## 机器学习-NumPy使用总结 黄海广 副教授 2021年06月 ## 本章目录 01 NumPy概述 02 NumPy数组(ndarry)对象 03 ufunc函数 04 NumPy的函数库 ### 1 \.NumPy概述 ## 01 NumPy概述 02 NumPy数组(ndarry)对象 03 ufunc函数 04 NumPy的函数库 ## NumPy是什么？ NumPy(Numeric 609f/p4_1.jpg) ## NumPy是什么？ NumPy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，以及核心的科学计算组织如：Lawrence Livermore，NASA用其处理一些本来使用C++，Fortran或Matlab等所做的任务。 ## NumPy是什么？ 标准的Python中用li 间和内存。 NumPy诞生为了弥补这些缺陷。它提供了两种基本的对象： ndarray：全称（n-dimensional array object）是储存单一数据类型的多维数组。 ufunc：全称（universal function object）它是一种能够对数组进行处理的函数。 NumPy的官方文档： https://docs.scipy.org/doc/numpy/reference/

DataFrame provides everything that R's data.frame provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party transforming data • Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects • Intelligent label-based slicing, fancy indexing, and subsetting Note: This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first. # WHAT'S NEW These are new features and improvements

## PyTorch ## 创建Tensor 主讲人：龙良曲 ## I mport from numpy ## ● ● ● 1 In [62]: a=np.array([2,3.3]) 3 In [63]: torch.from_numpy(a) 4 Out[63]: tensor([2.0000, 3.3000], dtype=torch.float64) 6 In [65]: a=np a=np.ones([2,3]) 7 In [66]: torch.from_numpy(a) 8 Out[66]: 9 tensor([[1., 1., 1.], 10 [1., 1., 1.]], dtype=torch.float64) ## I mport from List ## ● ● ● 1 In [67]: torch.tensor([2., 3.2]) 2 Out[67]:

Using the Numba engine can yield significant performance gains if the apply function can operate on numpy arrays and the data set is larger (1 million rows or greater). For more details, see rolling apply extension type dedicated to string data. Previously, strings were typically stored in object-dtype NumPy arrays. (GH29975) Warning: StringDtype is currently considered experimental. The implementation and API may change without warning. The 'string' extension type solves several issues with object-dtype NumPy arrays: 1. You can accidentally store a mixture of strings and non-strings in an object dtype array

. . . . . . . . . . . . . . . . . . . . 438 2.5.16 Setting with enlargement conditionally using numpy() . . . . . . . . . . . . . . . . . . . . 442 2.5.17 The query() Method . . . . . . . . . . . . . and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 976 2.26.5 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 979 2.26.6 Thread-safety above, 3.8, and 3.9. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not

. . . . . . . . . . . . . . . . . . . . 438 2.5.16 Setting with enlargement conditionally using numpy() . . . . . . . . . . . . . . . . . . . . 442 2.5.17 The query() Method . . . . . . . . . . . . . and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 975 2.26.5 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978 2.26.6 Thread-safety above, 3.8, and 3.9. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not

. . . . . . . . . . . . . . . . . . . . 419 2.5.16 Setting with enlargement conditionally using numpy() . . . . . . . . . . . . . . . . . . . 423 2.5.17 The query() Method . . . . . . . . . . . . . . and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 935 2.26.5 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 938 2.26.6 Thread-safety above, 3.8, and 3.9. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not

and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 873 2.24.4 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 875 2.24.5 Thread-safety above, 3.7, and 3.8. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution

and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 859 2.21.4 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 861 2.21.5 Thread-safety above, 3.7, and 3.8. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way to install not not only pandas, but Python and the most popular packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution

and NA type promotions . . . . . . . . . . . . . . . . . . . . . . . . 857 3.21.4 Differences with NumPy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 3.21.5 Thread-safety GroupBy.apply() if called with a function which returned a non-pandas non-scalar object (e.g. a list or numpy array) (GH31441) • Fixed regression in DataFrame.groupby() whereby taking the minimum or maximum of na_rep might truncate the values written (GH31447) • Fixed regression in Categorical construction with numpy.str_ categories (GH31499) • Fixed regression in DataFrame.loc() and DataFrame.iloc() when selecting