Python pour la data science

Lino Galiana

doi:10.5281/zenodo.8229676

Pandas and Numpy, the first packages of our introductory journey, are essential for manipulating data. However, it is important not to overlook the fundamentals of the Python language when discovering it. A good understanding of the fundamental elements of the language helps to better grasp the logic of data science packages, understand the errors encountered, and results in greater productivity and freedom.

To explore basic objects and the structure of the language, a series of notebooks is provided below. The course is flexible; you can work through these notebooks in any order or only complete parts of them if you are already familiar with some of the content.

Once you’ve reviewed the material, you will find summary exercises to help you put your knowledge of data structures into practice. Once you have done these, you will find the rest of the course in the “Handling data” section.

1 Review notebooks

await teaserList(listeChapters)

isEnglish = window.location.href.includes("/en/")
lang = (isEnglish) ? "en" : "fr"

textButton = (isEnglish) ? "VSCode" : "VSCode"
textButtonTest = (isEnglish) ? "Try on SSP Cloud" : "Tester sur SSP Cloud"
textButtonRead = (isEnglish) ? "Read" : "Lire"

url = "https://minio.lab.sspcloud.fr/lgaliana/python-ENSAE/inputs/educationalresources.json"
content = d3.json(url)
fondamentaux = content.parts.filter(part => part.name.fr == "Fondamentaux du langage")
listeChapters = fondamentaux[0].parts

function renderTeaser(doc) {
  const { name, abstract, deploymentUrl, articleUrl, tags, authors } = doc;

  const badgeTest = isEnglish
    ? "https://custom-icon-badges.demolab.com/badge/SSP%20Cloud-Try-blue?logo=vsc&logoColor=white"
    : "https://custom-icon-badges.demolab.com/badge/SSP%20Cloud-Tester-blue?logo=vsc&logoColor=white";

  const badgeRead = isEnglish
    ? "https://custom-icon-badges.demolab.com/badge/Read-Online-ffa500?logo=book&logoColor=white"
    : "https://custom-icon-badges.demolab.com/badge/Lire-En%20ligne-ffa500?logo=book&logoColor=white";


  const tagsHTML = tags
    .map((tag) => `<span class="topic" style="margin-right: 8px; border: 2px solid #C4C4C4; border-radius: 5px; width: fit-content;">${tag}</span>`)
    .join("");

  const authorsHTML = authors
    .map((author) => `<span class="author" style="margin-right: 8px;">${author}</span>`)
    .join("");

  const localizedName = (lang === "en") ? name.en : name.fr;
  const localizedDeploymentUrl = (lang === "en") ? deploymentUrl.vscode.en : deploymentUrl.vscode.fr;
  const localizedAbstract = (lang === "en") ? abstract.en : abstract.fr;
  const localizedArticleUrl = (lang === "en") ? articleUrl.en : articleUrl.fr;


  return `
    <div class="gridcard-item">
      <div class="gridcard-body">
        <a class="gridcard-title" href="${localizedDeploymentUrl}">${localizedName}</a>
        ${localizedAbstract ? `<p class="gridcard-description">${localizedAbstract}</p>` : ""}

          <a class="launch-badge"
            href="${localizedDeploymentUrl}"
            target="_blank"
            rel="noopener noreferrer"
            aria-label="Tester sur SSP Cloud">
            <img src="${badgeTest}" alt="Tester sur SSP Cloud" loading="lazy">
          </a>

          <a class="launch-badge"
            href="${localizedArticleUrl}"
            target="_blank"
            rel="noopener noreferrer"
            aria-label="Lire en ligne">
            <img src="${badgeRead}" alt="Lire en ligne" loading="lazy">
          </a>
        </div>

      </div>
    </div>
  `;
}

function teaserList(docs, css = THEME_TEASERS) {
  const id = DOM.uid().id;
  return html`<div id="${id}">
    <div class=listing-grid>${docs.map(doc => renderTeaser(doc))}
  `;
}

import { THEME_TEASERS, ICON_LIKE, getScopedStyle } from "@mootari/notebook-teasers";

2 Synthesis exercises

To make sure you are fluent on data structures and operations in Python for different problems, here is a series of exercises.

They require you to think hard about the appropriate data structure to answer - that’s normal!

For once, the exercises are done directly on this page rather than via notebooks¹. The correction will soon be available on the dedicated page.

2.1 Exercice 1

Exercice 1

You have at your disposal a list of quotations from the magnificent French literary heritage:

See the list of quotations that will be useful

Citation	Auteur
“Rien ne sert de courir ; il faut partir à point.”	La Fontaine
“Selon que vous serez puissant ou misérable, les jugements de cour vous rendront blanc ou noir.”	La Fontaine
“Heureux qui comme Ulysse a fait bon voyage”	Du Bellay
“L’homme est né libre, et partout il est dans les fers.”	Rousseau
“Parce que c’était lui, parce que c’était moi.”	Montaigne
“La première fois qu’Aurélien vit Bérénice, il la trouva franchement laide”	Aragon
“Aujourd’hui maman est morte. Ou peut-être hier, je ne sais pas.”	Camus

Create a citations object that uses a suitable data structure in Python to:

Easily retrieve all quotations associated with an author. Create a citation_aragon object that tests the validity of your approach with the Aragon example.
For each quotation, count the number of words and the number of unique words. Create a stats_phrases object that lists these properties. Test on La Fontaine quotations.

