Manage Invalid, Duplicate, and Missing Data in Python

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Axel Sirota

56:48

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1. Course Overview

1. Course Overview.mp4

01:43

2. Why Clean Data

1. Introduction.mp4

01:37

2. Getting the Best out of This Course.mp4

02:15

3. Version Check.mp4

01:23

4. What Will You Be Able to Do When Finishing This Course.mp4

00:39

5. Outline of the Course.mp4

00:42

3. Handling Missing Values in Your Data

1. Recap - Using Indexers in Pandas.mp4

01:54

2. Demo - Identify NaNs - Part1.mp4

02:25

3. Demo - Identify NaNs - Part2.mp4

05:50

4. Demo - Drop Rows or Columns with a Number of NaNs - Part1.mp4

03:42

5. Demo - Drop Rows or Columns with a Number of NaNs - Part2.mp4

03:28

6. Demo - Drop Rows or Columns with a Number of NaNs - Part3.mp4

00:45

7. Demo - Using Fillers to Replace NaNs.mp4

05:27

8. Key Takeaways and Tips.mp4

01:03

4. Handling Duplicate Values in Your Data

1. Demo - Identify Duplicate Rows with duplicated.mp4

05:04

2. Demo - Drop Rows if Some Columns Are Duplicate with drop_duplicates.mp4

03:19

3. Key Takeaways and Tips.mp4

00:57

5. Handling Invalid Values in Your Data

1. Demo - Using Functions with Apply to Detect Invalid Rows and Fix Them - Part1.mp4

04:34

2. Demo - Using Functions with Apply to Detect Invalid Rows and Fix Them - Part2.mp4

02:51

3. Demo - Using Replace to Make Data Valid.mp4

05:08

4. Key Takeaways and Tips.mp4

02:02

Description

Cleaning data is one of those tasks that is not fancy, but key to any data application. This course will teach you the skills and knowledge of data cleaning in Pandas needed to convert your datasets from raw and useless to clean and useful.

What You'll Learn?

Regardless of your line of work; data is everywhere. Today, we generate more data per second than ever before; however, this data is usually raw, dirty, and frequently unusable.

In this course, Manage Invalid, Duplicate, and Missing Data in Python, you’ll gain the ability to clean your data to make it usable for any application you may need.

First, you’ll explore how to handle missing values and how to fill NaN columns.

Next, you’ll discover how to deal with duplicate rows on a subset of columns.

Finally, you’ll learn how to cope with invalid values and how to fix or remove them.

When you’re finished with this course, you’ll have the skills and knowledge of data cleaning in Pandas needed to convert your datasets from raw and useless to clean and useful.

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Python

Axel Sirota

Instructor's Courses

Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O'Reilly Media.

Pluralsight

View courses Pluralsight

Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.