Python pour la data science

Lino Galiana

doi:10.5281/zenodo.8229676

Pandas et Numpy, les premiers packages de notre parcours initiatique, sont essentiels pour manipuler les données. Néanmoins, il est nécessaire de ne pas faire l’impasse sur les fondements du langage Python lorsqu’on découvre ce langage. Une bonne compréhension des éléments structurants du langage permet de mieux comprendre la logique des packages de data science, de mieux comprendre les erreurs rencontrées et entraîne une plus grande productivité et liberté.

Pour découvrir les objets de base et la structure du langage, une série de notebooks est mise à disposition ci-dessous. Le parcours est à la carte, vous pouvez faire ces notebooks dans le désordre ou n’en faire qu’une partie si vous avez de bons souvenirs sur le contenu couvert par certains.

Après avoir révisé, la suite du parcours se trouve dans la partie “Manipuler les données”

Notebooks de révision

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

environment files have been tested on.

Latest built version: 2024-10-01

Python version used:

'3.11.6 | packaged by conda-forge | (main, Oct  3 2023, 10:40:35) [GCC 12.3.0]'

Package	Version
affine	2.4.0
aiobotocore	2.12.2
aiohttp	3.9.3
aioitertools	0.11.0
aiosignal	1.3.1
alembic	1.13.1
altair	5.4.1
aniso8601	9.0.1
annotated-types	0.7.0
appdirs	1.4.4
archspec	0.2.3
astroid	3.1.0
asttokens	2.4.1
attrs	23.2.0
babel	2.16.0
bcrypt	4.1.2
beautifulsoup4	4.12.3
black	24.8.0
blinker	1.7.0
blis	0.7.11
bokeh	3.4.0
boltons	23.1.1
boto3	1.34.51
botocore	1.34.51
branca	0.7.1
Brotli	1.1.0
cachetools	5.3.3
cartiflette	0.0.2
Cartopy	0.23.0
catalogue	2.0.10
cattrs	24.1.2
certifi	2024.2.2
cffi	1.16.0
charset-normalizer	3.3.2
click	8.1.7
click-plugins	1.1.1
cligj	0.7.2
cloudpathlib	0.19.0
cloudpickle	3.0.0
colorama	0.4.6
comm	0.2.2
commonmark	0.9.1
conda	24.3.0
conda-libmamba-solver	24.1.0
conda-package-handling	2.2.0
conda_package_streaming	0.9.0
confection	0.1.5
contextily	1.6.2
contourpy	1.2.1
cryptography	42.0.5
cycler	0.12.1
cymem	2.0.8
cytoolz	0.12.3
dask	2024.4.1
dask-expr	1.0.10
debugpy	1.8.1
decorator	5.1.1
dill	0.3.8
diskcache	5.6.3
distributed	2024.4.1
distro	1.9.0
docker	7.0.0
duckdb	0.10.1
en-core-web-sm	3.7.1
entrypoints	0.4
et-xmlfile	1.1.0
exceptiongroup	1.2.0
executing	2.0.1
fastexcel	0.11.6
fastjsonschema	2.19.1
fiona	1.9.6
flake8	7.0.0
Flask	3.0.2
folium	0.16.0
fontawesomefree	6.6.0
fonttools	4.51.0
frozenlist	1.4.1
fsspec	2023.12.2
GDAL	3.8.4
gensim	4.3.2
geographiclib	2.0
geopandas	1.0.1
geoplot	0.5.1
geopy	2.4.1
gitdb	4.0.11
GitPython	3.1.43
google-auth	2.29.0
graphene	3.3
graphql-core	3.2.3
graphql-relay	3.2.0
graphviz	0.20.3
great-tables	0.12.0
greenlet	3.0.3
gunicorn	21.2.0
htmltools	0.5.3
hvac	2.1.0
idna	3.6
imageio	2.35.1
importlib_metadata	7.1.0
importlib_resources	6.4.0
inflate64	1.0.0
ipykernel	6.29.3
ipython	8.22.2
ipywidgets	8.1.2
isort	5.13.2
itsdangerous	2.1.2
jedi	0.19.1
Jinja2	3.1.3
jmespath	1.0.1
joblib	1.3.2
jsonpatch	1.33
jsonpointer	2.4
jsonschema	4.21.1
jsonschema-specifications	2023.12.1
jupyter-cache	1.0.0
jupyter_client	8.6.1
jupyter_core	5.7.2
jupyterlab_widgets	3.0.10
kaleido	0.2.1
kiwisolver	1.4.5
kubernetes	29.0.0
langcodes	3.4.1
language_data	1.2.0
lazy_loader	0.4
libmambapy	1.5.7
llvmlite	0.42.0
locket	1.0.0
lxml	5.3.0
lz4	4.3.3
Mako	1.3.2
mamba	1.5.7
mapclassify	2.6.1
marisa-trie	1.2.0
Markdown	3.6
markdown-it-py	3.0.0
MarkupSafe	2.1.5
matplotlib	3.8.3
matplotlib-inline	0.1.6
