{"id":145,"date":"2026-02-06T08:27:58","date_gmt":"2026-02-06T08:27:58","guid":{"rendered":"https:\/\/pythonia.fr\/?page_id=145"},"modified":"2026-04-28T14:03:35","modified_gmt":"2026-04-28T14:03:35","slug":"librairies-python-de-data-science","status":"publish","type":"page","link":"https:\/\/pythonia.fr\/index.php\/librairies-python-de-data-science\/","title":{"rendered":"Librairies Python de Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"145\" class=\"elementor elementor-145\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e2fae96 e-grid e-con-boxed e-con e-parent\" data-id=\"e2fae96\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4c539a2 elementor-widget elementor-widget-heading\" data-id=\"4c539a2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Librairies Python pour la Data Science<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b7eeeed e-grid e-con-boxed e-con e-parent\" data-id=\"b7eeeed\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7d50fe5 elementor-widget elementor-widget-heading\" data-id=\"7d50fe5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><div style=\"display:flex;gap:40px;flex-wrap:wrap\"><div style=\"display:flex;gap:40px;flex-wrap:wrap\">\n  <!-- Colonne gauche : Contenu -->\n  <div style=\"flex:2;min-width:300px\">\n    <h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Objectifs<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Savoir choisir les librairies Python ad\u00e9quates dans des projets de donn\u00e9es<\/li>\n      <li>Ma\u00eetriser NumPy, Pandas, Matplotlib, Seaborn et Plotly<\/li>\n      <li>\u00catre autonome dans l'analyse, le nettoyage et la visualisation de donn\u00e9es<\/li>\n    <\/ul>\n  <\/div>\n  <!-- Colonne droite : Liens et boutons -->\n  <div style=\"flex:1;min-width:200px;background:#0B1D33;padding:25px;border-radius:12px;height:fit-content\">\n    <div style=\"padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      <span style=\"color:#8899AA;font-size:12px;text-transform:uppercase;letter-spacing:0.5px\">Tarif inter \/ participant<\/span>\n      <div style=\"color:#FFFFFF;font-size:22px;font-weight:bold;margin-top:4px\">\n        2 500 \u20ac <span style=\"font-size:13px;font-weight:normal;color:#8899AA\">HT<\/span>\n      <\/div>\n    <\/div>\n    <a href=\"http:\/\/pythonia.fr\/wp-content\/uploads\/2026\/02\/Programme_Formation_DataScience_F.pdf\" target=\"_blank\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc4 Programme (PDF)\n    <\/a>\n\n    <a href=\"#\" id=\"btn-dates\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc5 Voir les dates\n    <\/a>\n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9515a2f e-grid e-con-boxed e-con e-parent\" data-id=\"9515a2f\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-432ebaa elementor-widget elementor-widget-heading\" data-id=\"432ebaa\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Public vis\u00e9<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Data analysts<\/li>\n  <li>D\u00e9veloppeurs Python<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Pr\u00e9requis<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Ma\u00eetriser les bases de Python (variables, types, boucles, conditions, fonctions, fichiers)<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">M\u00e9thodes p\u00e9dagogiques<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Apports th\u00e9oriques : expos\u00e9s interactifs avec diaporama<\/li>\n  <li>Mise en pratique : TP individuels, exercices progressifs sur donn\u00e9es financi\u00e8res r\u00e9elles<\/li>\n  <li>P\u00e9dagogie active : r\u00e9solution de probl\u00e8mes collaboratifs<\/li>\n  <li>Alternance th\u00e9orie\/pratique : ratio 30 % th\u00e9orie \/ 70 % pratique<\/li>\n  <li>Support de cours remis aux stagiaires<\/li>\n  <li>QCM mi-parcours (15 questions) et Passage de la certification API Society RS6763 en fin de session<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Certification vis\u00e9e :  RS6763 \u2014 Manipuler, analyser et visualiser des donn\u00e9es gr\u00e2ce aux modules Python de Data Science \u2014<\/h3>\n\n<h1 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-bottom:20px\">Programme d\u00e9taill\u00e9<\/h1>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Jour 1 \u2014 L'\u00e9cosyst\u00e8me Python scientifique &amp; NumPy<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Tour d'horizon des packages Python de Data Science<\/li>\n  <li>Installation de biblioth\u00e8ques scientifiques : pip, venv, miniconda, mamba, miniforge, WinPython<\/li>\n  <li>Environnement de d\u00e9veloppement : IPython, Jupyter Notebook, JupyterLab, Spyder, VS Code<\/li>\n  <li>Pr\u00e9sentation de la librairie NumPy<\/li>\n  <li>Avantages des tableaux (performance, repr\u00e9sentation des donn\u00e9es)<\/li>\n  <li>Cr\u00e9ation de tableaux avec array(), zeros(), ones(), full(), arange(), linspace(), logspace()<\/li>\n  <li>Multiplication matricielle avec np.dot et l'op\u00e9rateur @<\/li>\n  <li>Matrice identit\u00e9 avec identity() et eye(), matrice diagonale avec diag()<\/li>\n  <li>Initialisation avec des donn\u00e9es al\u00e9atoires (module random de NumPy)<\/li>\n  <li>Types de donn\u00e9es