Complete list of numpy functions and their uses: Detailed explanation of all functions in the numpy library

numpy function encyclopedia: Detailed explanation of all functions and their uses in the numpy library, specific code examples are required

Introduction:
In the field of data analysis and scientific computing , often need to process large-scale numerical data. Numpy is the most commonly used open source library in Python, providing efficient multi-dimensional array objects and a series of functions for operating arrays. This article will introduce in detail all the functions and their uses in the numpy library, and give specific code examples to help readers better understand and use the numpy library.

1. Creation and transformation of arrays

1. np.array(): Create an array and convert the input data into an ndarray object.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
print(arr)

The output result is:

[1 2 3 4 5]

2. np.arange(): Create an arithmetic array.

import numpy as np

arr = np.arange(0, 10, 2)
print(arr)

The output result is:

[0 2 4 6 8]

3. np.zeros(): Create an array whose elements are all 0.

import numpy as np

arr = np.zeros((2, 3))
print(arr)

The output result is:

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

4. np.ones(): Create an array with all elements being 1.

import numpy as np

arr = np.ones((2, 3))
print(arr)

The output result is:

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

5. np.linspace(): Create an equally spaced array.

import numpy as np

arr = np.linspace(0,1,5)
print(arr)

The output result is:

[0.   0.25 0.5  0.75 1.  ]

6. np.eye(): Create a matrix with a diagonal of 1.

import numpy as np

arr = np.eye(3)
print(arr)

The output result is:

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

2. Array operations and calculations

1. Array shape operations

• np.reshape(): Change the shape of the array.

import numpy as np

arr = np.arange(1, 10)
arr_reshape = np.reshape(arr, (3, 3))
print(arr_reshape)

The output result is:

[[1 2 3]
 [4 5 6]
 [7 8 9]]

• arr.flatten(): Convert a multi-dimensional array to a one-dimensional array.

import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])
arr_flatten = arr.flatten()
print(arr_flatten)

The output result is:

[1 2 3 4 5 6]

2. Array element operation

• np.sort(): Array element operation Sort.

import numpy as np

arr = np.array([3, 1, 5, 2, 4])
arr_sorted = np.sort(arr)
print(arr_sorted)

The output result is:

[1 2 3 4 5]

• np.argmax(): Returns the index of the largest element in the array.

import numpy as np

arr = np.array([3, 1, 5, 2, 4])
max_index = np.argmax(arr)
print(max_index)

The output result is:

2

3. Array operations

• np.add(): Add two arrays .

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.add(arr1, arr2)
print(result)

The output result is:

[5 7 9]

• np.dot(): Dot multiplication of two arrays.

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
result = np.dot(arr1, arr2)
print(result)

The output result is:

32

3. Statistical functions and linear algebra functions

1. Statistical functions

• np.mean(): Calculate the mean of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
mean = np.mean(arr)
print(mean)

The output result is:

3.0

• np.std(): Calculate the standard deviation of the array.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
std = np.std(arr)
print(std)

The output result is:

1.4142135623730951

2. Linear algebra function

• np.linalg.det(): Calculate the matrix determinant.

import numpy as np

matrix = np.array([[1, 2], [3, 4]])
det = np.linalg.det(matrix)
print(det)

The output result is:

-2.0000000000000004

• np.linalg.inv(): Calculate the inverse matrix of the matrix.

import numpy as np

matrix = np.array([[1, 2], [3, 4]])
inv = np.linalg.inv(matrix)
print(inv)

The output result is:

[[-2.   1. ]
 [ 1.5 -0.5]]

IV. Auxiliary functions and general functions

1. Auxiliary functions

• np.loadtxt(): Load data from a text file.

import numpy as np

arr = np.loadtxt('data.txt')
print(arr)

• np.savetxt(): Save data to a text file.

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
np.savetxt('data.txt', arr)

2. General function

• np.sin(): Calculate the sine value of the elements in the array.

import numpy as np

arr = np.array([0, np.pi / 2, np.pi])
sin_val = np.sin(arr)
print(sin_val)

The output result is:

[0.         1.         1.2246468e-16]

• np.exp(): Calculate the exponent value of the elements in the array.

import numpy as np

arr = np.array([1, 2, 3])
exp_val = np.exp(arr)
print(exp_val)

The output result is:

[ 2.71828183  7.3890561  20.08553692]

This article only shows a small part of the functions in the numpy library, and numpy has more powerful functions and functions. I hope readers can flexibly use the functions of the numpy library in actual programming to improve the efficiency and accuracy of data processing.

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