mccabe	0.7.0
mdurl	0.1.2
menuinst	2.0.2
mercantile	1.2.1
mizani	0.11.4
mlflow	2.11.3
mlflow-skinny	2.11.3
msgpack	1.0.7
multidict	6.0.5
multivolumefile	0.2.3
munkres	1.1.4
murmurhash	1.0.10
mypy	1.9.0
mypy-extensions	1.0.0
narwhals	1.9.0
nbclient	0.10.0
nbformat	5.10.4
nest_asyncio	1.6.0
networkx	3.3
nltk	3.8.1
numba	0.59.1
numpy	1.26.4
oauthlib	3.2.2
opencv-python-headless	4.9.0.80
openpyxl	3.1.5
OWSLib	0.28.1
packaging	23.2
pandas	2.2.3
paramiko	3.4.0
parso	0.8.4
partd	1.4.1
pathspec	0.12.1
patsy	0.5.6
Pebble	5.0.7
pexpect	4.9.0
pickleshare	0.7.5
pillow	10.3.0
pip	24.0
pkgutil_resolve_name	1.3.10
platformdirs	4.2.0
plotly	5.19.0
plotnine	0.13.6
pluggy	1.4.0
polars	1.8.2
preshed	3.0.9
prometheus_client	0.20.0
prometheus-flask-exporter	0.23.0
prompt-toolkit	3.0.42
protobuf	4.25.3
psutil	5.9.8
ptyprocess	0.7.0
pure-eval	0.2.2
py7zr	0.20.8
pyarrow	17.0.0
pyarrow-hotfix	0.6
pyasn1	0.5.1
pyasn1-modules	0.3.0
pybcj	1.0.2
pycodestyle	2.11.1
pycosat	0.6.6
pycparser	2.21
pycryptodomex	3.20.0
pydantic	2.9.2
pydantic_core	2.23.4
pyflakes	3.2.0
Pygments	2.17.2
PyJWT	2.8.0
pylint	3.1.0
PyNaCl	1.5.0
pynsee	0.1.8
pyogrio	0.10.0
pyOpenSSL	24.0.0
pyparsing	3.1.2
pyppmd	1.1.0
pyproj	3.6.1
pyshp	2.3.1
PySocks	1.7.1
python-dateutil	2.9.0
python-dotenv	1.0.1
python-magic	0.4.27
pytz	2024.1
pyu2f	0.1.5
pywaffle	1.1.1
PyYAML	6.0.1
pyzmq	25.1.2
pyzstd	0.16.1
querystring-parser	1.2.4
rasterio	1.4.0
referencing	0.34.0
regex	2023.12.25
requests	2.31.0
requests-cache	1.2.1
requests-oauthlib	2.0.0
retrying	1.3.4
rich	13.8.1
rpds-py	0.18.0
rsa	4.9
Rtree	1.2.0
ruamel.yaml	0.18.6
ruamel.yaml.clib	0.2.8
s3fs	2023.12.2
s3transfer	0.10.1
scikit-image	0.24.0
scikit-learn	1.4.1.post1
scipy	1.13.0
seaborn	0.13.2
setuptools	69.2.0
shapely	2.0.3
shellingham	1.5.4
six	1.16.0
smart_open	7.0.4
smmap	5.0.0
sortedcontainers	2.4.0
soupsieve	2.5
spacy	3.7.5
spacy-legacy	3.0.12
spacy-loggers	1.0.5
SQLAlchemy	2.0.29
sqlparse	0.4.4
srsly	2.4.8
stack-data	0.6.2
statsmodels	0.14.1
tabulate	0.9.0
tblib	3.0.0
tenacity	8.2.3
texttable	1.7.0
thinc	8.2.5
threadpoolctl	3.4.0
tifffile	2024.9.20
tomli	2.0.1
tomlkit	0.12.4
toolz	0.12.1
topojson	1.9
tornado	6.4
tqdm	4.66.2
traitlets	5.14.2
truststore	0.8.0
typer	0.12.5
typing_extensions	4.11.0
tzdata	2024.1
Unidecode	1.3.8
url-normalize	1.4.3
urllib3	1.26.18
wasabi	1.1.3
wcwidth	0.2.13
weasel	0.4.1
webdriver-manager	4.0.2
websocket-client	1.7.0
Werkzeug	3.0.2
wheel	0.43.0
widgetsnbextension	4.0.10
wordcloud	1.9.3
wrapt	1.16.0
xgboost	2.0.3
xlrd	2.0.1
xyzservices	2024.4.0
yarl	1.9.4
yellowbrick	1.5
zict	3.0.0
zipp	3.17.0
zstandard	0.22.0

View file history

md`Ce fichier a été modifié __${table_commit.length}__ fois depuis sa création le ${creation_string} (dernière modification le ${last_modification_string})`

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"
})

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

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

SHA	Date	Author	Description
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)

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')

Retour au sommet

Citation

BibTeX

@book{galiana2023,
  author = {Galiana, Lino},
  title = {Python pour la data science},
  date = {2023},
  url = {https://pythonds.linogaliana.fr/},
  doi = {10.5281/zenodo.8229676},
  langid = {fr}
}

Veuillez citer ce travail comme suit :

Galiana, Lino. 2023. Python pour la data science. https://doi.org/10.5281/zenodo.8229676.