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Pandas for Data Science

Pandas for Data Science

本课程是 Programming for Python Data Science: Principles to Practice 专项课程的一部分

位教师：Genevieve M. Lipp

3,625 人已注册

包含在中

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4个模块

深入了解一个主题并学习基础知识。

16 条评论

初级等级

推荐体验

4 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。

16 条评论

初级等级

推荐体验

4 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

How and when to leverage the Pandas library for your data science projects
Best practices for cleaning, manipulating, and optimizing data with Pandas

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

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作业

9 项作业

授课语言：英语（English）

91%

of learners achieved a positive career outcome

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积累特定领域的专业知识

本课程是 Programming for Python Data Science: Principles to Practice 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。

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该课程共有4个模块

How can you effectively use Python to clean, sort, and store data? What are the benefits of using the Pandas library for data science? What best practices can data scientists leverage to better work with multiple types of datasets? In the third course of Data Science Python Foundations Specialization from Duke University, Python users will learn about how Pandas — a common library in Python used for data science — can ease their workflow.

We recommend you should take this course after the first two courses of the specialization. However, if you hold a prerequisite knowledge of basic algebra, Python programming, and NumPy, you should be able to complete the material in this course. In the first week, we’ll discuss Python file concepts, including the programming syntax that allows you to read and write to a file. Then in the following weeks, we’ll transition into discussing Pandas more specifically and the pros and cons of using this library for specific data projects. By the end of this course, you should be able to know when to use Pandas, how to load and clean data in Pandas, and how to use Pandas for data manipulation. This will prepare you to take the next step in your data scientist journey using Python; creating larger software programs.

单元详情

This module, you will learn how to read data from files into your python program, and write that corresponding data to a file. We’ll be working primarily with string-type data in this unit and will give special attention to the way that python handles strings. Additionally we’ll go over some basic debugging in python using exception traces, and you’ll leverage these to create your own python program that is capable of reading and writing to a file.

涵盖的内容

5个视频8篇阅读材料3个作业3个编程作业

5个视频总计25分钟

Course Introduction 3分钟
Paths and Filesystems 4分钟
Exceptions 8分钟
Using an Exception Traceback to Debug4分钟
File IO6分钟

8篇阅读材料总计64分钟

Report a problem with the course10分钟
Reading and Writing Data10分钟
Strings5分钟
File System Concepts10分钟
Exceptions2分钟
Understanding Exception Traces15分钟
You Might Need Data Other than Pandas10分钟
CSV Files2分钟

3个作业总计375分钟

Exception Concepts15分钟
Understanding Exception Traces180分钟
File Concepts180分钟

3个编程作业总计540分钟

Roman Numerals180分钟
Get Parenthesized Regions180分钟
Sort File180分钟

This module, you’ll learn how to begin to utilize Pandas, one of the most commonly used libraries in Data Science with python. Pandas is predominantly used for working with tabular data. By the end of this module you’ll be able to identify the hallmarks and quirks of working with tabular data, describe the benefits and limitations of using Pandas, and be able to perform some basic data manipulation techniques in Pandas.

涵盖的内容

1个视频9篇阅读材料2个作业3个非评分实验室

1个视频总计32分钟

Live Coding - Indexing and Subsetting32分钟

9篇阅读材料总计90分钟

Introduction to the Module: Tabular Data10分钟
Pandas Series10分钟
Manipulating Series: Subsetting and Indexing Series10分钟
(OPTIONAL) Indexing with Brackets10分钟
The object Data Type10分钟
Tabular Data through Dataframes10分钟
Subsetting DataFrames: Tips and Pitfalls10分钟
The Categorical Data Type10分钟
PyArrow: An Alternative to Numpy as Pandas Backend10分钟

2个作业总计60分钟

Module 2 Pandas Series Self-Check30分钟
Module 2 Wrap Up Graded Quiz (For Submitting Answers Generated in Lab)30分钟

3个非评分实验室总计180分钟

Module 2 Pandas Series Exercises60分钟
Live Coding Lab60分钟
Module 2 Wrap Up Lab (For Graded Quiz)60分钟

This Module, you will learn how to perform basic file operations in Pandas, as well as how to clean up large datasets. You’ll learn to read and write from common tabular file formats, and Pandas-specific intricacies for working with that data. Additionally, you’ll learn best practices for cleaning your data.

涵盖的内容

1个视频13篇阅读材料3个作业4个非评分实验室

1个视频总计50分钟

Live Coding: Cleaning Data 50分钟

13篇阅读材料总计130分钟

Intro to Module 310分钟
Storing and Reading Data: Plain Text10分钟
Storing and Reading Data: Binary Files10分钟
Pandas Indices10分钟
Views and Copies: NumPy Review10分钟
Views and Copies: Pandas 10分钟
Views and Copies: Copy on Write 10分钟
Indexing Lab Discussion10分钟
Cleaning Data: Identifying10分钟
Cleaning Data: Editing Globally10分钟
Cleaning Data: Editing Specific Data10分钟
Cleaning Data: Datatypes10分钟
Cleaning Data: Missing Data10分钟

3个作业总计90分钟

Module 3 Missing Data Exercises Self-Check30分钟
Module 3 Missing Data Exercises Self-Check30分钟
Module 3 Wrap Up Graded Quiz (For Submitting Answers Generated in Lab)30分钟

4个非评分实验室总计240分钟

Indexing Lab60分钟
Module 3 Missing Data Exercises (Ungraded)60分钟
Module 3 Live Coding Lab60分钟
Module 3 Wrap Up Lab (for Graded Quiz)60分钟

This module you will learn how to combine datasets from different sources. Pandas has different methods of combining data depending on your preferred outcome, and you’ll be able to differentiate between when to use each kind. Additionally, we’ll go over computationally efficient ways of querying your data, which, while similar to selecting data via subsetting in its outcomes, has a distinct set of advantages.