numpy常用用法总结

numpy 简介

各种用法介绍

首先是numpy中的数据类型，ndarray类型，和标准库中的array.array并不一样。

ndarray.ndim
the number of axes (dimensions) of the array. In the Python world, the number of dimensions is referred to as rank.

ndarray.shape
the dimensions of the array. This is a tuple of integers indicating the size of the array in each dimension. For a matrix with n rows and m columns, shape will be (n,m). The length of the shape tuple is therefore the rank, or number of dimensions, ndim.

ndarray.size
the total number of elements of the array. This is equal to the product of the elements of shape.

ndarray.dtype
an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples.

ndarray.itemsize
the size in bytes of each element of the array. For example, an array of elements of type float64 has itemsize 8 (=64/8), while one of type complex32 has itemsize 4 (=32/8). It is equivalent to ndarray.dtype.itemsize.

ndarray.data
the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities.

>>> import numpy as np
>>> a = np.array([2,3,4])
>>> a
array([2, 3, 4])
>>> a.dtype
dtype('int64')
>>> b = np.array([1.2, 3.5, 5.1])
>>> b.dtype
dtype('float64')

二维的数组

>>> b = np.array([(1.5,2,3), (4,5,6)])
>>> b
array([[ 1.5,  2. ,  3. ],
       [ 4. ,  5. ,  6. ]])

创建时指定类型

>>> c = np.array( [ [1,2], [3,4] ], dtype=complex )
>>> c
array([[ 1.+0.j,  2.+0.j],
       [ 3.+0.j,  4.+0.j]])

创建一些特殊的矩阵

>>> np.zeros( (3,4) )
array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])
>>> np.ones( (2,3,4), dtype=np.int16 )                # dtype can also be specified
array([[[ 1, 1, 1, 1],
        [ 1, 1, 1, 1],
        [ 1, 1, 1, 1]],
       [[ 1, 1, 1, 1],
        [ 1, 1, 1, 1],
        [ 1, 1, 1, 1]]], dtype=int16)
>>> np.empty( (2,3) )                                 # uninitialized, output may vary
array([[  3.73603959e-262,   6.02658058e-154,   6.55490914e-260],
       [  5.30498948e-313,   3.14673309e-307,   1.00000000e+000]])

[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:32d1db30-2cc8-4e8d-b79d-5ccdab2e14a5

>>> np.arange( 10, 30, 5 )
array([10, 15, 20, 25])
>>> np.arange( 0, 2, 0.3 )                 # it accepts float arguments
array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8])

>>> from numpy import pi
>>> np.linspace( 0, 2, 9 )                 # 9 numbers from 0 to 2
array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ,  1.25,  1.5 ,  1.75,  2.  ])
>>> x = np.linspace( 0, 2*pi, 100 )        # useful to evaluate function at lots of points
>>> f = np.sin(x)

[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:a73ed083-1db5-4253-982c-ffe57eca4893

>>> a = np.array( [20,30,40,50] )
>>> b = np.arange( 4 )
>>> b
array([0, 1, 2, 3])
>>> c = a-b
>>> c
array([20, 29, 38, 47])
>>> b**2
array([0, 1, 4, 9])
>>> 10*np.sin(a)
array([ 9.12945251, -9.88031624,  7.4511316 , -2.62374854])
>>> a

矩阵运算
matlab中有. ,./等等
但是在numpy中，如果使用+，-，×，/优先执行的是各个点之间的加减乘除法
[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:d168da2f-1820-4c9f-ad00-e32c690c9f84

>>> import numpy as np
>>> A = np.arange(10,20)
>>> B = np.arange(20,30)
>>> A + B
array([30, 32, 34, 36, 38, 40, 42, 44, 46, 48])
>>> A * B
array([200, 231, 264, 299, 336, 375, 416, 459, 504, 551])
>>> A / B
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
>>> B / A
array([2, 1, 1, 1, 1, 1, 1, 1, 1, 1])

