Course overview

In this course, you’ll learn how to reduce the memory footprint of a pandas DataFrame while working with data from the Museum of Modern Art. You’ll learn how to work with DataFrame chunks, how to use them to increase processing speed in pandas, and how to optimize DataFrame types while exploring data from the Lending Club. You’ll also learn how to augment pandas with SQLite to combine the best of both tools. Finally, you’ll learn when to use disk space over in-memory space, as well as how to run SQL queries using pandas.

Best of all, you’ll learn by doing — you’ll practice and get feedback directly in the browser. At the end of the course, you’ll complete a project that asks you to work on a real-life example — using the pandas SQLite workflow to analyze startup fundraising deals using data from CrunchBase.

Key skills

    • Reducing the memory footprint of a pandas DataFrame
    • Processing large DataFrames in chunks using SQLite

Course outline

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Projects in this course

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