Data Wrangling and Cleaning with Pandas

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Data Wrangling and Cleaning with Pandas

Instructor: Matt Bushby

Included with Learn more

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

5 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Select rows and subsets of data using loc, iloc and Boolean masks.
Identify missing values and apply repeatable data-cleaning methods with Pandas.
Explain how to combine information from multiple tables when preparing data for analysis.

Skills you'll gain

Tools you'll learn

Pandas (Python Package)

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There are 3 modules in this course

Prepare tabular data for analysis using Python and Pandas. This course builds on your knowledge of DataFrames and Series to help you select relevant records, work with missing values and combine information from multiple tables.

You will use loc and iloc to select rows by label and position, then construct Boolean masks to filter data according to specific conditions. You will also examine missing-value representations, including None and NaN, and use functions such as fillna to make your cleaning steps repeatable and transparent. By the end of the course, you will be able to extract useful subsets of data, apply basic cleaning methods and explain how joining tables supports data preparation.

This topic we look at two new pandas features: 'loc', 'iloc', and masks. 'loc' and 'iloc' allow us to look row-first instead of column-first, which is actually the more natural way to operate with tabular data. Masks will allow us to select a set of rows we are interested in based on values in other columns.

What's included

1 video1 assignment5 plugins

This topic you will learn about the different types of empty data you will encounter in a notebook, how to clean data in a repeatable and transparent way, and how to join multiple tables together.You will learn the difference between 'null', 'None', and 'NaN', use 'fillna' and its friends. You will also start on the joining journey.

What's included

1 assignment6 plugins

Learners prepare a synthetic regional library dataset for analysis. They select rows with loc and iloc, build and apply Boolean masks, find and clean missing and inconsistent values, merge two related tables into one DataFrame, and record every cleaning decision in a log. The result is a notebook that answers questions about overdue loans from the cleaned, combined data.

What's included

3 readings1 assignment

Instructor

Matt Bushby

Macquarie University

37 Courses34,324 learners

Offered by

Macquarie University

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