Python pour la data science

Lino Galiana

doi:10.5281/zenodo.8229676

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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

renderCorrection({fpath: "content/manipulation/01_numpy.qmd", correction: true})

Introduction à Pandas

renderCorrection({fpath: "content/manipulation/02_pandas_intro.qmd", correction: true})

Manipulations de données avec Pandas

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Exercices Geopandas :

renderCorrection({fpath: "content/manipulation/03_geopandas_TP.qmd", correction: true})

Exercices webscraping

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Exercices expressions régulières

renderCorrection({fpath: "content/manipulation/04b_regex_TP.qmd", correction: true})

Exercices API

renderCorrection({fpath: "content/manipulation/04c_API_TP.qmd", correction: true})

2 Partie 2: visualiser les données

Exercices graphiques classiques

renderCorrection({fpath: "content/visualisation/matplotlib.qmd", correction: defaultPrint})

Exercices sur la cartographie

renderCorrection({fpath: "content/visualisation/maps.qmd", correction: defaultPrint})

3 Partie 3: modéliser

Exercices sur le preprocessing

renderCorrection({fpath: "content/modelisation/0_preprocessing.qmd", correction: defaultPrint})

Exercices sur l’évaluation des modèles

renderCorrection({fpath: "content/modelisation/1_modelevaluation.qmd", correction: defaultPrint})

Exercices sur la classification

renderCorrection({fpath: "content/modelisation/2_SVM.qmd", correction: defaultPrint})

Exercices sur la régression

renderCorrection({fpath: "content/modelisation/3_regression.qmd", correction: defaultPrint})

Exercices sur la sélection de variables

renderCorrection({fpath: "content/modelisation/4_featureselection.qmd", correction: defaultPrint})

Exercices sur le clustering

renderCorrection({fpath: "content/manipulation/5_clustering.qmd", correction: defaultPrint})

Exercices sur les pipelines

renderCorrection({fpath: "content/manipulation/6_pipeline.qmd", correction: defaultPrint})

4 Partie 4: Natural Language Processing (NLP)

Découverte des enjeux du NLP

renderCorrection({fpath: "content/NLP/01_intro.qmd", correction: defaultPrint})

Mise en pratique de l’approche bag of words

renderCorrection({fpath: "content/NLP/02_exoclean.qmd", correction: defaultPrint})

Exercices sur la latent dirichlet allocation

renderCorrection({fpath: "content/NLP/03_lda.qmd", correction: defaultPrint})

Découverte de Word2Vec

renderCorrection({fpath: "content/NLP/04_word2vec.qmd", correction: defaultPrint})

Exercices supplémentaires

renderCorrection({fpath: "content/NLP/05_exo_supp.qmd", correction: defaultPrint})

Informations additionnelles

environment files have been tested on.

Latest built version: 2024-11-20

Python version used:

'3.12.6 | packaged by conda-forge | (main, Sep 30 2024, 18:08:52) [GCC 13.3.0]'

