VIP内容
本书通过使用Python的案例研究来探索数据分析和统计的基础知识。这本书将向你展示如何自信地用Python编写代码，以及如何使用各种Python库和函数来分析任何数据集。该代码在Jupyter 笔记本中提出，可以进一步调整和扩展。
这本书分为三个部分——用Python编程，数据分析和可视化，以及统计。首先介绍Python——语法、函数、条件语句、数据类型和不同类型的容器。然后，您将回顾更高级的概念，如正则表达式、文件处理和用Python解决数学问题。
本书的第二部分将介绍用于数据分析的Python库。将有一个介绍性的章节涵盖基本概念和术语，和一个章节的NumPy(科学计算库)，NumPy(数据角力库)和可视化库，如Matplotlib和Seaborn。案例研究将包括作为例子，以帮助读者理解一些实际应用的数据分析。
本书的最后几章集中在统计学上，阐明了与数据科学相关的统计学的重要原则。这些主题包括概率、贝叶斯定理、排列和组合、假设检验(方差分析、卡方检验、z检验和t检验)，以及Scipy库如何简化涉及统计的繁琐计算。
你会: 进一步提高你的Python编程和分析技能 用Python解决微积分、集合论和代数中的数学问题 使用Python中的各种库来结构化、分析和可视化数据 使用Python进行实际案例研究 回顾基本的统计概念，并使用Scipy库来解决统计方面的问题
成为VIP会员查看完整内容
A Python Data Analyst’s Toolkit Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics
热门内容
最新内容
最新论文
The COVID-19 pandemic has lead to a worldwide effort to characterize its evolution through the mapping of mutations in the genome of the coronavirus SARS-CoV-2. Ideally, one would like to quickly identify new mutations that could confer adaptive advantages (e.g. higher infectivity or immune evasion) by leveraging the large number of genomes. One way of identifying adaptive mutations is by looking at convergent mutations, mutations in the same genomic position that occur independently. However, the large number of currently available genomes precludes the efficient use of phylogeny-based techniques. Here, we establish a fast and scalable Topological Data Analysis approach for the early warning and surveillance of emerging adaptive mutations based on persistent homology. It identifies convergent events merely by their topological footprint and thus overcomes limitations of current phylogenetic inference techniques. This allows for an unbiased and rapid analysis of large viral datasets. We introduce a new topological measure for convergent evolution and apply it to the GISAID dataset as of February 2021, comprising 303,651 high-quality SARS-CoV-2 isolates collected since the beginning of the pandemic. We find that topologically salient mutations on the receptor-binding domain appear in several variants of concern and are linked with an increase in infectivity and immune escape, and for many adaptive mutations the topological signal precedes an increase in prevalence. We show that our method effectively identifies emerging adaptive mutations at an early stage. By localizing topological signals in the dataset, we extract geo-temporal information about the early occurrence of emerging adaptive mutations. The identification of these mutations can help to develop an alert system to monitor mutations of concern and guide experimentalists to focus the study of specific circulating variants.
