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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.
import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr)
The output result is:
[1 2 3 4 5]
import numpy as np arr = np.arange(0, 10, 2) print(arr)
The output result is:
[0 2 4 6 8]
import numpy as np arr = np.zeros((2, 3)) print(arr)
The output result is:
[[0. 0. 0.] [0. 0. 0.]]
import numpy as np arr = np.ones((2, 3)) print(arr)
The output result is:
[[1. 1. 1.] [1. 1. 1.]]
import numpy as np arr = np.linspace(0,1,5) print(arr)
The output result is:
[0. 0.25 0.5 0.75 1. ]
import numpy as np arr = np.eye(3) print(arr)
The output result is:
[[1. 0. 0.] [0. 1. 0.] [0. 0. 1.]]
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]]
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]
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]
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
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]
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
import numpy as np arr = np.array([1, 2, 3, 4, 5]) mean = np.mean(arr) print(mean)
The output result is:
3.0
import numpy as np arr = np.array([1, 2, 3, 4, 5]) std = np.std(arr) print(std)
The output result is:
1.4142135623730951
import numpy as np matrix = np.array([[1, 2], [3, 4]]) det = np.linalg.det(matrix) print(det)
The output result is:
-2.0000000000000004
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]]
import numpy as np arr = np.loadtxt('data.txt') print(arr)
import numpy as np arr = np.array([1, 2, 3, 4, 5]) np.savetxt('data.txt', arr)
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]
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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