numpy基础用法

  1. 1. 基本类型
    1. 1.1 ndarray
    2. 1.2 matrix
  2. 2. 初期化
    1. 2.1 array
    2. 2.2 zeros
    3. 2.3 ones
    4. 2.4 empty
    5. 2.5 arange
    6. 2.6 linspace

numpy基础用法

import numpy as np
np.__version__
'1.19.1'

1. 基本类型

1.1 ndarray

a = np.array([1, 2, 3, 4, 5, 6])
print(type(a), a)
<class 'numpy.ndarray'> [1 2 3 4 5 6]

1.2 matrix

b = np.mat(a)
print(type(b), b)
<class 'numpy.matrix'> [[1 2 3 4 5 6]]

2. 初期化

2.1 array

n1array = np.array([1, 2, 3, 4, 5, 6]) # 一维
print(n1array.shape, n1array)
(6,) [1 2 3 4 5 6]
n2array = np.array([[1, 2, 3], [4, 5, 6]]) # 二维
print(n2array.shape, n2array)
(2, 3) [[1 2 3]
 [4 5 6]]

2.2 zeros

n1zeros = np.zeros(6) # 一维
print(n1zeros.shape, n1zeros)
(6,) [0. 0. 0. 0. 0. 0.]
n2zeros = np.zeros((2,3)) # 二维
print(n2zeros.shape, n2zeros)
(2, 3) [[0. 0. 0.]
 [0. 0. 0.]]

2.3 ones

n1ones = np.ones(6) # 一维
print(n1ones.shape, n1ones)
(6,) [1. 1. 1. 1. 1. 1.]
n2ones = np.ones((2,3)) # 二维
print(n2ones.shape, n2ones)
(2, 3) [[1. 1. 1.]
 [1. 1. 1.]]

2.4 empty

n1empty = np.empty(6) # 一维
print(n1empty.shape, n1empty)
(6,) [0. 0. 0. 0. 0. 0.]
n2empty = np.empty((2,3)) # 二维
print(n2empty.shape, n2empty)
(2, 3) [[0. 0. 0.]
 [0. 0. 0.]]

2.5 arange

n1arange = np.arange(6) # 一维
print(n1arange.shape, n1arange)
(6,) [0 1 2 3 4 5]
n2arange = np.arange(6).reshape(2,3) # 二维
print(n2arange.shape, n2arange)
(2, 3) [[0 1 2]
 [3 4 5]]

2.6 linspace

n1linspace = np.linspace(0, 10, num=6) # 一维
print(n1linspace.shape, n1linspace)
(6,) [ 0.  2.  4.  6.  8. 10.]
n2linspace = np.linspace(0, 10, num=6).reshape(2,3) # 二维
print(n2linspace.shape, n2linspace)
(2, 3) [[ 0.  2.  4.]
 [ 6.  8. 10.]]

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