Comprehensive analysis of numpy library functions

numpy (Numerical Python) is a library for scientific computing in Python, which provides efficient numerical operation functions. In the numpy library, there are a large number of functions for us to use. This article will analyze in detail the usage of some common functions in the numpy library and give corresponding code examples.

1. Create array function

1. numpy.array function
   The numpy.array function is used to create an array object, which can be a one-dimensional, two-dimensional, or multi-dimensional array. Parameters can be lists, tuples, arrays, etc.
   Code example:

import numpy as np
# 创建一维数组
a = np.array([1, 2, 3, 4, 5])
print(a)

# 创建二维数组
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)

# 创建多维数组
c = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(c)

2. numpy.zeros function
   The numpy.zeros function is used to create an array of all 0s and can specify the shape of the array.
   Code example:

import numpy as np
# 创建一个全为0的一维数组
a = np.zeros(5)
print(a)

# 创建一个全为0的二维数组
b = np.zeros((2, 3))
print(b)

3. numpy.ones function
   The numpy.ones function is used to create an array of all 1s. The shape of the array can also be specified.
   Code example:

import numpy as np
# 创建一个全为1的一维数组
a = np.ones(5)
print(a)

# 创建一个全为1的二维数组
b = np.ones((2, 3))
print(b)

2. Mathematical functions

1. numpy.sin function
   numpy.sin function is used to calculate the sine value of each element in the array .
   Code example:

import numpy as np
a = np.array([0, np.pi/2, np.pi])
b = np.sin(a)
print(b)

2. numpy.cos function
   The numpy.cos function is used to calculate the cosine value of each element in an array.
   Code example:

import numpy as np
a = np.array([0, np.pi/2, np.pi])
b = np.cos(a)
print(b)

3. numpy.exp function
   The numpy.exp function is used to calculate the exponent value of each element in the array.
   Code example:

import numpy as np
a = np.array([1, 2, 3])
b = np.exp(a)
print(b)

3. Statistical function

1. numpy.mean function
   numpy.mean function is used to calculate the average of each element in the array .
   Code example:

import numpy as np
a = np.array([1, 2, 3, 4, 5])
b = np.mean(a)
print(b)

2. numpy.max function
   The numpy.max function is used to calculate the maximum value in an array.
   Code example:

import numpy as np
a = np.array([1, 2, 3, 4, 5])
b = np.max(a)
print(b)

3. numpy.min function
   The numpy.min function is used to calculate the minimum value in an array.
   Code example:

import numpy as np
a = np.array([1, 2, 3, 4, 5])
b = np.min(a)
print(b)

4. Array operation function

1. numpy.reshape function
   The numpy.reshape function is used to change the shape of the array. You can Converts an array to the specified number of rows and columns.
   Code example:

import numpy as np
a = np.array([1, 2, 3, 4, 5, 6])
b = np.reshape(a, (2, 3))
print(b)

2. numpy.transpose function
   The numpy.transpose function is used to transpose an array.
   Code example:

import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
b = np.transpose(a)
print(b)

The above are only some of the functions in the numpy library. There are many other functions that can be used for array calculations, statistics, operations, etc. I hope this article can help readers better understand the function list in the numpy library.

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