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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/getting-started/*.qmd"
- "content/manipulation/*.qmd"
- "!content/manipulation/03_geopandas_tutorial.qmd"
- "content/visualisation/*.qmd"
- "content/modelisation/*.qmd"
- "content/NLP/*.qmd"
- "content/git/*.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}
:::::: {.content-visible when-profile="fr"}
::::::: h1
Python pour la _data science_ {{< fa brands python >}}
:::::::
::::::
:::::: {.content-visible when-profile="en"}
::::::: h1
Data science with Python {{< 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>
```
:::::: {.content-visible when-profile="fr"}
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`.
::::::
:::::: {.content-visible when-profile="en"}
Course website [*Python for 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>, an introduction to `Python` for the second year of the engineering curriculum at [`ENSAE`](https://www.ensae.fr/) (Master 1).
<br>
All content of this group is freely available here or on [`Github`](https://github.com/linogaliana/python-datascientist)
<a href="https://github.com/linogaliana/python-datascientist" class="github"><i class="fab fa-github"></i></a> and can be tested in the form of `Jupyter` notebooks.
::::::
<br>
<details>
<summary>
:::::: {.content-visible when-profile="fr"}
Exemple avec l'introduction à `Pandas`
::::::
:::::: {.content-visible when-profile="en"}
Example with the introduction to `Pandas`
::::::
</summary>
```{ojs}
//| echo: false
html`${printBadges({fpath: "content/manipulation/02_pandas_intro.qmd"})}`
```
</details>
:::::: {.content-visible when-profile="fr"}
<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...
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. Le programme est présenté de manière linéaire dans la partie supérieure de cette page (👆️) ou de manière désordonnée ci-dessous (👇️).
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/](https://ensae-reproductibilite.github.io/website/)
</details>
::::::
:::::: {.content-visible when-profile="en"}
<details>
<summary>
On the agenda:
</summary>
Overall, this course offers a comprehensive content that can satisfy both beginners in data science and those looking for more advanced material:
<br>
1. **Data Manipulation**: standard data manipulation (`Pandas`), geographic data (`Geopandas`), data retrieval (web scraping, APIs)...
1. **Data Visualization**: classic visualizations (`Matplotlib`, `Seaborn`), cartography, reactive visualizations (`Plotly`, `Folium`)
1. **Modeling**: machine learning (`Scikit`), econometrics
1. **Text Data Processing** (NLP): introduction to tokenization with `NLTK` and `SpaCy`, modeling...
1. **Introduction to Modern Data Science**: cloud computing, `ElasticSearch`, continuous integration...
All content on this site relies on open data, whether it is French data (mainly from the central platform [`data.gouv`](https://www.data.gouv.fr) or the Insee website) or American data. The program is presented linearly at the top of this page (👆️) or in a disordered manner below (👇️).
A good complement to the website content is the course given with Romain Avouac in the final year at ENSAE, which focuses more on the production of data science projects: [https://ensae-reproductibilite.github.io/](https://ensae-reproductibilite.github.io/website/)
</details>
::::::
:::::
::::: {.g-col-lg-6 .g-col-12 .g-col-md-12}
:::::
::::
:::
<br>
## Thèmes en vrac {.unnumbered}
Pour découvrir `Python` {{< fa brands python >}} de manière désordonnée. La version ordonnée est dans la partie supérieure de cette page (👆️).
::: {#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`;
let sspcloudJupyterLink;
if (type === "md") {
sspcloudJupyterLink = `[](${sspcloudJupyterLinkLauncher})`;
} else {
sspcloudJupyterLink = `<a href="${sspcloudJupyterLinkLauncher}" target="_blank" rel="noopener"><img src="https://img.shields.io/badge/SSP%20Cloud-Lancer_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`;
let sspcloudVscodeLink;
if (type === "md") {
sspcloudVscodeLink = `[](${sspcloudVscodeLinkLauncher})`;
} else {
sspcloudVscodeLink = `<a href="${sspcloudVscodeLinkLauncher}" target="_blank" rel="noopener"><img src="https://img.shields.io/badge/SSP%20Cloud-Lancer_avec_VSCode-blue?logo=visualstudiocode&logoColor=blue" alt="Onyxia"></a>`;
}
if (split === 5) {
sspcloudVscodeLink += '<br>';
}
let colabLink;
if (type === "md") {
colabLink = `[](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 = `[](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 });
```