[TencentCloudSDKException] code:FailedOperation.ServiceIsolate message:service is stopped due to arrears, please recharge your account in Tencent Cloud requestId:364f9d2f-e8f8-4d34-a6a1-dfb042d11ce6

>>> A = np.array([1,1,1,1])
>>> B = np.array([2,2,2,2])
>>> A.reshape(2,2)
array([[1, 1],
       [1, 1]])
>>> B.reshape(2,2)
array([[2, 2],
       [2, 2]])
>>> A * B
array([2, 2, 2, 2])
>>> np.dot(A,B)
8
>>> A.dot(B)
8

一些常用的全局函数

>>> B = np.arange(3)
>>> B
array([0, 1, 2])
>>> np.exp(B)
array([ 1.        ,  2.71828183,  7.3890561 ])
>>> np.sqrt(B)
array([ 0.        ,  1.        ,  1.41421356])
>>> C = np.array([2., -1., 4.])
>>> np.add(B, C)
array([ 2.,  0.,  6.])

>>> a = np.arange(10)**3
>>> a
array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729])
>>> a[2]
8
>>> a[2:5]
array([ 8, 27, 64])
>>> a[:6:2] = -1000    # equivalent to a[0:6:2] = -1000; from start to position 6, exclusive, set every 2nd element to -1000
>>> a
array([-1000,     1, -1000,    27, -1000,   125,   216,   343,   512,   729])
>>> a[ : :-1]                                 # reversed a
array([  729,   512,   343,   216,   125, -1000,    27, -1000,     1, -1000])
>>> for i in a:
...     print(i**(1/3.))
...

nan
1.0
nan
3.0
nan
5.0
6.0
7.0
8.0
9.0

矩阵的遍历

>>> import numpy as np
>>> b = np.arange(16).reshape(4, 4)
>>> for row in b:
...  print(row)
...

[0 1 2 3]
[4 5 6 7]
[ 8  9 10 11]
[12 13 14 15]
>>> for node in b.flat:
...  print(node)
...

0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

改变矩阵形状–reshape

>>> a = np.floor(10 * np.random.random((3,4)))
>>> a
array([[ 6.,  5.,  1.,  5.],
       [ 5.,  5.,  8.,  9.],
       [ 5.,  5.,  9.,  7.]])
>>> a.ravel()
array([ 6.,  5.,  1.,  5.,  5.,  5.,  8.,  9.,  5.,  5.,  9.,  7.])
>>> a
array([[ 6.,  5.,  1.,  5.],
       [ 5.,  5.,  8.,  9.],
       [ 5.,  5.,  9.,  7.]])

resize和reshape的区别
resize会改变原来的矩阵，reshape并不会

>>> a
array([[ 6.,  5.,  1.,  5.],
       [ 5.,  5.,  8.,  9.],
       [ 5.,  5.,  9.,  7.]])
>>> a.reshape(2,-1)
array([[ 6.,  5.,  1.,  5.,  5.,  5.],
       [ 8.,  9.,  5.,  5.,  9.,  7.]])
>>> a
array([[ 6.,  5.,  1.,  5.],
       [ 5.,  5.,  8.,  9.],
       [ 5.,  5.,  9.,  7.]])
>>> a.resize(2,6)
>>> a
array([[ 6.,  5.,  1.,  5.,  5.,  5.],
       [ 8.,  9.,  5.,  5.,  9.,  7.]])

矩阵的合并

>>> a = np.floor(10*np.random.random((2,2)))
>>> a
array([[ 8.,  8.],
       [ 0.,  0.]])
>>> b = np.floor(10*np.random.random((2,2)))
>>> b
array([[ 1.,  8.],
       [ 0.,  4.]])
>>> np.vstack((a,b))
array([[ 8.,  8.],
       [ 0.,  0.],
       [ 1.,  8.],
       [ 0.,  4.]])
>>> np.hstack((a,b))
array([[ 8.,  8.,  1.,  8.],
       [ 0.,  0.,  0.,  4.]])

Original: https://www.cnblogs.com/0x12345678/p/6179771.html
Author: Hackergin
Title: numpy常用用法总结