Pandas Joins for Spreadsheet Users
Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.
- 4 hours
- 12 Videos
- 44 Exercises
- 1,215 Participants
- 3,700 XP
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Course Description
Joining two or more datasets is necessary for almost any real-world analysis. You’ve done it before with spreadsheets using VLOOKUP and related functions. Can you build on this experience as you transition to the world of Python? Yes! In this course you will learn the ins and outs of bringing datasets together with pandas, Python’s gold standard for manipulating tabular data. You’ll apply pandas functions to combine data from the National Football League (NFL) framed in a familiar spreadsheet environment. Armed with these skills you will be able to harness the power of pandas and integrate larger, more complex datasets into any analysis.
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1
Introduction to joining data
FreeIn this chapter, we'll build a foundation for using pandas to join data. You'll learn about the types of joins and how pandas can improve your effectiveness and productivity.
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One-to-many joins
In this chapter, we'll focus on one-to-many relationships. You'll practice identifying the relationship of key columns and joining data frames by column. You'll also learn how to join two or more data frames based on their indices.
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VLOOKUP-style joins
In this chapter, you'll learn how to use pandas for joining data in a way similar to using VLOOKUP formulas in a spreadsheet. You'll learn about three types of joins and then focus on the first type, one-to-one joins.
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Advanced joins
In the final chapter, you'll learn advanced joining techniques to use when faced with challenging data. You'll be presented with a challenge of your own in the form of a case study that tests your skills.
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1
Introduction to joining data
FreeIn this chapter, we'll build a foundation for using pandas to join data. You'll learn about the types of joins and how pandas can improve your effectiveness and productivity.
-
VLOOKUP-style joins
In this chapter, you'll learn how to use pandas for joining data in a way similar to using VLOOKUP formulas in a spreadsheet. You'll learn about three types of joins and then focus on the first type, one-to-one joins.
-
One-to-many joins
In this chapter, we'll focus on one-to-many relationships. You'll practice identifying the relationship of key columns and joining data frames by column. You'll also learn how to join two or more data frames based on their indices.
-
Advanced joins
In the final chapter, you'll learn advanced joining techniques to use when faced with challenging data. You'll be presented with a challenge of your own in the form of a case study that tests your skills.
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John Miller
Data Scientist
John Miller is a senior data scientist who helps companies use machine learning to improve operations. His favorite work involves building predictive models that provide insights into solving difficult problems. John also works as an expert witness and actively participates in the global AI community as a speaker and writer. He holds Master's degrees in business and engineering from MIT and a Bachelor's in engineering from the US Military Academy.
See MoreCollaborators
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Anneleen Beckers
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Ruanne Van Der Walt