Here are the quotes, in bulk, to get you started.

In this cell, create the appropriate data structure for this exercise.

For question 1, you can use this cell.

For question 2, you can use this cell.

Hint 2

Adopt this general structure :

for auteur, liste_citations in citations.items():
    # do something

How to have the length of a sentence (sequence of words) and single words in it?

Warning

Ideally, you should pay attention to punctuation.

But we have not yet learned how to make refined substitutions in strings - that will come in another chapter.

2.2 Exercise 2

Advent of Code challenges are excellent practical problems for learning algorithms. With one problem per day of increasing difficulty between 1 December and Christmas Day, you will quickly become comfortable with the many data structures offered by Python.

The next exercise proposes solving the first part of day 1 (year 2022) using only basic Python.

Warning

It would be appropriate to use Numpy for the next problem. But the purpose of the next chapter is to show how this package simplifies basic numerical operations.

Here are the objects we will need for this exercise:

You can write your code for question 2 in this cell:

calories_example

You can write your code for question 3 in this cell:

Finally, after generalizing, you should find the following values:

Elf number 217 carries the most calories (72602 calories).

2.3 Exercise 3: strings

This is the final pattern you should end up in question 1 and 2

On 17 March 2022, Nadia (34) reported an income of $33,333.33.

2.4 Exercise 3

Using the format method, try to obtain the expected result.
Do the same with f-strings.
Modify the message to create a monthly income rounded to 1 digit. Use a space as the thousands separator rather than the default separator (the comma).
Here is a fictitious path C:\Users\Nadia\Documents\cours\chapitre_03\notes.txt to a file. Make a correct print of it.

In the first part of this exercise, you should have these results.

Question 1:
On 17 March 2022, Nadia (34) reported an income of $33,333.33.
Question 2:
On 17 March 2022, Nadia (34) reported an income of $33,333.33.
Question 3:
On 17 March 2022  Nadia (34) reported an income of $2 777.8.

Try replicating these results using interactive cell below

In the second part of the exercise, if you use the wrong type of string, you should get an error:

  Cell In[14], line 2
    chemin_bad = "C:\Users\Nadia\Documents\cours\chapitre_03\notes.txt"
                 ^
SyntaxError: (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \UXXXXXXXX escape

Slashes often cause problems. With raw strings, you should not encounter this problem:

File can be found here: C:\Users\Nadia\Documents\cours\chapitre_03\notes.txt

Informations additionnelles

Python environment

This site was built automatically through a Github action using the Quarto reproducible publishing software (version 1.8.26).

The environment used to obtain the results is reproducible via uv. The pyproject.toml file used to build this environment is available on the linogaliana/python-datascientist repository

pyproject.toml

[project]
name = "python-datascientist"
version = "0.1.0"
description = "Source code for Lino Galiana's Python for data science course"
readme = "README.md"
requires-python = ">=3.13,<3.14"
dependencies = [
    "altair>=6.0.0",
    "black==24.8.0",
    "cartiflette",
    "contextily==1.6.2",
    "duckdb>=0.10.1",
    "folium>=0.19.6",
    "gdal!=3.11.1",
    "geoplot==0.5.1",
    "graphviz==0.20.3",
    "great-tables>=0.12.0",
    "gt-extras>=0.0.8",
    "ipykernel>=6.29.5",
    "jupyter>=1.1.1",
    "jupyter-cache==1.0.0",
    "kaleido==0.2.1",
    "langchain-community>=0.3.27",
    "loguru==0.7.3",
    "markdown>=3.8",
    "nbclient==0.10.0",
    "nbformat==5.10.4",
    "nltk>=3.9.1",
    "pip>=25.1.1",
    "plotly>=6.1.2",
    "plotnine>=0.15",
    "polars==1.8.2",
    "pyarrow>=17.0.0",
    "pynsee==0.1.8",
    "python-dotenv==1.0.1",
    "python-frontmatter>=1.1.0",
    "pywaffle==1.1.1",
    "requests>=2.32.3",
    "scikit-image==0.24.0",
    "scipy>=1.13.0",
    "selenium<4.39.0",
    "spacy>=3.8.4",
    "webdriver-manager==4.0.2",
    "wordcloud==1.9.3",
]

[tool.uv.sources]
cartiflette = { git = "https://github.com/inseefrlab/cartiflette" }
gdal = [
  { index = "gdal-wheels", marker = "sys_platform == 'linux'" },
  { index = "geospatial_wheels", marker = "sys_platform == 'win32'" },
]

[[tool.uv.index]]
name = "geospatial_wheels"
url = "https://nathanjmcdougall.github.io/geospatial-wheels-index/"
explicit = true

[[tool.uv.index]]
name = "gdal-wheels"
url = "https://gitlab.com/api/v4/projects/61637378/packages/pypi/simple"
explicit = true

[dependency-groups]
dev = [
    "nb-clean>=4.0.1",
]

To use exactly the same environment (version of Python and packages), please refer to the documentation for uv.

