A few refresher exercises to get back in the saddle

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.

After spending some time reviewing your Python skills, you can find the next part of the course in the “Data wrangling” section.

Review notebooks

await teaserList(listeChapters)

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

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,
    imageUrl,
    deploymentUrl,
    tags,
    authors,
  } = doc;

  // Generate the HTML for each tag with space between them
  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("");

  // Generate the HTML for each author with space between them
  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.en : deploymentUrl.fr;
    const localizedAbstract = (lang === "en") ? abstract.en : abstract.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="btn btn-outline-primary btn-launch" href="${localizedDeploymentUrl}" target="_blank">${textButton}</a>
      </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";

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
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')