python中 numpy_Python中的Numpy

python中 numpy

Python中的Numpy是什么？ (What is Numpy in Python?)

Numpy is an array processing package which provides high-performance multidimensional array object and utilities to work with arrays. It is a basic package for scientific computation with python. It is a linear algebra library and is very important for data science with python since almost all of the libraries in the pyData ecosystem rely on Numpy as one of their main building blocks. It is incredibly fast, as it has bindings to C.

Numpy是一个数组处理程序包，它提供高性能的多维数组对象和实用程序来处理数组。它是使用python进行科学计算的基本软件包。它是一个线性代数库，对于使用python进行数据科学非常重要，因为pyData生态系统中的几乎所有库都依赖Numpy作为其主要构建模块之一。它非常快，因为它与C具有绑定。

Some of the many features, provided by numpy are as always,

numpy提供的许多功能一如既往，

N-dimensional array object

N维数组对象
Broadcasting functions

广播功能
Utilities for integrating with C / C++

与C / C ++集成的实用程序
Useful linear algebra and random number capabilities

有用的线性代数和随机数功能

安装Numpy (Installing Numpy)

1) Using pip

1)使用点子

    pip install numpy

Installing output

安装输出

pip install numpy
Collecting numpy
Downloading https://files.pythonhosted.org/packages/60/9a/a6b3168f2194fb468dcc4cf54c8344d1f514935006c3347ede198e968cb0/numpy-1.17.4-cp37-cp37m-macosx_10_9_x86_64.whl (15.1MB)
100% |████████████████████████████████| 15.1MB 1.3MB/s
Installing collected packages: numpy
Successfully installed numpy-1.17.4

2) Using Anaconda

2)使用水蟒

    conda install numpy

numpy中的数组 (Arrays in Numpy)

Numpy's main object is the homogeneous multidimensional array. Numpy arrays are two types: vectors and matrices. vectors are strictly 1-d arrays and matrices are 2-d.

Numpy的主要对象是齐次多维数组。 numpy数组有两种类型： 向量和矩阵 。向量严格是一维数组，矩阵是二维。

In Numpy dimensions are known as axes. The number of axes is rank. The below examples lists the most important attributes of a ndarray object.

在Numpy中，尺寸称为轴。轴数为等级。以下示例列出了ndarray对象的最重要属性。

Example:

例：

# importing package
import numpy as np
# creating array
arr = np.array([[11,12,13],[14,15,16]])
print("Array is of type {}".format(type(arr)))
print("No. of dimensions {}".format(arr.ndim))
print("shape of array:{}".format(arr.shape))
print("size of array:{}".format(arr.size))
print("type of elements in the array:{}".format(arr.dtype))

Output

输出量

Array is of type <class 'numpy.ndarray'>
No. of dimensions 2
shape of array:(2, 3)
size of array:6
type of elements in the array:int64

创建一个numpy数组 (Creating a numpy array)

Creating a numpy array is possible in multiple ways. For instance, a list or a tuple can be cast to a numpy array using the. array() method (as explained in the above example). The array transforms a sequence of the sequence into 2-d arrays, sequences of sequences into a 3-d array and so on.

创建numpy数组的方式有多种。例如，可以使用将列表或元组强制转换为numpy数组。 array()方法 (如以上示例中所述)。数组将序列的序列转换为2维数组，将序列的序列转换为3维数组，依此类推。

To create sequences of numbers, NumPy provides a function called arange that returns arrays instead of lists.

为了创建数字序列，NumPy提供了一个称为arange的函数，该函数返回数组而不是列表。

Syntax:

句法：

    # returns evenly spaced values within a given interval.
arange([start,] stop [,step], dtype=None)

Example:

例：

x = np.arange(10,30,5)
print(x)
# Ouput: [10 15 20 25]

The function zeros create an array full of zeros, the function ones create an array full of ones, and the function empty creates an array whose initial content is random and depends on the state of the memory. By default, the dtype of the created array is float64.

函数零将创建一个由零组成的数组，函数一个将创建由零组成的数组，函数空将创建一个数组，其初始内容是随机的，并且取决于内存的状态。默认情况下，创建的数组的dtype为float64。

Example:

例：

# importing package
import numpy as np
x = np.zeros((3,4))
print("np.zeros((3,4))...")
print(x)
x = np.ones((3,4))
print("np.ones((3,4))...")
print(x)
x = np.empty((3,4))
print("np.empty((3,4))...")
print(x)
x = np.empty((1,4))
print("np.empty((1,4))...")
print(x)

Output

输出量

np.zeros((3,4))...
[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
np.ones((3,4))...
[[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]]
np.empty((3,4))...
[[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]]
np.empty((1,4))...
[[1.63892563e-316 0.00000000e+000 2.11026305e-312 2.56761491e-312]]

numpy函数 (Numpy functions)

Some more function available with NumPy to create an array are,

NumPy提供了一些更多的函数来创建数组，

1) linspace()

1)linspace()

It returns an evenly spaced numbers over a specified interval.

