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index.qmd
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---
toc: false
sidebar: false
title-block-style: none
css: styles/homepage.css
listing:
id: sample-listings
contents: content/**/*.qmd
type: grid
sort: "number"
categories: true
#fields: [image, title, github, gitlab]
#template: cards.ejs
---
::: {#hero-banner .column-screen}
:::: {.grid .column-page}
::::: {.headline .g-col-lg-6 .g-col-12 .g-col-md-12}
:::::: h1
Python pour la _data science_ {{< fa brands python >}}
::::::
:::::: h2
[Lino Galiana](https://github.com/linogaliana/)
::::::
```{=html}
<div style="text-align: right;">
<a class="github-button" href="https://github.com/linogaliana/python-datascientist" data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star this website on Github">Star this website on Github</a><script async defer src="https://buttons.github.io/buttons.js"></script>
</div>
```
Site web du cours [*Python pour la data science*](https://www.ensae.fr/courses/1425-python-pour-le-data-scientist)
<a href="https://github.com/linogaliana/python-datascientist" class="github"><i class="fab fa-python"></i></a>, une introduction à `Python` pour
la deuxième année du cursus d'ingénieur de l'[`ENSAE`](https://www.ensae.fr/) (Master 1).
<br>
L'ensemble du contenu de ce groupe est librement disponible ici
ou sur [`Github`](https://github.com/linogaliana/python-datascientist)
<a href="https://github.com/linogaliana/python-datascientist" class="github"><i class="fab fa-github"></i></a> et peut être testé
sous forme de _notebooks_ `Jupyter`.
<br>
<details>
<summary>
Exemple avec l'introduction à `Pandas`
</summary>
```{ojs}
//| echo: false
html`${printBadges({fpath: "content/manipulation/02_pandas_intro.qmd"})}`
```
</details>
<details>
<summary>
Au programme:
</summary>
Globalement, ce cours propose un contenu très complet pouvant autant
satisfaire des débutants en
_data science_ que des personnes à la recherche de contenu plus avancé :
<br>
1. __Manipulation de données__ : manipulation de données standards (`Pandas`), données géographiques (`Geopandas`), récupération de données (webscraping, API)...
1. __Visualisation de données__ : visualisations classiques (`Matplotlib`, `Seaborn`), cartographie, visualisations réactives (`Plotly`, `Folium`)
1. __Modélisation__ : _machine learning_ (`Scikit`), économétrie
1. __Traitement de données textuelles__ (NLP): découverte de la tokenisation avec `NLTK` et `SpaCy`, modélisation...
1. **Introduction à la _data science_ moderne** : _cloud computing_, `ElasticSearch`, intégration continue...
<br>
L'ensemble du contenu de ce site s'appuie sur des données
ouvertes, qu'il s'agisse de données françaises (principalement
issues de la plateforme
centralisatrice [`data.gouv`](https://www.data.gouv.fr) ou du site
_web_ de l'[Insee](https://www.insee.fr)) ou de données
américaines.
Un bon complément du contenu du site web est le cours que nous donnons avec Romain Avouac en dernière année de l'ENSAE plus tourné autour de la mise en production de projets _data science_ : [https://ensae-reproductibilite.github.io/website/](https://ensae-reproductibilite.github.io/website/)
</details>
:::::
::::: {.g-col-lg-6 .g-col-12 .g-col-md-12}
![](https://minio.lab.sspcloud.fr/lgaliana/generative-art/pythonds/kiddos.png)
:::::
::::
:::
<br>
## Thèmes en vrac {.unnumbered}
Pour découvrir `Python` {{< fa brands python >}} de manière thématique
::: {#sample-listings}
:::
```{python}
#| include: false
#| eval: false
import os
import glob
import yaml
list_files = glob.glob("./content/**/*.qmd", recursive=True)
posts = [l for l in list_files if l.endswith('.qmd') ]
def extract_slug_from_markdown(file_path):
with open(file_path, 'r') as file:
content = file.read()
front_matter = yaml.safe_load(content.split('---', 2)[1])
if isinstance(front_matter, dict) and 'slug' in front_matter:
slug = front_matter['slug']
return slug
return None
# Extract the slugs
slugs = {post: extract_slug_from_markdown(post) for post in posts if extract_slug_from_markdown(post) is not None}
# Lines to insert in a Netlify _redirect file
redirects = [f"/{slug} /posts/{post}" for slug, post in zip(slugs, posts)]
# Write the _redirect file
with open("_site/_redirects", "w") as f:
f.write("\n".join(redirects))
```
```{ojs}
//| echo: false
function reminderBadges({
sourceFile = "content/01_toto.Rmd",
type = ['md', 'html'],
split = null,
onyxiaOnly = false,
sspCloudService = "python",
GPU = false,
correction = false
} = {}) {
if (Array.isArray(type)) {
type = type[0];
}
let notebook = sourceFile.replace(/(.Rmd|.qmd)/, ".ipynb");
if (correction) {