File history

md`This file has been modified __${table_commit.length}__ times since its creation on ${creation_string} (last modified on ${last_modification_string})`

html`<div>${git_history_table}</div>`

html`<div>${git_history_plot}</div>`

SHA	Date	Author	Description
8cfd74d9	2025-12-26 13:57:40	Lino Galiana	Adding fstring/rstring exercise (#671)
086116d6	2025-12-23 13:35:15	lgaliana	Fix problem with script location
7b8b7f9b	2025-12-15 11:01:54	lgaliana	Traduction anglaise
8e387110	2025-12-15 10:20:42	Lino Galiana	Fix WASM build in GHA (#663)
02abcf02	2025-12-14 22:52:19	Lino Galiana	Intro à Python: des exercices sur les structures de données (#662)
81837397	2025-09-26 15:13:01	lgaliana	deployment url from vscode for intro notebooks
91431fa2	2025-06-09 17:08:00	Lino Galiana	Improve homepage hero banner (#612)
dac49604	2024-08-29 15:07:49	linogaliana	Change URL on edit on github button
f8b04136	2024-08-28 15:15:04	Lino Galiana	Révision complète de la partie introductive (#549)

creation = d3.min(
  table_commit.map(d => new Date(d.Date))
)

last_modification = d3.max(
  table_commit.map(d => new Date(d.Date))
)

creation_string = creation.toLocaleString("fr", {
  "day": "numeric",
  "month": "long",
  "year": "numeric"
})

last_modification_string = last_modification.toLocaleString("fr", {
  "day": "numeric",
  "month": "long",
  "year": "numeric"
})

git_history_table = Inputs.table(
  table_commit,
  {
    format: {
      SHA: x => md`[${x}](${github_repo}/commit/${x})`,
      Description: x => md`${replacePullRequestPattern(x, github_repo)}`,
      /*Date: x => x.toLocaleString("fr", {
        "month": "numeric",
        "day": "numeric",
        "year": "numeric"
        })
      */
    }
  }
)

git_history_plot = Plot.plot({
  marks: [
    Plot.ruleY([0], {stroke: "royalblue"}),
    Plot.dot(
          table_commit,
          Plot.pointerX({x: (d) => new Date(d.date), y: 0, stroke: "red"})),
    Plot.dot(table_commit, {x: (d) => new Date(d.Date), y: 0, fill: "royalblue"})
  ]
})

function replacePullRequestPattern(inputString, githubRepo) {
    // Use a regular expression to match the pattern #digit
    var pattern = /#(\d+)/g;

    // Replace the pattern with ${github_repo}/pull/#digit
    var replacedString = inputString.replace(pattern, '[#$1](' + githubRepo + '/pull/$1)');

    return replacedString;
}

github_repo = "https://github.com/linogaliana/python-datascientist"

table_commit = {

// Get the HTML table by its class name
var table = document.querySelector('.commit-table');

// Check if the table exists
if (table) {
    // Initialize an array to store the table data
    var dataArray = [];

    // Extract headers from the first row
    var headers = [];
    for (var i = 0; i < table.rows[0].cells.length; i++) {
        headers.push(table.rows[0].cells[i].textContent.trim());
    }

    // Iterate through the rows, starting from the second row
    for (var i = 1; i < table.rows.length; i++) {
        var row = table.rows[i];
        var rowData = {};

        // Iterate through the cells in the row
        for (var j = 0; j < row.cells.length; j++) {
            // Use headers as keys and cell content as values
            rowData[headers[j]] = row.cells[j].textContent.trim();
        }

        // Push the rowData object to the dataArray
        dataArray.push(rowData);
    }
  }

  return dataArray

}

// Get the element with class 'git-details'
{
  var gitDetails = document.querySelector('.commit-table');

  // Check if the element exists
  if (gitDetails) {
      // Hide the element
      gitDetails.style.display = 'none';
  }
}

Plot = require('@observablehq/plot@0.6.12/dist/plot.umd.min.js')

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Footnotes

It is possible to run Python on the browser thanks to its implementation in WASM via Pyodide. This is not a recommended method when you need to perform big calculations, as in the next chapters. But since we are working on small examples here, this is quite practical to benefit from the interactivity enabled by the browser, notably the helpers in case ou are blocked.↩︎

Citation

BibTeX citation:

@book{galiana2025,
  author = {Galiana, Lino},
  title = {Python Pour La Data Science},
  date = {2025},
  url = {https://pythonds.linogaliana.fr/},
  doi = {10.5281/zenodo.8229676},
  langid = {en}
}

For attribution, please cite this work as:

Galiana, Lino. 2025. Python Pour La Data Science. https://doi.org/10.5281/zenodo.8229676.