Working With Missing And Duplicate Data

Learn how to work with missing and duplicate data in pandas.


  • Learn techniques for dropping rows and columns with missing data.
  • Learn how to impute values to replicate missing data.
  • Learn how to identify and drop duplicate rows.

Mission Outline

1. Introduction
2. Identifying Missing Values
3. Correcting Data Cleaning Errors that Result in Missing Values
4. Visualizing Missing Data
5. Using Data From Additional Sources to Fill in Missing Values
6. Identifying Duplicates Values
7. Correcting Duplicates Values
8. Handle Missing Values by Dropping Columns
9. Handle Missing Values by Dropping Columns Continued
10. Analyzing Missing Data
11. Handling Missing Values with Imputation
12. Dropping Rows
13. Next steps
14. Takeaways


Course Info:

Data Cleaning and Analysis


The median completion time for this course is 7.2 hours

This course requires a basic subscription and includes five missions and one guided project.  It is the sixth course in the Data Analyst in Python path and Data Scientist in Python path.


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