|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/fundamentals-of-pandas/
课程评论：没有评论
**课程名称：Pandas 基础** **课程概述：** 本课程是关于使用 Python 编程语言进行数据分析和数据结构化的高性能、易于使用的数据分析工具库——Pandas 的入门课程。Pandas 是一个快速、强大、灵活且易于使用的开源数据分析和操作工具。它提供了全面的数据处理工具集，包括读取和写入各种文件、数据清洗和整理、分析和建模以及可视化。Pandas 在数据科学、金融、神经科学、经济学、广告、网络分析、统计学、社会科学以及工程学等众多领域有着广泛的应用。 通过本课程，您将学习如何使用 Pandas 库进行数据分析和数据结构化。您将深入了解 DataFrame 对象、基本绘图、索引以及 groupby 操作。课程旨在帮助您更有效地处理数据，学完后，您将对 Pandas 有扎实的理解，并能熟练运用 Python Pandas 库进行数据分析。 **Pandas 库亮点：** * **高效的 DataFrame 对象：** 提供集成的索引功能，用于数据处理。 * **数据读写能力：** 支持在内存数据结构与 CSV、文本文件、Microsoft Excel、SQL 数据库以及 HDF5 等多种格式之间进行数据读写。 * **智能数据对齐与缺失数据处理：** 自动进行基于标签的计算对齐，轻松处理混乱的数据，使其有序化。 * **灵活的数据重塑和透视：** 提供对数据集进行分组、重塑和透视的功能。 * **智能标签索引、高级索引和子集选择：** 能够对大型数据集进行基于标签的切片、高级索引和子集选择。 * **数据结构的可变性：** 支持在数据结构中插入和删除列。 * **强大的 Group By 引擎：** 允许对数据集进行 split-apply-combine 操作，实现数据聚合与转换。 * **高性能数据合并与连接：** 提供高效的数据集合并和连接功能。 * **分层轴索引：** 通过分层轴索引，可以在低维数据结构中直观地处理高维数据。 * **时间序列功能：** 包括日期范围生成、频率转换、移动窗口统计、日期偏移和滞后。同时支持创建特定领域的日期偏移，并在不丢失数据的情况下连接时间序列。 * **高性能优化：** 关键代码路径已用 Cython 或 C 语言优化，确保高性能。
Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool. Pandas provides a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with widespread use of Pandas include: data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.In this course, you'll learn how to use the pandas library and tools for data analysis and data structuring. Students will learn about DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, By the end of this course, students should have a good understanding of Pandas and gain proficiency using the Python Pandas library for data analysis.Library HighlightsA fast and efficient DataFrame object for data manipulation with integrated indexing;Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;Flexible reshaping and pivoting of data sets;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Columns can be inserted and deleted from data structures for size mutability;Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;High performance merging and joining of data sets;Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;Time series-functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;Highly optimized for performance, with critical code paths written in Cython or C.