et attributs ndim, shape, size, dtype, itemsize, nbytes<\/li>\n  <li>Indexation, slicing, indexation avanc\u00e9e et broadcasting<\/li>\n  <li>Transposer et changer les dimensions de tableaux (transpose(), reshape(), newaxis())<\/li>\n  <li>Concat\u00e9ner et d\u00e9couper des tableaux (concatenate(), vstack(), hstack(), split())<\/li>\n  <li>Fonctions sum(), min(), max(), median(), percentile(), cumsum(), var(), argmin(), argmax()<\/li>\n  <li>Masques bool\u00e9ens pour extraire des informations<\/li>\n  <li>Charger et sauvegarder les tableaux : loadtxt(), save(), load()<\/li>\n<\/ul>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Jour 2 \u2014 Manipulation de donn\u00e9es avec Pandas<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Pr\u00e9sentation de la librairie Pandas<\/li>\n  <li>Cr\u00e9er une Series et un DataFrame<\/li>\n  <li>Extraire les indices de ligne et de colonnes (attributs index et columns)<\/li>\n  <li>Lire et exporter des donn\u00e9es (CSV, Excel\u2026)<\/li>\n  <li>Exploration de donn\u00e9es : head(), tail(), info(), describe(), dtypes<\/li>\n  <li>Indexation implicite et explicite avec loc et iloc<\/li>\n  <li>S\u00e9lection avanc\u00e9e : expressions bool\u00e9ennes, m\u00e9thode query()<\/li>\n  <li>Concat\u00e9ner des donn\u00e9es avec concat(), fusion et jointure : merge() et join()<\/li>\n  <li>Valeurs manquantes : isna(), dropna(), fillna(), interpolate()<\/li>\n  <li>Trier les donn\u00e9es : sort_index(), sort_values()<\/li>\n  <li>Supprimer des donn\u00e9es et doublons : drop(), drop_duplicates()<\/li>\n  <li>Fonctions d'agr\u00e9gation : sum(), cumsum(), min(), max(), mean(), median(), var(), std(), quantile()<\/li>\n  <li>Grouper et analyser : groupby(), aggregate(), apply(), filter(), transform()<\/li>\n  <li>Tableaux crois\u00e9s dynamiques : pivot_table()<\/li>\n  <li>Moyennes glissantes : rolling(), expanding(), ewm()<\/li>\n  <li>Multi-indices : MultiIndex.from_product(), from_tuple(), from_arrays()<\/li>\n  <li>Cha\u00eenes de caract\u00e8res et expressions r\u00e9guli\u00e8res avec Pandas<\/li>\n  <li>Donn\u00e9es temporelles : to_datetime(), date_range(), asfreq(), resample()<\/li>\n<\/ul>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Jour 3 \u2014 Visualisation avec Matplotlib &amp; Seaborn<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Pr\u00e9sentation de Matplotlib : style MATLAB vs style orient\u00e9 objet<\/li>\n  <li>Les objets Figure et Axes<\/li>\n  <li>Tracer des courbes : plot() \u2014 couleur, style, largeur, titre, l\u00e9gende<\/li>\n  <li>Nuages de points : scatter()<\/li>\n  <li>Barres d'erreurs : errorbar()<\/li>\n  <li>Remplissage entre courbes : fill_between()<\/li>\n  <li>Histogrammes : hist()<\/li>\n  <li>Graphiques multiples avec subplots() et graphiques 3D avec mplot3d<\/li>\n  <li>Pandas plot : m\u00e9thodes plot(), bar(), barh(), hist(), box(), scatter(), pie()<\/li>\n  <li>Pr\u00e9sentation de Seaborn : API Figure-level et Axes-level<\/li>\n  <li>Relational plots : relplot(), lineplot(), scatterplot()<\/li>\n  <li>Distributions : displot(), histplot(), jointplot(), pairplot()<\/li>\n  <li>Donn\u00e9es qualitatives : catplot(), barplot(), countplot(), boxplot(), violinplot()<\/li>\n  <li>Cartes thermiques : heatmap()<\/li>\n  <li>Mod\u00e8les de r\u00e9gression lin\u00e9aire : lmplot()<\/li>\n  <li>Personnalisation : set_theme(), set_style(), set_context(), despine()<\/li>\n<\/ul>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Jour 4 \u2014 Visualisation interactive avec Plotly<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Pr\u00e9sentation de la librairie Plotly et de Kaleido : introduction et exploration de Plotly Express<\/li>\n  <li>Tracer des courbes avec line() : modification de la figure avec les options title, width, height, marker, labels<\/li>\n  <li>Ajout d'informations : hover_data, hover_name, text<\/li>\n  <li>Graphiques multiples : facet_row, facet_col<\/li>\n  <li>Modifier le style : option template, th\u00e8me par d\u00e9faut<\/li>\n  <li>Cr\u00e9er des graphiques en aires avec area() : ajout de motifs avec pattern_shape<\/li>\n  <li>Cr\u00e9er des nuages de points avec scatter() : utilisation des options size, size_max, opacity, symbol<\/li>\n  <li>Barre de couleur : color_continuous_scale, update_layout(), update_coloraxes()<\/li>\n  <li>Mettre en forme des diagrammes en barres avec bar() et des histogrammes avec histogram()<\/li>\n  <li>Graphiques 3D : utilisation de scatter_3d() et line_3d()<\/li>\n  <li>Tracer des cartes avec line_map(), scatter_map(), line_geo(), scatter_geo(), et choropleth()<\/li>\n<\/ul>\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Librairies Python pour la Data Science Objectifs Savoir choisir les librairies Python ad\u00e9quates dans des projets de donn\u00e9es Ma\u00eetriser NumPy, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"no-sidebar","site-content-layout":"","ast-site-content-layout":"full-width-container","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"disabled","ast-breadcrumbs-content":"","ast-featured-img":"disabled","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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