Package	Version
affine	2.4.0
aiobotocore	2.15.1
aiohappyeyeballs	2.4.3
aiohttp	3.10.8
aioitertools	0.12.0
aiosignal	1.3.1
alembic	1.13.3
altair	5.4.1
aniso8601	9.0.1
annotated-types	0.7.0
appdirs	1.4.4
archspec	0.2.3
asttokens	2.4.1
attrs	24.2.0
babel	2.16.0
bcrypt	4.2.0
beautifulsoup4	4.12.3
black	24.8.0
blinker	1.8.2
blis	0.7.11
bokeh	3.5.2
boltons	24.0.0
boto3	1.35.23
botocore	1.35.23
branca	0.7.2
Brotli	1.1.0
cachetools	5.5.0
cartiflette	0.0.2
Cartopy	0.24.1
catalogue	2.0.10
cattrs	24.1.2
certifi	2024.8.30
cffi	1.17.1
charset-normalizer	3.3.2
click	8.1.7
click-plugins	1.1.1
cligj	0.7.2
cloudpathlib	0.20.0
cloudpickle	3.0.0
colorama	0.4.6
comm	0.2.2
commonmark	0.9.1
conda	24.9.1
conda-libmamba-solver	24.7.0
conda-package-handling	2.3.0
conda_package_streaming	0.10.0
confection	0.1.5
contextily	1.6.2
contourpy	1.3.0
cryptography	43.0.1
cycler	0.12.1
cymem	2.0.8
cytoolz	1.0.0
dask	2024.9.1
dask-expr	1.1.15
databricks-sdk	0.33.0
debugpy	1.8.6
decorator	5.1.1
Deprecated	1.2.14
diskcache	5.6.3
distributed	2024.9.1
distro	1.9.0
docker	7.1.0
duckdb	0.10.1
en-core-web-sm	3.7.1
entrypoints	0.4
et_xmlfile	2.0.0
exceptiongroup	1.2.2
executing	2.1.0
fastexcel	0.11.6
fastjsonschema	2.20.0
fiona	1.10.1
Flask	3.0.3
folium	0.17.0
fontawesomefree	6.6.0
fonttools	4.54.1
frozendict	2.4.4
frozenlist	1.4.1
fsspec	2023.12.2
funcy	2.0
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	2.35.0
graphene	3.3
graphql-core	3.2.4
graphql-relay	3.2.0
graphviz	0.20.3
great-tables	0.12.0
greenlet	3.1.1
gunicorn	22.0.0
h2	4.1.0
hpack	4.0.0
htmltools	0.6.0
hyperframe	6.0.1
idna	3.10
imageio	2.36.0
importlib_metadata	8.5.0
importlib_resources	6.4.5
inflate64	1.0.0
ipykernel	6.29.5
ipython	8.28.0
itsdangerous	2.2.0
jedi	0.19.1
Jinja2	3.1.4
jmespath	1.0.1
joblib	1.4.2
jsonpatch	1.33
jsonpointer	3.0.0
jsonschema	4.23.0
jsonschema-specifications	2024.10.1
jupyter-cache	1.0.0
jupyter_client	8.6.3
jupyter_core	5.7.2
kaleido	0.2.1
kiwisolver	1.4.7
langcodes	3.5.0
language_data	1.3.0
lazy_loader	0.4
libmambapy	1.5.9
locket	1.0.0
lxml	5.3.0
lz4	4.3.3
Mako	1.3.5
mamba	1.5.9
mapclassify	2.8.1
marisa-trie	1.2.1
Markdown	3.6
markdown-it-py	3.0.0
MarkupSafe	2.1.5
matplotlib	3.9.2
matplotlib-inline	0.1.7
mdurl	0.1.2
menuinst	2.1.2
mercantile	1.2.1
mizani	0.11.4
mlflow	2.16.2
mlflow-skinny	2.16.2
msgpack	1.1.0
multidict	6.1.0
multivolumefile	0.2.3
munkres	1.1.4
murmurhash	1.0.10
mypy-extensions	1.0.0
narwhals	1.14.1
nbclient	0.10.0
nbformat	5.10.4
nest_asyncio	1.6.0
networkx	3.3
nltk	3.9.1
numexpr	2.10.1
numpy	1.26.4
opencv-python-headless	4.10.0.84
openpyxl	3.1.5
opentelemetry-api	1.16.0
opentelemetry-sdk	1.16.0
opentelemetry-semantic-conventions	0.37b0
OWSLib	0.28.1
packaging	24.1
pandas	2.2.3
paramiko	3.5.0
parso	0.8.4
partd	1.4.2
pathspec	0.12.1
patsy	0.5.6
Pebble	5.0.7
pexpect	4.9.0
pickleshare	0.7.5
pillow	10.4.0
pip	24.2
platformdirs	4.3.6
plotly	5.24.1
plotnine	0.13.6
pluggy	1.5.0
polars	1.8.2
preshed	3.0.9
prometheus_client	0.21.0
prometheus_flask_exporter	0.23.1
prompt_toolkit	3.0.48
protobuf	4.25.3
psutil	6.0.0
ptyprocess	0.7.0
pure_eval	0.2.3
py7zr	0.20.8
pyarrow	17.0.0
pyarrow-hotfix	0.6
pyasn1	0.6.1
pyasn1_modules	0.4.1
pybcj	1.0.2
pycosat	0.6.6
pycparser	2.22
pycryptodomex	3.21.0
pydantic	2.9.2
pydantic_core	2.23.4
Pygments	2.18.0
pyLDAvis	3.4.1
PyNaCl	1.5.0
pynsee	0.1.8
pyogrio	0.10.0
pyOpenSSL	24.2.1
pyparsing	3.1.4
pyppmd	1.1.0
pyproj	3.7.0
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	6.0.2
pyzmq	26.2.0
pyzstd	0.16.2
querystring_parser	1.2.4
rasterio	1.4.2
referencing	0.35.1
regex	2024.9.11
requests	2.32.3
requests-cache	1.2.1
retrying	1.3.4
rich	13.9.4
rpds-py	0.21.0
rsa	4.9
ruamel.yaml	0.18.6
ruamel.yaml.clib	0.2.8
s3fs	2023.12.2
s3transfer	0.10.2
scikit-image	0.24.0
scikit-learn	1.5.2
scipy	1.13.0
seaborn	0.13.2
setuptools	74.1.2
shapely	2.0.6
shellingham	1.5.4
six	1.16.0
smart-open	7.0.5
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	2.0.35
sqlparse	0.5.1
srsly	2.4.8
stack-data	0.6.2
statsmodels	0.14.4
tabulate	0.9.0
tblib	3.0.0
tenacity	9.0.0
texttable	1.7.0
thinc	8.2.5
threadpoolctl	3.5.0
tifffile	2024.9.20
toolz	1.0.0
topojson	1.9
tornado	6.4.1
tqdm	4.66.5
traitlets	5.14.3
truststore	0.9.2
typer	0.13.1
typing_extensions	4.12.2
tzdata	2024.2
Unidecode	1.3.8
url-normalize	1.4.3
urllib3	1.26.20
wasabi	1.1.3
wcwidth	0.2.13
weasel	0.4.1
webdriver-manager	4.0.2
websocket-client	1.8.0
Werkzeug	3.0.4
wheel	0.44.0
wordcloud	1.9.3
wrapt	1.16.0
xgboost	2.1.1
xlrd	2.0.1
xyzservices	2024.9.0
yarl	1.13.1
yellowbrick	1.5
zict	3.0.0
zipp	3.20.2
zstandard	0.23.0

