algo et data science
This commit is contained in:
gwen
56
algo/algofundoc/_build/html/_sources/MachineLearning.txt
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56
algo/algofundoc/_build/html/_sources/MachineLearning.txt
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Machine learning
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=================
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Data science (not big data yet)
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some links
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------------
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numpy, scipy, matplotlib... and scikit
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https://www.scipy.org/install.html
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https://matplotlib.org/
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http://scikit-learn.org/stable/
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installation
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--------------
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.. code-block:: python
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Python 3.6.5 (default, Apr 1 2018, 05:46:30)
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[GCC 7.3.0] on linux
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Type "help", "copyright", "credits" or "license" for more information.
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>>> import sys
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>>> print('Python: {}'.format(sys.version))
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Python: 3.6.5 (default, Apr 1 2018, 05:46:30)
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[GCC 7.3.0]
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>>> import scipy
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>>> print('scipy: {}'.format(scipy.__version__))
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scipy: 0.19.1
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>>> import numpy
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>>> print('numpy: {}'.format(numpy.__version__))
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numpy: 1.13.3
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>>> import matplotlib
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>>> print('matplotlib: {}'.format(matplotlib.__version__))
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matplotlib: 2.1.1
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>>> # pandas
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...
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>>> import pandas
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>>> print('pandas: {}'.format(pandas.__version__))
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pandas: 0.22.0
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>>> # scikit-learn
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... import sklearn
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>>> print('sklearn: {}'.format(sklearn.__version__))
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sklearn
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usage
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-------
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https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
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34
algo/algofundoc/_build/html/_sources/fil_conducteur.txt
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34
algo/algofundoc/_build/html/_sources/fil_conducteur.txt
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Fil conducteur : 1er tour des élections présidentielles 2017
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=============================================================
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Le fil conducteur sera l’exploitation de données issues du 1er tour des élections présidentielles qui ont eu lieu en France le 23 avril 2017.
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Les données dont on dispose sont les résultats par canton (plus de 2000 cantons). Pour chaque canton sont donnés
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- le nombre d’inscrits
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- le nombre de votants
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- le nombre de bulletins nuls
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- le nombre de bulletins blancs
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- le nombre de voix obtenus par chacun des candidats.
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L’objectif est d’établir
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- les résultats au niveau national
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- la participation
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.. figure:: images/participation_globale.png
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:width: 650
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:alt: Participation globale
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- le scores des candidats
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.. figure:: images/scores_1er_tour.png
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:width: 650
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:alt: Score des candidats
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Ce sera l'occasion de découvrir :
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* les structures itérables, en particulier les tuples et dictionnaires
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* la lecture et l'écriture de données dans des fichiers
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* des algorithmes de tris et de recherche.
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36
algo/algofundoc/_build/html/_sources/index.txt
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36
algo/algofundoc/_build/html/_sources/index.txt
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.. default-role:: literal
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.. meta::
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:description: algo tutorial
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:keywords: algorithm, python, tutorial
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.. title:: algofundoc
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Algo Fun
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==========
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.. toctree::
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:maxdepth: 1
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programmation
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liens
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MachineLearning
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pandas
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Indices and tables
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==================
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* `All modules for which code is available <_modules/index.html>`_
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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.. note:: The pyfun code is licensed under the `LGPL licence`_
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and this documentation is licensed under the `Creative Commons
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Attribution-ShareAlike 3.0 Unported License`_\ .
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.. _`Creative Commons Attribution-ShareAlike 3.0 Unported License`: http://creativecommons.org/licenses/by-sa/3.0/deed.en_US
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.. _`LGPL licence`: http://www.gnu.org/licenses/lgpl.html
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16
algo/algofundoc/_build/html/_sources/liens.txt
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16
algo/algofundoc/_build/html/_sources/liens.txt
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Liens utiles
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=============
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- random_ datas
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- pandas_
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- ploting_
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- machine_ learning
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- making_ a machine learning
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- data_ science
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.. _making: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
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.. _data: https://realpython.com/tutorials/data-science/
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.. _machine: https://realpython.com/tutorials/machine-learning/
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.. _ploting: https://realpython.com/python-histograms/?__s=o2w1az6ypdj7ogdsnqwf
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.. _random: https://realpython.com/python-random/?__s=o2w1az6ypdj7ogdsnqwf
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.. _pandas: https://realpython.com/fast-flexible-pandas/?__s=o2w1az6ypdj7ogdsnqwf
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229
algo/algofundoc/_build/html/_sources/notebooks/Pandas.ipynb.txt
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229
algo/algofundoc/_build/html/_sources/notebooks/Pandas.ipynb.txt
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The **import** keyword is used to import a library"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6.123233995736766e-17\n"