它在指定的间隔内返回均匀间隔的数字。

Syntax:

句法：

    linspace(start, stop, num=50, endpoint=True, restep=False, dtype=None)

Example:

例：

# importing package
import numpy as np
x = np.linspace(1,3,num=10)
print(x)

Output

输出量

[1.         1.22222222 1.44444444 1.66666667 1.88888889 2.11111111
2.33333333 2.55555556 2.77777778 3.        ]

2) eye()

2)眼睛()

It returns a 2-D array with ones on the diagonal and zeros elsewhere.

它返回一个二维数组，对角线上有一个，其他位置为零。

Syntax:

句法：

    eye(N, M=None, k=0, dtype=<class 'float'>, order='C')

Example:

例：

# importing package
import numpy as np
x = np.eye(4)
print(x)

Output

输出量

[[1. 0. 0. 0.] [0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]

3) random()

3)random()

It creates an array with random numbers

它创建一个带有随机数的数组

Example:

例：

# importing package
import numpy as np
x = np.random.rand(5)
print("np.random.rand(5)...")
print(x)
x = np.random.rand(5,1)
print("np.random.rand(5,1)...")
print(x)
x = np.random.rand(5,1,3)
print("np.random.rand(5,1,3)...")
print(x)
# returns a random number
x = np.random.randn()
print("np.random.randn()...")
print(x)
# returns a 2-D array with random numbers
x = np.random.randn(2,3)
print("np.random.randn(2,3)...")
print(x)
x = np.random.randint(3)
print("np.random.randint(3)...")
print(x)
# returns a random number in between low and high
x = np.random.randint(3,100)
print("np.random.randint(3,100)...")
print(x)
# returns an array of random numbers of length 34
x = np.random.randint(3,100,34)
print("np.random.randint(3,100,34)...")
print(x)

Output

输出量

np.random.rand(5)...[0.87417146 0.77399086 0.40012698 0.37192848 0.98260636]
np.random.rand(5,1)...
[[0.712829  ]
[0.65959462]
[0.41553044]
[0.30583293]
[0.83997539]]
np.random.rand(5,1,3)...
[[[0.75920149 0.54824968 0.0547891 ]]
[[0.70911911 0.16475541 0.5350475 ]]
[[0.74052103 0.4782701  0.2682752 ]]
[[0.76906319 0.02881364 0.83366651]]
[[0.79607073 0.91568043 0.7238144 ]]]
np.random.randn()...
-0.6793254693909823
np.random.randn(2,3)...
[[ 0.66683143  0.44936287 -0.41531392]
[ 1.86320357  0.76638331 -1.92146833]]
np.random.randint(3)...
1
np.random.randint(3,100)...
53
np.random.randint(3,100,34)...
[43 92 76 39 78 83 89 87 96 59 32 74 31 77 56 53 18 45 78 21 46 10 25 86
64 29 49  4 18 19 90 17 62 29]

4) Reshape method (shape manipulation)

4)整形方法(形状处理)

An array has a shape given by the number of elements along each axis,

数组的形状由沿每个轴的元素数确定，

# importing package
import numpy as np
x = np.floor(10*np.random.random((3,4)))
print(x)
print(x.shape)

Output

输出量

[[0. 2. 9. 4.]
[0. 4. 1. 7.]
[9. 7. 6. 2.]]
(3, 4)

The shape of an array can be changes with various commands. However, the shape commands return all modified arrays but do not change the original array.

数组的形状可以通过各种命令进行更改。但是，shape命令返回所有修改后的数组，但不更改原始数组。

# importing package
import numpy as np
x = np.floor(10*np.random.random((3,4)))
print(x)
# returns the array, flattened
print("x.ravel()...")
print(x.ravel())
# returns the array with modified shape
print("x.reshape(6,2)...")
print(x.reshape(6,2))
# returns the array , transposed
print("x.T...")
print(x.T)
print("x.T.shape...")
print(x.T.shape)
print("x.shape...")
print(x.shape)

Output

输出量

[[3. 1. 0. 6.] [3. 1. 2. 4.]
[7. 0. 0. 1.]]
x.ravel()...
[3. 1. 0. 6. 3. 1. 2. 4. 7. 0. 0. 1.]
x.reshape(6,2)...
[[3. 1.]
[0. 6.]
[3. 1.]
[2. 4.]
[7. 0.]
[0. 1.]]
x.T...
[[3. 3. 7.] [1. 1. 0.]
[0. 2. 0.]
[6. 4. 1.]]
x.T.shape...
(4, 3)
x.shape...
(3, 4)

其他方法 (Additional methods)

# importing package
import numpy as np
x = np.floor(10*np.random.random((3,4)))
print(x)
#Return the maximum value in an array
print("x.max():", x.max())
# Return the minimum value in a array
print("x.min():", x.min())
# Return the index of max value in an array
print("x.argmax():", x.argmax())
# Return the index of min value in an array
print("x.argmin():", x.argmin())

Output

输出量

[[4. 0. 5. 2.] [8. 5. 9. 7.]
[9. 3. 5. 5.]]
x.max(): 9.0
x.min(): 0.0
x.argmax(): 6
x.argmin(): 1

翻译自: https://www.includehelp.com/python/numpy.aspx

python中 numpy