Manage Invalid, Duplicate, and Missing Data in Python

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Why Clean Data

1. Introduction

2. Getting the Best out of This Course

3. Version Check

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

5. Outline of the Course

3. Handling Missing Values in Your Data

1. Recap - Using Indexers in Pandas

2. Demo - Identify NaNs - Part1

3. Demo - Identify NaNs - Part2

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

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

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

7. Demo - Using Fillers to Replace NaNs

8. Key Takeaways and Tips

4. Handling Duplicate Values in Your Data

1. Demo - Identify Duplicate Rows with duplicated

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

3. Key Takeaways and Tips

5. Handling Invalid Values in Your Data

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

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

3. Demo - Using Replace to Make Data Valid

4. Key Takeaways and Tips

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تولید کننده: PluralSight