|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-analysis-with-polars/
课程评论：没有评论
课程名称：使用 Polars 进行数据分析 课程概述：本课程将帮助您充分利用 Polars，这个快速增长的开源数据框架库，已成为 Python 数据科学家首选的数据框架库。作为 Polars 的贡献者，我的重点是让新用户更容易使用 Polars，并将本课程更新到最新版本1.30.0。学员将获得对 Polars 的全面介绍，课程内容由 Ritchie Vink（Polars 创建者）确认。本课程适合对 Pandas 或其他数据框架库有一定了解的数据科学家，旨在帮助他们转向 Polars，因为它的语法更加简洁且运行更快。 核心材料为 Jupyter 笔记本，每个主题都有深入的探讨，每个笔记本还配有一系列练习，帮助您理解核心概念。对许多重要主题而言，本课程可能是唯一的学习文档来源，它基于我对 Polars 源代码的考察。 需要注意的是，尽管本课程包含一些视频讲座，但它主要以笔记本为中心，而不是以视频为主。并非所有讲座都有视频，某些视频也可能不再更新，因为 Polars API 的变化较快。为了保持课程内容的时效性，我专注于通过广泛的自动化测试系统，及时更新笔记本内容。每两个月，我会根据 Polars 的更新发布一次课程的新版。 课程介绍了 Polars 的语法，并展示了多种易于阅读和编写的查询方式。课程深度探讨了驱动 Polars 优秀性能的算法，使学员能够更好地理解和利用这些算法。完成本课程后，您将掌握以下数据处理技能： - 从 CSV、Excel、Parquet、云存储或数据库加载和转换数据 - 进行并行分析 - 理解构建查询的最佳模式 - 处理超大内存数据集 - 在数据上执行聚合 - 通过连接和合并来组合数据集 - 处理嵌套数据类型，包括列表和结构体 - 优化查询的速度和内存使用 - 处理字符串和分类数据 - 使用 Matplotlib、Seaborn、Plotly、hvPlot 和 Altair 可视化输出 - 用 sklearn 准备机器学习管道的数据 本课程是希望提升数据分析技能的学习者理想的选择。
In this course I show you how to take advantage of Polars - the fast-growing open source dataframe library that is becoming the go-to dataframe library for data scientists in python. I am a Polars contributor with a focus on making Polars accessible to new users and I keep this course up-to-date with new releases of Polars - updated to version 1.30.0"A thorough introduction to Polars" - Ritchie Vink, creator of Polars"Thank you for your great work with this course - I've optimized some code thanks to it already!" Maiia BocharovaThe course is for data scientists who have some familiarity with a dataframe library like Pandas but who want to move to Polars because it is easier to write and faster to run. The core materials are Jupyter notebooks that examine each topic in depth. Each notebook comes with a set of exercises to help you develop your understanding of the core concepts. For many key topics this course is the only source of documentation for learners and comes from my time examining the Polars source code.An important note about videos: this is a primarily a notebook course and not a video course. Not all of the lectures have videos and some of the videos may have components that are not up-to-date. Why? Because the Polars API has changed too often to allow me to keep videos up-to-date. Instead I focus on keeping the notebooks up-to-date with an extensive automated testing system that alerts me to changes in the API. I release an updated version of the course about twice a month in response to changes in Polars. The course introduces the syntax of Polars and shows you the many ways that Polars allows you to produce queries that are easy to read and write. However, the course also delves deeper to help you understand and exploit the algorithms that drive the outstanding performance of Polars. By the end of the course you will have optimised ways to:load and transform your data from CSV, Excel, Parquet, cloud storage or a database run your analysis in parallelunderstand optimal patterns for building querieswork with larger-than-memory datasetscarry out aggregations on your datacombine your datasets with joins and concatenationswork with nested dtypes including lists and structsoptimise the speed and memory usage of your querieswork with string and categorical datavisualise your outputs with Matplotlib, Seaborn, Plotly, hvPlot & Altairprepare your data for machine learning pipelines with sklearn
