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Galleries | ||
========= | ||
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.. contents:: | ||
:local: | ||
:depth: 1 | ||
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Découvertes | ||
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Quelques notebooks autour de modules très utiles. | ||
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Examens | ||
+++++++ | ||
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Solutions des exercices servant à l'évaluation de | ||
ce cursus. | ||
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Exemples | ||
++++++++ | ||
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Quelques astuces récurrentes. |
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Exercices | ||
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Exerices pour pratiquer. |
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Internet | ||
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Automatisation de traitements avec des données | ||
récupérées depuis Internet. |
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.. blogpost:: | ||
:title: Préparation pour la séance 5, données out of memory | ||
:keywords: big data, | ||
:date: 2017-05-23 | ||
:categories: big data | ||
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Certains jeux disponibles sur | ||
`data.gouv.fr <https://www.data.gouv.fr/fr/>`_ | ||
sont très volumineux. C'est l'occasion de voir comment se | ||
débrouiller quand les données ne tiennent pas en mémoire. | ||
Nous choisir un des fichiers disponibles sur cette page : | ||
`DAMIR <https://www.data.gouv.fr/fr/datasets/open-damir-base-complete-sur-les-depenses-dassurance-maladie-inter-regimes/>`_. | ||
Il faudra le télécharger avant la séance. Nous verrons : | ||
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* `pandas et les itérateurs <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html>`_ | ||
* `sqlite3 <https://docs.python.org/3/library/sqlite3.html>`_ | ||
* `dask <http://dask.pydata.org/en/latest/>`_ | ||
* `format de données compressés <https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.sparse.csr_matrix.html>`_ | ||
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Correction de l'exercice proposé dans le blog précédent : | ||
:ref:`pyramidebigarreerst`. |
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.. _l-exercices: | ||
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========= | ||
Exercices | ||
========= | ||
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Pour apprendre à programmer avec des exercices plus ou moins courts. | ||
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Faciles | ||
======= | ||
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.. toctree:: | ||
:maxdepth: 1 | ||
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notebooks/pyramide_bigarree |
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