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
d22ec21	2024-11-13 16:21:30	lgaliana	Corrections
cb18759	2024-11-01 09:38:00	lgaliana	Broken url for corrected exercises
00f28a3	2024-10-15 13:41:20	lgaliana	Ajoute questions collaboratifs Git
c326488	2024-10-10 14:31:57	Romain Avouac	Various fixes (#565)
bd374ba	2024-09-24 20:05:55	lgaliana	install altair & correction numpy
4640e6d	2024-09-18 11:53:05	linogaliana	corrections
005d89b	2023-12-20 17:23:04	Lino Galiana	Finalise l’affichage des statistiques Git (#478)
4c1c22d	2023-12-10 11:50:56	Lino Galiana	Badge en javascript plutôt (#469)
417fb66	2023-12-04 18:49:21	Lino Galiana	Corrections partie ML (#468)
1f23de2	2023-12-01 17:25:36	Lino Galiana	Stockage des images sur S3 (#466)
a06a268	2023-11-23 18:23:28	Antoine Palazzolo	2ème relectures chapitres ML (#457)
8728352	2023-10-24 11:50:08	Lino Galiana	Correction coquilles geopandas (#444)
6362a07	2023-10-10 15:08:33	linogaliana	Ajoute corrections
241cab3	2023-10-04 17:53:29	Lino Galiana	Ergonomie du site web (#421)
a8f90c2	2023-08-28 09:26:12	Lino Galiana	Update featured paths (#396)
8082302	2023-08-25 17:48:36	Lino Galiana	Mise à jour des scripts de construction des notebooks (#395)
3bdf3b0	2023-08-25 11:23:02	Lino Galiana	Simplification de la structure 🤓 (#393)
ab7ac8a	2023-08-17 17:47:59	Lino Galiana	Path error
68f960b	2023-08-11 16:27:52	linogaliana	corrige probleme execution
0800d90	2023-08-11 13:32:19	linogaliana	Retour des corrections

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.