Python Data Science: pandas, Matplotlib, and Seaborn

Python Data Science: pandas, Matplotlib, and Seaborn

本课程是 Using Data Science Tools in Python 专项课程的一部分

位教师：Bill Rosenthal

访问权限由 New York State Department of Labor 提供

4个模块

深入了解一个主题并学习基础知识。

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

In this course, you will manage, analyze, manipulate, modify, and visualize pandas DataFrames; and visualize data with Matplotlib and Seaborn.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

1 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Using Data Science Tools in Python 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有4个模块

To round out your exploration of data science in Python, in this course, you'll work with the pandas DataFrame—one of the most prominent data structures in data science. You'll create DataFrames, load and save data, analyze data, and slice and filter data in DataFrames. Then, you'll manipulate, modify, and plot DataFrame data. Lastly, you'll work with specialized plotting libraries Matplotlib and Seaborn to create common types of plots and format those plots so they are visually appealing and optimal for analysis.

While NumPy serves as the foundation of your data science tasks, you may instead work directly with the more user-friendly library called pandas, which builds on NumPy. Or, you may find it beneficial to work with both. In either case, as with NumPy, you'll want to begin by managing your data within pandas structures and then analyze that data for useful insights.

涵盖的内容

1篇阅读材料6个插件

The pandas libraries provides many tools for changing data to meet your needs. It also provides basic plotting functionality for the analysis and/or presentation of data. In this lesson, you'll transform and visualize your data in multiple ways.

涵盖的内容

5个插件

Although you did some simple plotting with pandas directly, you'll likely need to get more detailed with your visualizations. Matplotlib is the most common plotting library in Python®, and you'll use it to generate visualizations that help you tell a story with your data. Likewise, you'll use the Seaborn library, which is built on Matplotlib, to help you streamline your plotting efforts.