notebook = notebook.replace(/content/, "corrections");
} else {
notebook = notebook.replace(/content/, "notebooks");
}
const githubRepoNotebooksSimplified = "github/linogaliana/python-datascientist-notebooks";
const githubAlias = githubRepoNotebooksSimplified.replace("github", "github.com");
const githubRepoNotebooks = `https://${githubAlias}`;
let githubLink ;
if (notebook === "") {
githubLink = githubRepoNotebooks;
} else {
githubLink = `${githubRepoNotebooks}/blob/main`;
}
const notebookRelPath = `/${notebook}`;
const [section, chapter] = notebook.split("/").slice(-2);
githubLink = `<a href="${githubLink}${notebookRelPath}" class="github"><i class="fab fa-github"></i></a>`;
const sectionLatest = section.split("/").slice(-1)[0];
const chapterNoExtension = chapter.replace(".ipynb", "");
const onyxiaInitArgs = [sectionLatest, chapterNoExtension];
if (correction) {
onyxiaInitArgs.push("correction");
}
const gpuSuffix = GPU ? "-gpu" : "";
const sspcloudJupyterLinkLauncher = `https://datalab.sspcloud.fr/launcher/ide/jupyter-${sspCloudService}${gpuSuffix}?autoLaunch=true&onyxia.friendlyName=%C2%AB${chapterNoExtension}%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-jupyter.sh%C2%BB&init.personalInitArgs=%C2%AB${onyxiaInitArgs.join('%20')}%C2%BB&security.allowlist.enabled=false`;
let sspcloudJupyterLink;
if (type === "md") {
sspcloudJupyterLink = `[![Onyxia](https://img.shields.io/badge/SSP%20Cloud-Tester_avec_Jupyter-orange?logo=Jupyter&logoColor=orange)](${sspcloudJupyterLinkLauncher})`;
} else {
sspcloudJupyterLink = `<a href="${sspcloudJupyterLinkLauncher}" target="_blank" rel="noopener"><img src="https://img.shields.io/badge/SSP%20Cloud-Tester_avec_Jupyter-orange?logo=Jupyter&logoColor=orange" alt="Onyxia"></a>`;
}
if (split === 4) {
sspcloudJupyterLink += '<br>';
}
const sspcloudVscodeLinkLauncher = `https://datalab.sspcloud.fr/launcher/ide/vscode-${sspCloudService}${gpuSuffix}?autoLaunch=true&onyxia.friendlyName=%C2%AB${chapterNoExtension}%C2%BB&init.personalInit=%C2%ABhttps%3A%2F%2Fraw.githubusercontent.com%2Flinogaliana%2Fpython-datascientist%2Fmaster%2Fsspcloud%2Finit-vscode.sh%C2%BB&init.personalInitArgs=%C2%AB${onyxiaInitArgs.join('%20')}%C2%BB&security.allowlist.enabled=false`;
let sspcloudVscodeLink;
if (type === "md") {
sspcloudVscodeLink = `[![Onyxia](https://img.shields.io/badge/SSP%20Cloud-Tester_avec_VSCode-blue?logo=visualstudiocode&logoColor=blue)](${sspcloudVscodeLinkLauncher})`;
} else {
sspcloudVscodeLink = `<a href="${sspcloudVscodeLinkLauncher}" target="_blank" rel="noopener"><img src="https://img.shields.io/badge/SSP%20Cloud-Tester_avec_VSCode-blue?logo=visualstudiocode&logoColor=blue" alt="Onyxia"></a>`;
}
if (split === 5) {
sspcloudVscodeLink += '<br>';
}
let colabLink;
if (type === "md") {
colabLink = `[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](http://colab.research.google.com/${githubRepoNotebooksSimplified}/blob/main${notebookRelPath})`;
} else {
colabLink = `<a href="https://colab.research.google.com/${githubRepoNotebooksSimplified}/blob/main${notebookRelPath}" target="_blank" rel="noopener"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>`;
}
if (split === 7) {
colabLink += '<br>';
}
let vscodeLink;
if (type === "md") {
vscodeLink = `[![githubdev](https://img.shields.io/static/v1?logo=visualstudiocode&label=&message=Open%20in%20Visual Studio Code&labelColor=2c2c32&color=007acc&logoColor=007acc)](https://github.dev/linogaliana/python-datascientist-notebooks${notebookRelPath})`;
} else {
vscodeLink = `<a href="https://github.dev/linogaliana/python-datascientist-notebooks${notebookRelPath}" target="_blank" rel="noopener"><img src="https://img.shields.io/static/v1?logo=visualstudiocode&label=&message=Open%20in%20Visual%20Studio%20Code&labelColor=2c2c32&color=007acc&logoColor=007acc" alt="githubdev"></a></p>`;
}
const badges = [
githubLink,
sspcloudVscodeLink,
sspcloudJupyterLink
];
if (!onyxiaOnly) {
badges.push(colabLink);
}
let result = badges.join("\n");
if (type === "html") {
result = `<p class="badges">${result}</p>`;
}
if (onyxiaOnly) {
result = `${sspcloudJupyterLink}${sspcloudVscodeLink}`;
}
return result;
}
function printBadges({
fpath,
onyxiaOnly = false,
split = 5,
type = "html",
sspCloudService = "python",
GPU = false,
correction = false
} = {}) {
const badges = reminderBadges({
sourceFile: fpath,
type: type,
split: split,
onyxiaOnly: onyxiaOnly,
sspCloudService: sspCloudService,
GPU: GPU,
correction: correction
});
return badges
}
// Example usage:
// printBadges({ fpath: "content/getting-started/05_rappels_types.qmd", onyxiaOnly: false, split: 5, type: "html", sspCloudService: "python", GPU: false, correction: false });
```