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.

Wide and long data formats

The long and the wide

Flipping players

Reshaping using pivot method

Dribbling the pivot method

Offensive or defensive player?

Replay that last move!

Pivot tables

Reviewing the moves

Exploring the big match

The tallest and the heaviest

Introduction to Data Reshaping

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.

Reshaping with melt

Gothic times

Rating is not everything

How is Frankenstein, Dorian Gray?

Wide to long function

The golden age

Decrypting the code

Time to read, Katniss!

Working with string columns

Did you say dystopia?

What's your rating, Harry?

Elementary, dear Watson!

Converting Between Wide and Long Format

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.

Stacking DataFrames

Stack the calls!

Phone directory index

Text me!

Unstacking DataFrames

International caller

Call another time

Organizing your voicemail

Working with multiple levels

Swap your SIM card

Two many calls

Handling missing data

A missed phone call

Don't drop the stack

Stacking and Unstacking DataFrames

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.

Reshaping and combining data

Less fast food, please!

Only going up

A group analysis

Transforming a list-like column

Merge it all

Explode the bounds

The good old split

Reading nested data into a DataFrame

Nested movies

A complex film

Dealing with nested data columns

Un-nesting birds

Don't dump the bird

The final reshape

Advanced Reshaping

Customer churn data

Books data

FIFA players data

Obesity data

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.

Data Manipulation with pandas

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

Reshaping Data with pandas

Datenbearbeitung  in Python

Datenimport/-bereinigung  in Python

Data Scientist

List-like columns

Transforming list-like columns

The .explode() method

Exploding a column

Exploding a column in the DataFrame

Empty lists

Chaining operations

Let's practice!

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Advanced Reshaping - Transforming a list-like column

Transforming a list-like column

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