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]
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}
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],
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"source": [
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"import math\n",
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"print(math.cos(math.pi / 2))\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pandas\n",
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"======="
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>col1</th>\n",
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" <th>col2</th>\n",
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" <th>col3</th>\n",
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" <th>col4</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>line1</th>\n",
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" <td>-0.882125</td>\n",
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" <td>2.176452</td>\n",
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" <td>0.163955</td>\n",
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" <td>-0.618232</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>line2</th>\n",
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" <td>-0.721538</td>\n",
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" <td>0.035578</td>\n",
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" <td>0.180072</td>\n",
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" <td>1.015987</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>line3</th>\n",
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" <td>-1.162355</td>\n",
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" <td>0.384632</td>\n",
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" <td>-0.674092</td>\n",
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" <td>0.162693</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>line4</th>\n",
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" <td>-1.399455</td>\n",
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" <td>-0.698512</td>\n",
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" <td>0.039420</td>\n",
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" <td>0.898408</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>line5</th>\n",
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" <td>1.755342</td>\n",
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" <td>-0.073242</td>\n",
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" <td>-1.502503</td>\n",
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" <td>-0.586194</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" col1 col2 col3 col4\n",
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"line1 -0.882125 2.176452 0.163955 -0.618232\n",
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"line2 -0.721538 0.035578 0.180072 1.015987\n",
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"line3 -1.162355 0.384632 -0.674092 0.162693\n",
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"line4 -1.399455 -0.698512 0.039420 0.898408\n",
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"line5 1.755342 -0.073242 -1.502503 -0.586194"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import numpy\n",
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"import pandas\n",
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"rows = ['line1', 'line2', 'line3', 'line4', 'line5']\n",
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"cols = ['col1', 'col2', 'col3', 'col4']\n",
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"from IPython.display import display\n",
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"dataframe = pandas.DataFrame(numpy.random.randn(5,4), index=rows, columns=cols)\n",
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"display(dataframe)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"reorganise a **dataframe** from datas as a dictionary with tuples as keys\n",
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"-----------------------------------------------------------------------------------------------------"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Column 1</th>\n",
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" <th>Column 2</th>\n",
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" <th>Column 3</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Cerise</td>\n",
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" <td>Lanister</td>\n",
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" <td>14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Paul</td>\n",
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" <td>Durand</td>\n",
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" <td>13</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Pierre</td>\n",
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" <td>Dupont</td>\n",
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" <td>16</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>john</td>\n",
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" <td>Snow</td>\n",
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" <td>12</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Column 1 Column 2 Column 3\n",
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"0 Cerise Lanister 14\n",
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"1 Paul Durand 13\n",
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"2 Pierre Dupont 16\n",
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"3 john Snow 12"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"dico = {('john', 'Snow') : 12, ('Paul', 'Durand') : 13, (\"Pierre\", \"Dupont\") : 16, (\"Cerise\", \"Lanister\") : 14}\n",
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"import pandas\n",
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"df = pandas.Series(dico).reset_index()\n",
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"df.columns = ['Column 1', 'Column 2', 'Column 3']\n",
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"from IPython.display import display\n",
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"display(df)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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14
algo/algofundoc/_build/html/_sources/pandas.txt
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14
algo/algofundoc/_build/html/_sources/pandas.txt
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@ -0,0 +1,14 @@
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pandas
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==========
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**examples** in the jupyter_ notebooks
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.. _jupyter: http://jupyter.org/
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ipython and pandas::
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jupyter notebook Pandas.ipynb
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.. toctree::
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||||
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notebooks/Pandas.ipynb
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61
algo/algofundoc/_build/html/_sources/programmation.txt
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61
algo/algofundoc/_build/html/_sources/programmation.txt
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==============================
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Algorithmes et Programmation
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==============================
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||||
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||||
-----
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||||
Cours
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||||
-----
|
||||
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||||
.. toctree::
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||||
:maxdepth: 1
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||||
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||||
fil_conducteur
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||||
|
||||
|
||||
- Structures de données séquentielles
|
||||
- Ensembles et dictionnaires
|
||||
- Algorithmes de recherche
|
||||
- Les fichiers
|
||||
- Algorithmes de tri
|
||||
|
||||
|
||||
|
||||
----
|
||||
TP
|
||||
----
|
||||
|
||||
.. toctree::
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||||
:maxdepth: 1
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||||
|
||||
|
||||
|
||||
|
||||
- Tester avec doctest
|
||||
- Listes
|
||||
- Gestion d’une promotion d’étudiants
|
||||
- Anagrammes
|
||||
- Analyse d’un fichier texte
|
||||
- Évaluation empirique des tris
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
-----------------
|
||||
Documents annexes
|
||||
-----------------
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
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||||
|
||||
|
||||
Bibliographie
|
||||
-------------
|
||||
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||||
* Apprendre à programmer avec Python 3, Gérard Swinnen, editions Eyrolles (Chapitres 1 à 7, et chapitre 10 en partie). `Version électronique téléchargeable <http://inforef.be/swi/python.htm>`_.
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||||
|
||||
* `Site officiel du langage Python <https://www.python.org/>`_.
|
||||
|
||||
* `Documentation officielle de la version 3.5 de Python <https://docs.python.org/3.5/>`_.
|
||||
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||||
* `Site officiel de Thonny <http://www.thonny.org/>`_.
|
||||
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Block a user