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Pandas Joins for Spreadsheet Users

Learn how to effectively and efficiently join datasets in tabular format using the Python Pandas library.

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4 Hours12 Videos44 Exercises3,104 Learners

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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.
  1. 1

    Introduction to joining data

    Free

    In 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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    Joining data: a real-world necessity
    50 xp
    The need for joining data
    50 xp
    Working with split data
    100 xp
    Working with complementary data
    100 xp
    Concatenation
    50 xp
    Concatenating rows
    100 xp
    Concatenating rows with duplicated indexes
    100 xp
    Concatenating columns
    100 xp
    Power and flexibility
    50 xp
    Advantages of pandas joins
    100 xp
    Simple coding for complex merges
    100 xp
  2. 2

    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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    Types of joins
    50 xp
    One-to-one joins
    100 xp
    One-to-many joins
    100 xp
    Many-to-many joins
    100 xp
    A closer look at one-to-one joins
    50 xp
    Unscrambling the framework.
    100 xp
    Replicating VLOOKUP
    100 xp
    Merging on two or more keys
    100 xp
    Combining common data with inner joins
    50 xp
    Object-oriented merges
    100 xp
    Basic inner joins
    100 xp
    Dealing with different names
    100 xp
    Choosing the correct join method
    100 xp
  3. 3

    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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    "Out of many, one"
    50 xp
    Framework part 2: one-to-many merges
    100 xp
    Identifying one-to-many relationships
    100 xp
    Joining on key columns
    50 xp
    Checking for duplicate keys
    100 xp
    Completing a one-to-many merge
    100 xp
    Index-based joins
    50 xp
    Joining on index
    100 xp
    Joining multiple tables
    100 xp
    Reviewing the one-to-many join
    50 xp
  4. 4

    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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    Joining data in real life
    50 xp
    Mixing indexes and columns
    100 xp
    Suffixes and indicators
    100 xp
    Working with time data
    50 xp
    Combining time series
    100 xp
    Matching to the nearest time
    100 xp
    Recap and case study
    50 xp
    Case study challenge - part 1
    100 xp
    Case study challenge - part 2
    100 xp
    Case study challenge - part 3
    100 xp

Datasets

Games PlaysPlayers draftCurrent playersNGSGamesNew weather data

Collaborators

Anneleen Beckers
Ruanne Van Der Walt

Prerequisites

Python for Spreadsheet Users
John Miller HeadshotJohn 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.
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