Python pour la data science

Lino Galiana

doi:10.5281/zenodo.8229676

Seules les chapitres dont les corrections ne sont pas apparentes sont listés sur cette page.

defaultPrint = (isBetweenSeptAndDec() === false)

1 Partie 1: manipuler des données

Retour sur Numpy

Introduction à Pandas

Manipulations de données avec Pandas

Exercices Geopandas

Exercices webscraping

Exercices expressions régulières

Exercices API

2 Partie 2: visualiser les données

Exercices graphiques classiques

Exercices sur la cartographie

4 Partie 4: Natural Language Processing (NLP)

Découverte des enjeux de nettoyage de texte

L’approche bag of words pour synthétiser l’information textuelle

Les embeddings et la modélisation du langage

Informations additionnelles

Python environment

This site was built automatically through a Github action using the Quarto reproducible publishing software (version 1.7.33).

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 = ">=3.12,<3.13"
dependencies = [
    "altair==5.4.1",
    "black==24.8.0",
    "cartiflette",
    "contextily==1.6.2",
    "duckdb>=0.10.1",
    "folium>=0.19.6",
    "geoplot==0.5.1",
    "graphviz==0.20.3",
    "great-tables==0.12.0",
    "ipykernel>=6.29.5",
    "jupyter>=1.1.1",
    "jupyter-cache==1.0.0",
    "kaleido==0.2.1",
    "langchain-community==0.3.9",
    "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==0.13.6",
    "polars==1.8.2",
    "pyarrow==17.0.0",
    "pynsee==0.1.8",
    "python-dotenv==1.0.1",
    "pywaffle==1.1.1",
    "requests>=2.32.3",
    "scikit-image==0.24.0",
    "scipy==1.13.0",
    "spacy==3.8.4",
    "webdriver-manager==4.0.2",
    "wordcloud==1.9.3",
    "xlrd==2.0.1",
    "yellowbrick==1.5",
]

[tool.uv.sources]
cartiflette = { git = "https://github.com/inseefrlab/cartiflette" }

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
f6b3bbc9	2025-01-26 18:17:36	Lino Galiana	Upgrading uv workflow (#590)
a524c2c1	2024-12-17 13:44:35	lgaliana	Toutes les corrections
cc9a2d07	2024-12-10 09:36:21	lgaliana	Renomme les corrections
7a52adb5	2024-12-10 08:59:22	lgaliana	prépare ajout correction
5ff770b5	2024-12-04 10:07:34	lgaliana	Partie ML plus esthétique
d22ec215	2024-11-13 16:21:30	lgaliana	Corrections
cb187599	2024-11-01 09:38:00	lgaliana	Broken url for corrected exercises
00f28a35	2024-10-15 13:41:20	lgaliana	Ajoute questions collaboratifs Git
c326488c	2024-10-10 14:31:57	Romain Avouac	Various fixes (#565)
bd374ba9	2024-09-24 20:05:55	lgaliana	install altair & correction numpy
4640e6da	2024-09-18 11:53:05	linogaliana	corrections
005d89b8	2023-12-20 17:23:04	Lino Galiana	Finalise l’affichage des statistiques Git (#478)
4c1c22d5	2023-12-10 11:50:56	Lino Galiana	Badge en javascript plutôt (#469)
417fb669	2023-12-04 18:49:21	Lino Galiana	Corrections partie ML (#468)
1f23de28	2023-12-01 17:25:36	Lino Galiana	Stockage des images sur S3 (#466)
a06a2689	2023-11-23 18:23:28	Antoine Palazzolo	2ème relectures chapitres ML (#457)
8728352d	2023-10-24 11:50:08	Lino Galiana	Correction coquilles geopandas (#444)
6362a077	2023-10-10 15:08:33	linogaliana	Ajoute corrections
241cab30	2023-10-04 17:53:29	Lino Galiana	Ergonomie du site web (#421)
a8f90c2f	2023-08-28 09:26:12	Lino Galiana	Update featured paths (#396)
80823022	2023-08-25 17:48:36	Lino Galiana	Mise à jour des scripts de construction des notebooks (#395)
3bdf3b06	2023-08-25 11:23:02	Lino Galiana	Simplification de la structure 🤓 (#393)
ab7ac8a0	2023-08-17 17:47:59	Lino Galiana	Path error
68f960be	2023-08-11 16:27:52	linogaliana	corrige probleme execution
0800d908	2023-08-11 13:32:19	linogaliana	Retour des corrections

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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Citation

BibTeX citation:

@book{galiana2023,
  author = {Galiana, Lino},
  title = {Python Pour La Data Science},
  date = {2023},
  url = {https://pythonds.linogaliana.fr/},
  doi = {10.5281/zenodo.8229676},
  langid = {en}
}

For attribution, please cite this work as:

Galiana, Lino. 2023. Python Pour La Data Science. https://doi.org/10.5281/zenodo.8229676.

1 Partie 1: manipuler des données

2 Partie 2: visualiser les données

3 Partie 3: modéliser

4 Partie 4: Natural Language Processing (NLP)

Informations additionnelles

Citation