title: "100 numpy exercises with hint" date: 2026-05-24T13:50:42Z 100 numpy exercises with hint This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for both old and new users …

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title: "Set routines" date: 2026-05-24T13:51:04Z Set routines 1import numpy as np 1np.version '1.11.2' 1author = 'kyubyong. longinglove@nate.com' Making proper sets Q1. Get unique elements and reconstruction indices from x. And reconstruct x. 1x = np.array([1, 2, 6, 4, 2, 3, 2]) unique elements = [1 2 3 4 6] …

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title: "Set routines" date: 2026-05-24T13:51:07Z Set routines 1import numpy as np 1np.version '1.11.2' 1author = 'kyubyong. longinglove@nate.com' Making proper sets Q1. Get unique elements and reconstruction indices from x. And reconstruct x. 1x = np.array([1, 2, 6, 4, 2, 3, 2]) 2out, indices = np.unique(x, …

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title: "Soring, searching, and counting" date: 2026-05-24T13:51:10Z Soring, searching, and counting 1import numpy as np 1np.version '1.11.2' 1author = 'kyubyong. longinglove@nate.com' Sorting Q1. Sort x along the second axis. 1x = np.array([[1,4],[3,1]]) [[1 4] [1 3]] Q2. Sort pairs of surnames and first names and …

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title: "Soring, searching, and counting" date: 2026-05-24T13:51:13Z Soring, searching, and counting 1import numpy as np 1np.version '1.11.2' 1author = 'kyubyong. longinglove@nate.com' Sorting Q1. Sort x along the second axis. 1x = np.array([[1,4],[3,1]]) 2out = np.sort(x, axis=1) 3x.sort(axis=1) 4assert …

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title: "Statistics" date: 2026-05-24T13:51:15Z Statistics 1__author__ = "kyubyong. kbpark.linguist@gmail.com" 1import numpy as np 1np.version '1.11.3' Order statistics Q1. Return the minimum value of x along the second axis. 1x = np.arange(4).reshape((2, 2)) 2print("x=\n", x) x= [[0 1] [2 3]] ans= [0 2] Q2. Return …

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title: "Statistics" date: 2026-05-24T13:51:18Z Statistics 1__author__ = "kyubyong. kbpark.linguist@gmail.com" 1import numpy as np 1np.version '1.11.3' Order statistics Q1. Return the minimum value of x along the second axis. 1x = np.arange(4).reshape((2, 2)) 2print("x=\n", x) 3print("ans=\n", np.amin(x, 1)) x= [[0 …

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title: "Array manipulation routines" date: 2026-05-24T13:51:27Z Array manipulation routines 1import numpy as np 1np.version '1.11.2' Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150. [[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. …

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title: "Array manipulation routines" date: 2026-05-24T13:51:30Z Array manipulation routines 1import numpy as np 1np.version '1.11.2' Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150. 1x = np.ones([10, 10, 3]) 2out = np.reshape(x, [-1, …

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title: "1.Numpy简易入门" date: 2026-05-24T13:49:59Z 1.Numpy简易入门 1.1 认识NumPy数组对象 Numpy是一个用python实现的科学计算的扩展程序库，包括： 1、一个强大的N维数组对象Array； 2、比较成熟的（广播）函数库； 3、用于整合C/C++和Fortran代码的工具包； 4、实用的线性代数、傅里叶变换和随机数生成函数。numpy和稀疏矩阵运算包scipy配合使用更加方便。 NumPy（Numeric Python）提供了许多高级的数值编程工具，如：矩阵数据类型、矢量处理，以及精密的运算库。专为进行严格的数字处理而产生。多为很多大型金融公司使用，以及核心的科学计 …

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