When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Pandas with Python: Analyze, Transform & Export Data

本课程是 Data Analysis with NumPy and Pandas 专项课程的一部分

位教师：EDUCBA

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

12 条评论

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。

12 条评论

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

Filter, group, aggregate, and reshape datasets using Pandas.
Handle missing values, manage indexes, and analyze time series.
Create pivot tables, crosstabs, and export data to CSV/Excel.

您将获得的技能

您将学习的工具

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

16 项作业

授课语言：英语（English）

91% of learners achieved a positive career outcome

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 Data Analysis with NumPy and Pandas 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

Learners will gain the ability to manipulate, analyze, and visualize data effectively using Python’s Pandas library. By the end of this course, they will be able to filter and transform datasets, apply grouping and aggregation, handle missing values, manage indexes, and reshape data for advanced analytics. They will also master techniques for working with time series, pivot tables, crosstabs, and exporting data to CSV and Excel.

This course is designed for aspiring data analysts, Python enthusiasts, and professionals looking to strengthen their data manipulation skills. With hands-on lessons and quizzes, learners will build confidence in handling real-world datasets while applying best practices for efficiency and readability. What makes this course unique is its structured progression—from foundational Pandas operations to advanced techniques—combined with practical exercises and applied projects. Learners won’t just watch tutorials; they will actively practice data handling in Jupyter Notebooks, ensuring they are job-ready for data science and analytics roles.

单元详情

This module introduces learners to the Pandas library, its installation, and the Jupyter environment for hands-on coding. It covers Pandas’ core data structures, including Series and DataFrames, and explores fundamental operations for working with rows and columns. Learners build a strong foundation for effective data handling.

涵盖的内容

9个视频4个作业

9个视频总计46分钟

Introduction to Pandas with Python5分钟
Understanding Jupiter Environment4分钟
Reading the Data Set9分钟
Series and Data Frame4分钟
Operations in Data Set4分钟
More on Panda Functions6分钟
Column Names and Operation9分钟
Removing Columns and Rows3分钟
Sorting Data Frame3分钟

4个作业总计60分钟

Graded-Getting Started with Pandas30分钟
Introduction and Setup10分钟
Core Data Structures10分钟
Working with Columns and Rows10分钟

This module focuses on advanced filtering, selection, and transformation of data. Learners explore indexing by labels and positions, handle data types, apply string methods, and group data for aggregation. It also emphasizes working with Series, plotting, and handling null values.

涵盖的内容

12个视频4个作业

12个视频总计50分钟

Filtering Data7分钟
Filter Multiple Criteria6分钟
Selective Columns and Rows3分钟
Data Frame and Series2分钟
Axis Parameter3分钟
String Methods in Pandas4分钟
Changing the Data Types4分钟
Example of Data Type Change4分钟
Group by Functions4分钟
Functions on Series4分钟
Plotting series in Pandas4分钟
Dealing with Null Values5分钟

4个作业总计60分钟

Graded-Data Selection and Transformation30分钟
Filtering and Selection Techniques10分钟
Data Types and Grouping10分钟
Working with Series and Null Values10分钟

This module introduces indexing concepts and parameters that enhance data manipulation. Learners explore memory management, sampling strategies, dummy coding, handling duplicates, working with date/time functions, and avoiding common pitfalls like copy warnings.

涵盖的内容

16个视频4个作业

16个视频总计106分钟

Uses of Index6分钟
Column in Index6分钟
Output of Data8分钟
Functions of iX Method9分钟
InPlace Parameter4分钟
Inspecting the Space5分钟
Reducing the Space10分钟
Using in Country Series4分钟
Creating Manual Data Frame7分钟
Random Sampling with Pandas4分钟
Concept of Dummy Coding12分钟
Creating Dummified Values4分钟
Duplicates in Data Frame9分钟
Functions for Date and Time9分钟
Setting with Copy Warning5分钟
Example on Copy Warning5分钟

4个作业总计60分钟

Graded-Indexing, Sampling, and Advanced Functions30分钟
Exploring Index and Parameters10分钟
Space, Sampling, and Dummy Coding10分钟
Handling Duplicates, Dates, and Warnings10分钟

This module covers advanced reshaping, merging, and exporting functionalities in Pandas. Learners gain expertise in display options, formatting, working with pivot tables, crosstab functions, and exporting data to external formats like CSV and Excel for practical applications.