>>> subX[0, 0] = 100

>>> subX

array([[100,   1],

       [  5,   6]])

>>> X

array([[100,   1,   2,   3,   4],

       [  5,   6,   7,   8,   9]])

如何使子数组的修改不影响原数组

>>> subX = X[:2, :2].copy()

>>> subX

array([[100,   1],

       [  5,   6]])

>>> subX[0, 1] = 200

>>> subX

array([[100, 200],

       [  5,   6]])

>>> X

array([[100,   1,   2,   3,   4],

       [  5,   6,   7,   8,   9]])

>>> x

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> x.reshape(2, 5)

array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])

>>> x.reshape(5, 2)

array([[0, 1],

       [2, 3],

       [4, 5],

       [6, 7],

       [8, 9]])

>>> A = x.reshape(5, 2)

>>> A

array([[0, 1],

       [2, 3],

       [4, 5],

       [6, 7],

       [8, 9]])

>>> x.reshape(10, -1)

array([[0],

       [1],

       [2],

       [3],

       [4],

       [5],

       [6],

       [7],

       [8],

       [9]])

>>> x.reshape(-1, 10)

array([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]])

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

>>> b = np.array([4,5,6])

>>> a,b

(array([1, 2, 3]), array([4, 5, 6]))

>>> np.concatenate([a,b])

array([1, 2, 3, 4, 5, 6])

>>> c = np.array([7,8,9])

>>> np.concatenate([a,b,c])

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

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

>>> np.concatenate([A, A])

array([[1, 2, 3],

       [4, 5, 6],

       [1, 2, 3],

       [4, 5, 6]])

>>> np.concatenate([A, A], axis=0)

array([[1, 2, 3],

       [4, 5, 6],

       [1, 2, 3],

       [4, 5, 6]])

>>> np.concatenate([A, A], axis=1)

array([[1, 2, 3, 1, 2, 3],

       [4, 5, 6, 4, 5, 6]])

不能合并两个维度不同的数组

>>> np.concatenate([A, a])

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "<__array_function__ internals>", line 5, in concatenate

ValueError: all the input arrays must have same number of dimensions, but the array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s)

如何忽略维度问题

>>> np.concatenate([A, a.reshape(1, -1)])

array([[1, 2, 3],

       [4, 5, 6],

       [1, 2, 3]])

>>> A,a

(array([[1, 2, 3],

       [4, 5, 6]]), array([1, 2, 3]))

>>> A.shape, a.shape

((2, 3), (3,))

>>> np.vstack([A, a])

array([[1, 2, 3],

       [4, 5, 6],

       [1, 2, 3]])

>>> a = np.array([[6],[6]])

>>> a

array([[6],

       [6]])

>>> np.hstack([A, a])

array([[1, 2, 3, 6],

       [4, 5, 6, 6]])

>>> x

array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

>>> x1,x2,x3 = np.split(x, [3,7])

>>> x1,x2,x3

(array([0, 1, 2]), array([3, 4, 5, 6]), array([7, 8, 9]))

>>> A = np.arange(16).reshape(4,4)

>>> A

array([[ 0,  1,  2,  3],

       [ 4,  5,  6,  7],

       [ 8,  9, 10, 11],

       [12, 13, 14, 15]])

>>> A1,A2 = np.split(A, [2])

>>> A1,A2

(array([[0, 1, 2, 3],

       [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],

       [12, 13, 14, 15]]))

>>> A1,A2 = np.split(A,[2],axis=1)

>>> A1,A2

(array([[ 0,  1],

       [ 4,  5],

       [ 8,  9],

       [12, 13]]), array([[ 2,  3],

       [ 6,  7],

       [10, 11],

       [14, 15]]))

>>> A1, A2 = np.vsplit(A, [2])

>>> A1,A2

(array([[0, 1, 2, 3],

       [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],

       [12, 13, 14, 15]]))

>>> A1,A2 = np.hsplit(A,[2])

>>> A1,A2

(array([[ 0,  1],

       [ 4,  5],

       [ 8,  9],

       [12, 13]]), array([[ 2,  3],

       [ 6,  7],

       [10, 11],

       [14, 15]]))