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Joining Data with pandas

4.2+
70 reviews
Intermediate

Learn to combine data from multiple tables by joining data together using pandas.

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4 Hours15 Videos51 Exercises105,046 Learners4050 XPData Analyst with Python TrackData Manipulation with Python TrackData Scientist with Python TrackData Scientist Professional with Python Track

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Course Description

Being able to combine and work with multiple datasets is an essential skill for any aspiring Data Scientist. pandas is a crucial cornerstone of the Python data science ecosystem, with Stack Overflow recording 5 million views for pandas questions. Learn to handle multiple DataFrames by combining, organizing, joining, and reshaping them using pandas. You'll work with datasets from the World Bank and the City Of Chicago. You will finish the course with a solid skillset for data-joining in pandas.
  1. 1

    Data Merging Basics

    Free

    Learn how you can merge disparate data using inner joins. By combining information from multiple sources you’ll uncover compelling insights that may have previously been hidden. You’ll also learn how the relationship between those sources, such as one-to-one or one-to-many, can affect your result.

    Play Chapter Now
    Inner join
    50 xp
    What column to merge on?
    50 xp
    Your first inner join
    100 xp
    Inner joins and number of rows returned
    100 xp
    One-to-many relationships
    50 xp
    One-to-many classification
    100 xp
    One-to-many merge
    100 xp
    Merging multiple DataFrames
    50 xp
    Total riders in a month
    100 xp
    Three table merge
    100 xp
    One-to-many merge with multiple tables
    100 xp
  2. 2

    Merging Tables With Different Join Types

    Take your knowledge of joins to the next level. In this chapter, you’ll work with TMDb movie data as you learn about left, right, and outer joins. You’ll also discover how to merge a table to itself and merge on a DataFrame index.

    Play Chapter Now
    Left join
    50 xp
    Counting missing rows with left join
    100 xp
    Enriching a dataset
    100 xp
    How many rows with a left join?
    50 xp
    Other joins
    50 xp
    Right join to find unique movies
    100 xp
    Popular genres with right join
    100 xp
    Using outer join to select actors
    100 xp
    Merging a table to itself
    50 xp
    Self join
    100 xp
    How does pandas handle self joins?
    50 xp
    Merging on indexes
    50 xp
    Index merge for movie ratings
    100 xp
    Do sequels earn more?
    100 xp
  3. 3

    Advanced Merging and Concatenating

    In this chapter, you’ll leverage powerful filtering techniques, including semi-joins and anti-joins. You’ll also learn how to glue DataFrames by vertically combining and using the pandas.concat function to create new datasets. Finally, because data is rarely clean, you’ll also learn how to validate your newly combined data structures.

    Play Chapter Now
    Filtering joins
    50 xp
    Steps of a semi join
    100 xp
    Performing an anti join
    100 xp
    Performing a semi join
    100 xp
    Concatenate DataFrames together vertically
    50 xp
    Concatenation basics
    100 xp
    Concatenating with keys
    100 xp
    Verifying integrity
    50 xp
    Validating a merge
    50 xp
    Concatenate and merge to find common songs
    100 xp
  4. 4

    Merging Ordered and Time-Series Data

    In this final chapter, you’ll step up a gear and learn to apply pandas' specialized methods for merging time-series and ordered data together with real-world financial and economic data from the city of Chicago. You’ll also learn how to query resulting tables using a SQL-style format, and unpivot data using the melt method.

    Play Chapter Now
    Using merge_ordered()
    50 xp
    Correlation between GDP and S&P500
    100 xp
    Phillips curve using merge_ordered()
    100 xp
    merge_ordered() caution, multiple columns
    100 xp
    Using merge_asof()
    50 xp
    Using merge_asof() to study stocks
    100 xp
    Using merge_asof() to create dataset
    100 xp
    merge_asof() and merge_ordered() differences
    100 xp
    Selecting data with .query()
    50 xp
    Explore financials with .query()
    50 xp
    Subsetting rows with .query()
    100 xp
    Reshaping data with .melt()
    50 xp
    Select the right .melt() arguments
    50 xp
    Using .melt() to reshape government data
    100 xp
    Using .melt() for stocks vs bond performance
    100 xp
    Course wrap-up
    50 xp

In the following tracks

Data Analyst with PythonData Manipulation with PythonData Scientist with PythonData Scientist Professional with Python

Datasets

Chicago WardsChicago Business LicensesChicago CensusChicago Demographics by Zip CodeChicago Business OwnersChicago Land UseChicago Taxi VehiclesChicago Taxi OwnersCTA RidershipCTA CalendarCTA StationsMoviesMovie ActorsMovie RatingsMovie CastsMovie CrewsMovie GenresMovie SequelsMovie Financial DataMovie Tag LinesS&P 500World Bank GDPWorld Bank Population

Collaborators

Amy Peterson
Maggie Matsui

Prerequisites

Data Manipulation with pandas
Aaren Stubberfield Headshot

Aaren Stubberfield

Manager, Supply Chain Analytics @ Ingredion Incorporated

Manager of Supply Chain Analytics, with over 7 years of experience analyzing data to find insight for business related questions. I am responsible Supply Chain related Analytics for the NA business for $5.8 billion ingredient solutions provider to the food, beverage, brewing and pharmaceutical sectors. I graduated from DePaul University with distinction and received a MS in Predictive Analytics. I am passionate about Data Science / Machine Learning and I continue to work on my craft by learning new concepts through online classes.
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  • Alaeddine B.
    5 days

    Very good detailed course

  • K K.
    8 days

    I have learnt many useful techniques during this course.

  • Louis O.
    10 days

    Great topic

  • Thomas S.
    10 days

    Clear. Informative. Excellent learning environment.

  • Rodrigo A.
    18 days

    It is a good course

    • Loading ...

"Very good detailed course"

Alaeddine B.

"I have learnt many useful techniques during this course."

K K.

"Great topic"

Louis O.

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