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  3. Data Manipulation in Julia
  • 1

    Inspecting DataFrames

    Free

    Take your first steps toward complex data manipulation in Julia! Learn how to personalize your experience with the DataFrames package and how to create data visualizations using the Plots package.

  • 2

    Working with columns

    Columns are the basic building blocks of DataFrames. Knowing how to handle columns is essential for your data manipulation journey. You'll learn how to reorder and drop columns, as well as how to apply functions to individual rows and whole columns.

  • 3

    Aggregating DataFrames

    In this chapter, you'll learn how to group data and calculate grouped summary statistics, as well as how to create pivot tables. You'll also learn how to improve the readability of your code with the Chain.jl package.

  • 4

    Improving Your Workflow

    Learn how to load and join datasets, as well as how to handle missing values. Then it's time to fit everything together!

Initializing

Exercise

Chocolate location vs. rating

Are chocolates from certain countries better than those from others? And what about different companies? You can use grouped summary statistics to find out!

The chocolates dataset and the DataFrames and Statistics packages have been loaded for you.

Instructions

100 XP
  • Group the chocolates DataFrame by company_location and company, saving the result as choc_groups.
  • Compute the minimum, median, and maximum of rating for each group, renaming the columns as min_rating, med_rating, and max_rating; save the result as choc_groups_stats.
  • Sort choc_groups_stats by the med_rating column in reverse order.