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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-engineering-with-polars-in-python/
课程评论：没有评论
**Polars 数据分析与 Python 课程总结** 本课程面向希望掌握高效数据处理能力的学员，包括： * **数据分析师：** 学习数据发现实践。 * **初级数据工程师：** 提升数据操纵技能。 * **数据工程师：** 将 Polars 集成到数据管道中。 * **Pandas 用户：** 寻求向 Polars 迁移。 **为何学习 Polars？** Python 在数据管道中的应用日益广泛，但处理大型数据集时，性能瓶颈曾是其一大挑战。Polars 凭借其高性能数据结构，如并行处理能力，大幅提升了数据读取、写入和操纵的速度，远超 Pandas。 **课程收获：** 完成本课程后，您将能够： * 将 CSV 文件读取至 Polars DataFrame。 * 将数据从 Polars 直接推送到数据库。 * 将 DataFrame 导出至 Excel。 * 进行复杂数据集的聚合操作。 * 连接（Join）不同的 DataFrame。 * 充分利用 Polars 的卓越处理速度。 **常见问题解答：** * **Pandas 迁移是否困难？** 不难。核心概念与 Pandas 相似，尽管存在差异，但功能高度一致。 * **学习 Pandas 是否重复？** 不会。Pandas 经验有助于理解 Polars，而 Polars 在处理大型数据集时性能优势明显。 * **是否会错过 Pandas 的集成？** 不会。Polars 与 Pandas DataFrame 相互转换非常方便，您可以结合两者的优势。此外，Polars 的集成也在不断增加。 * **哪种 DataFrame 库“最好”？** Pandas 和 Polars 各有千秋，选择取决于您的具体需求。
Who Should Take This Course?Aspiring Data Analysts seeking to learn data discovery practices Beginner Data Engineers looking to improve data manipulation skillsData Engineers looking to utilize polars in their data pipelinesPandas users looking to make the switch to PolarsWhy Learn PolarsOver the last decade Python has become more utilized in Data Pipelines. However, most pipelines faced performance issues when processing large datasets in Python. This limitation hindered Python's ability to manage "Big Data".But in recent years, Polars unlocked the door to processing large datasets with its high performance data structures. It uses parallel processing to quickly read data into DataFrames and Series. And its performance doesn't stop there! Not only can Polars read and write data quickly, it can also manipulate vast amounts data faster than Pandas. After Finishing the Course, you'll be able to: Read CSV files into Polars DataFramesKnow how to push data directly from Polars into a databaseExport DataFrames to ExcelAggregate complex datasetsJoin DataFrames togetherUtilize Polars' superior processing speedFAQsQ: Is the switch from Pandas difficult?A: No. The basic concepts are the same. There are definitely differences between the two libraries, but functionality between the two are very similar. If you can do it in Pandas, you can do it in Polars! Q: I'm already learning Pandas, would you say I'm wasting my time?A: No. My first exposure to DataFrames was using Pandas. Many of the concepts I learned in Pandas helped me understand Polars. They are definitely different in terms of performance. Pandas may at some point release a faster version, but as for now Polars is much faster when working with large datasets. Q: Pandas has integrations with many more libraries than Polars. Won't I be missing out on these if I make the switch?A: Absolutely not. Its true that Polars does not have as many integrations with other python libraries, but switching from a polars DataFrame to a Pandas DataFrame is easy. Polars has a function that allows you to convert to and from a Pandas DataFrame. This allows you to get the performance of Polars while also getting the integrations of Pandas. Other libraries have also begun to build integrations with Polars so that may change altogether. Q: What kind of bear is best? A: There are basically two schools of thought.Pandas and Polars are indeed competing DataFrame libraries. Its probably for you to decide the answer to this question!
