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Reshaping Data with pandas

4+
11 reviews
Intermediate

Reshape DataFrames from a wide to long format, stack and unstack rows and columns, and wrangle multi-index DataFrames.

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4 Hours15 Videos52 Exercises10,833 Learners

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Course Description

Often data is in a human-readable format, but it’s not suitable for data analysis. This is where pandas can help—it’s a powerful tool for reshaping DataFrames into different formats. In this course, you’ll grow your data scientist and analyst skills as you learn how to wrangle string columns and nested data contained in a DataFrame. You’ll work with real-world data, including FIFA player ratings, book reviews, and churn analysis data, as you learn how to reshape a DataFrame from wide to long format, stack and unstack rows and columns, and get descriptive statistics of a multi-index DataFrame.
  1. 1

    Introduction to Data Reshaping

    Free

    Let's start by understanding the concept of wide and long formats and the advantages of using each of them. You’ll then learn how to pivot data from long to a wide format, and get summary statistics from a large DataFrame.

    Play Chapter Now
    Wide and long data formats
    50 xp
    The long and the wide
    100 xp
    Flipping players
    100 xp
    Reshaping using pivot method
    50 xp
    Dribbling the pivot method
    100 xp
    Offensive or defensive player?
    100 xp
    Replay that last move!
    100 xp
    Pivot tables
    50 xp
    Reviewing the moves
    100 xp
    Exploring the big match
    100 xp
    The tallest and the heaviest
    100 xp
  2. 2

    Converting Between Wide and Long Format

    Master the technique of reshaping DataFrames from wide to long format. In this chapter, you'll learn how to use the melting method and wide to long function before discovering how to handle string columns by concatenating or splitting them.

    Play Chapter Now
    Reshaping with melt
    50 xp
    Gothic times
    100 xp
    Rating is not everything
    100 xp
    How is Frankenstein, Dorian Gray?
    100 xp
    Wide to long function
    50 xp
    The golden age
    100 xp
    Decrypting the code
    100 xp
    Time to read, Katniss!
    100 xp
    Working with string columns
    50 xp
    Did you say dystopia?
    100 xp
    What's your rating, Harry?
    100 xp
    Elementary, dear Watson!
    100 xp
  3. 3

    Stacking and Unstacking DataFrames

    In this chapter, you’ll level-up your data manipulation skills using multi-level indexing. You'll learn how to reshape DataFrames by rearranging levels of the row indexes to the column axis, or vice versa. You'll also gain the skills you need to handle missing data generated in the stacking and unstacking processes.

    Play Chapter Now
    Stacking DataFrames
    50 xp
    Stack the calls!
    100 xp
    Phone directory index
    100 xp
    Text me!
    100 xp
    Unstacking DataFrames
    50 xp
    International caller
    100 xp
    Call another time
    100 xp
    Organizing your voicemail
    100 xp
    Working with multiple levels
    50 xp
    Swap your SIM card
    100 xp
    Two many calls
    100 xp
    Handling missing data
    50 xp
    A missed phone call
    100 xp
    Don't drop the stack
    100 xp
  4. 4

    Advanced Reshaping

    You'll finish by learning how to combine the reshaping process with grouping to produce quick data manipulations. Lastly, you'll discover how to transform list-like columns and handle complex nested data, such as nested JSON files.

    Play Chapter Now
    Reshaping and combining data
    50 xp
    Less fast food, please!
    100 xp
    Only going up
    100 xp
    A group analysis
    100 xp
    Transforming a list-like column
    50 xp
    Merge it all
    100 xp
    Explode the bounds
    100 xp
    The good old split
    100 xp
    Reading nested data into a DataFrame
    50 xp
    Nested movies
    100 xp
    A complex film
    100 xp
    Dealing with nested data columns
    50 xp
    Un-nesting birds
    100 xp
    Don't dump the bird
    100 xp
    The final reshape
    50 xp

In the following tracks

Data Manipulation with PythonImporting & Cleaning Data with Python

Datasets

Customer churn dataBooks dataFIFA players dataObesity data

Collaborators

Amy Peterson
Maggie Matsui

Prerequisites

Data Manipulation with pandas
Maria Eugenia Inzaugarat HeadshotMaria Eugenia Inzaugarat

Data Scientist and Artificial Intelligence Consultant

Eugenia is a passionate, dedicated, and proactive data scientist and Artificial Intelligence Consultant that enjoys not only doing machine learning projects but also telling stories with data. She obtained a Ph.D. from the University of Buenos Aires. She has taught university courses in mathematics and biology as well as online courses on Data Science. Having transitioned from an academic background into data science, Eugenia loves teaching concepts related to python programming, data science, and machine learning to help others also gain knowledge about these fields.
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  • Helmut D.
    6 days

    Just great

  • Andreas N.
    19 days

    Great to follow and a lot learned!

  • Evgeni N.
    about 1 month

    I have leared a lot!

  • José C.
    4 months

    Very interesting.

  • Ramana T.
    4 months

    Very good slides and exercises. Very ready to remember the content.

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"Just great"

Helmut D.

"Great to follow and a lot learned!"

Andreas N.

"I have leared a lot!"

Evgeni N.

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