By: Blog by Bogumił Kamiński

Re-posted from: https://bkamins.github.io/julialang/2022/10/14/exercises.html

Introduction

My Julia for Data Analysis book will be soon published (now all its
chapters are already available in preview for free).

An important part of the book is its GitHub repository containing
all the codes used in the book and ensuring their reproducibility.

Since the book was prepared to fit one semester course on data analysis using
Julia I am now preparing supporting teaching materials that accompany it.

Today together with Daniel Kaszyński we have released first part of these
supporting materials. In the exercises folder of the
book’s GitHub repository we have added 130 exercises that should help you
master the material covered in the book.

The exercises are grouped by book chapter. There are 10 exercises for
each chapter. Each exercise has a proposed solution. We have prepared the
exercises so that they have a varying difficulty level. The exercises from
initial chapters should be relatively easy. However, to solve exercises
from the final chapters you might need to have a significant knowledge of
Julia’s ecosystem for data analysis.

In the post I use Julia 1.8.2, and DataFrames.jl 1.4.1.

A sample exercise

To have some concrete example of what a typical exercise is I have picked a
question that was asked today on Discourse that I liked. The
problem is stated as follows.

Consider the following data frame:

julia> using DataFrames

julia> df = DataFrame(country=["Poland", "Poland", "Canada", "Canada"],
                      city=["Olecko", "Ełk", "Toronto", "Mississauga"])
4×2 DataFrame
 Row │ country  city
     │ String   String
─────┼──────────────────────
   1 │ Poland   Olecko
   2 │ Poland   Ełk
   3 │ Canada   Toronto
   4 │ Canada   Mississauga

The task is to reduce it by unique value in country column. More specifically
we want to create a new data frame with two columns. One of them should be
country that will store unique values of country column in the source data
frame df. The second column should be cities that should store a vector
of values in the city column from df that correspond to a given country.

Now let me show three ways how you can do it using the combine function.
The key to the solution is the following rule of how combine works
(taken from the documentation):

In all of these cases, function can return either a single row or multiple
rows. As a particular rule, values wrapped in a Ref or a
0-dimensional AbstractArray are unwrapped and then treated as a single row.

This means that in order to make a vector to be treated as a single row we have
three options:

• wrap a vector in another vector as its single element (so we have a multi-row
  object but with a single row);
• wrap a vector in Ref;
• wrap a vector in a 0-dimensional AbstractArray, which can be done using the
  fill function.

So the three solutions to our problem are:

julia> combine(groupby(df, :country, sort=true), :city => (x -> [x]) => :cities)
2×2 DataFrame
 Row │ country  cities
     │ String   SubArray…
─────┼─────────────────────────────────────
   1 │ Canada   ["Toronto", "Mississauga"]
   2 │ Poland   ["Olecko", "Ełk"]

julia> combine(groupby(df, :country, sort=true), :city => Ref => :cities)
2×2 DataFrame
 Row │ country  cities
     │ String   SubArray…
─────┼─────────────────────────────────────
   1 │ Canada   ["Toronto", "Mississauga"]
   2 │ Poland   ["Olecko", "Ełk"]

julia> combine(groupby(df, :country, sort=true), :city => fill => :cities)
2×2 DataFrame
 Row │ country  cities
     │ String   SubArray…
─────┼─────────────────────────────────────
   1 │ Canada   ["Toronto", "Mississauga"]
   2 │ Poland   ["Olecko", "Ełk"]

Conclusions

I hope you will enjoy and benefit from doing the exercises that I have added
to the book’s GitHub repository.

Expect that soon two other sets of materials will be added to this repository:

• For each chapter you will get a notebook which can serve as a starting point
  to develop teaching materials for a given chapter.
• Several openly available notebooks with additional data analysis problems that
  are solved end-to-end. They will be prepared in a
  similar style to Hands-on Data Science with Julia notebooks and are
  meant to be studied as a follow-up material after you have studied the book.

When these are added I will post about it.