{"id":494,"date":"2026-03-02T17:07:14","date_gmt":"2026-03-02T17:07:14","guid":{"rendered":"https:\/\/pythonia.fr\/?page_id=494"},"modified":"2026-03-20T10:03:46","modified_gmt":"2026-03-20T10:03:46","slug":"python-libraries-for-data-science","status":"publish","type":"page","link":"https:\/\/pythonia.fr\/index.php\/en\/python-libraries-for-data-science\/","title":{"rendered":"Python Libraries for Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"494\" class=\"elementor elementor-494\">\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\">Python Libraries for 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\">\n  <!-- Left column: Content -->\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\">Objectives<\/h3>\n    <ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n      <li>Be able to choose the appropriate Python libraries for data projects<\/li>\n      <li>Master NumPy, Pandas, Matplotlib, Seaborn and Plotly<\/li>\n      <li>Become autonomous in data analysis, data cleaning and visualization<\/li>\n    <\/ul>\n  <\/div>\n  <!-- Right column: Links and buttons -->\n  <div style=\"flex:1;min-width:200px;background:#0B1D33;padding:25px;border-radius:12px;height:fit-content\">\n    \n    <a href=\"http:\/\/pythonia.fr\/wp-content\/uploads\/2026\/02\/Librairies_Data_Science-1.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 Program (PDF)\n    <\/a>\n    \n    <a href=\"#\" id=\"btn-dates-en\" style=\"display:block;color:#FFFFFF;font-size:15px;text-decoration:none;padding:12px 0;border-bottom:1px solid #1a3a5c\">\n      \ud83d\udcc5 View dates\n    <\/a>\n    \n    <button id=\"btn-contact-en\" style=\"display:block;width:100%;margin-top:20px;padding:15px 20px;background:#5DADE2;color:#FFFFFF;font-size:15px;font-weight:bold;border:none;border-radius:8px;cursor:pointer\">\n      \u2709\ufe0f Request information\n    <\/button>\n  <\/div>\n<\/div><\/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\">Target Audience<\/h3> \n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Data analysts<\/li>\n  <li>Python developers<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Prerequisites<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Knowledge of Python fundamentals (variables, types, loops, conditions, functions, files)<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Teaching Methods<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Theoretical input: interactive presentations with slides<\/li>\n  <li>Hands-on practice: individual labs and progressive exercises using real financial datasets<\/li>\n  <li>Active learning: collaborative problem-solving<\/li>\n  <li>Balanced theory\/practice approach: 30% theory \/ 70% practice<\/li>\n  <li>Course materials provided to participants<\/li>\n  <li>Mid-course quiz (20 questions) and final quiz (30 questions) to validate acquired skills<\/li>\n<\/ul>\n\n<h3 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Target Certification: RS6701 \u2014 Manipulating, analyzing and visualizing data using Python Data Science modules \u2014 CPF eligible<\/h3>\n\n<h1 style=\"font-size:24px;color:#FFFFFF;font-weight:bold;margin-bottom:20px\">Detailed Program<\/h1>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Day 1 \u2014 The Scientific Python Ecosystem &amp; NumPy<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Overview of Python Data Science packages<\/li>\n  <li>Installing scientific libraries: pip, venv, miniconda, mamba, miniforge, WinPython<\/li>\n  <li>Development environments: IPython, Jupyter Notebook, JupyterLab, Spyder, VS Code<\/li>\n  <li>Introduction to the NumPy library<\/li>\n  <li>Advantages of arrays (performance, data representation)<\/li>\n  <li>Creating arrays with array(), zeros(), ones(), full(), arange(), linspace(), logspace()<\/li>\n  <li>Matrix multiplication with np.dot and the @ operator<\/li>\n  <li>Identity matrix with identity() and eye(), diagonal matrix with diag()<\/li>\n  <li>Random initialization using NumPy's random module<\/li>\n  <li>Data types and attributes ndim, shape, size, dtype, itemsize, nbytes<\/li>\n  <li>Indexing, slicing, advanced indexing and broadcasting<\/li>\n  <li>Transposing and reshaping arrays (transpose(), reshape(), newaxis())<\/li>\n  <li>Concatenating and splitting arrays (concatenate(), vstack(), hstack(), split())<\/li>\n  <li>Functions: sum(), min(), max(), median(), percentile(), cumsum(), var(), argmin(), argmax()<\/li>\n  <li>Boolean masks for extracting information<\/li>\n  <li>Loading and saving arrays: loadtxt(), save(), load()<\/li>\n<\/ul>\n\n<h3 style=\"font-size:16px;color:#FFFFFF;font-weight:bold;margin-top:25px;margin-bottom:10px\">Day 2 \u2014 Data Manipulation with Pandas<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Introduction to the Pandas library<\/li>\n  <li>Creating a Series and a DataFrame<\/li>\n  <li>Extracting row and column indices (index and columns attributes)<\/li>\n  <li>Importing and exporting data (CSV, Excel\u2026)<\/li>\n  <li>Data exploration: head(), tail(), info(), describe(), dtypes<\/li>\n  <li>Implicit and explicit indexing with loc and iloc<\/li>\n  <li>Advanced selection: boolean expressions, query() method<\/li>\n  <li>Concatenating data with concat(), merging and joining with merge() and join()<\/li>\n  <li>Missing values: isna(), dropna(), fillna(), interpolate()<\/li>\n  <li>Sorting data: sort_index(), sort_values()<\/li>\n  <li>Removing data and duplicates: drop(), drop_duplicates()<\/li>\n  <li>Aggregation functions: sum(), cumsum(), min(), max(), mean(), median(), var(), std(), quantile()<\/li>\n  <li>Grouping and analysis: groupby(), aggregate(), apply(), filter(), transform()<\/li>\n  <li>Pivot tables: pivot_table()<\/li>\n  <li>Moving averages: rolling(), expanding(), ewm()<\/li>\n  <li>Multi-indexing: MultiIndex.from_product(), from_tuple(), from_arrays()<\/li>\n  <li>String processing and regular expressions with Pandas<\/li>\n  <li>Time series data: 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\">Day 3 \u2014 Visualization with Matplotlib &amp; Seaborn<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Introduction to Matplotlib: MATLAB-style vs object-oriented approach<\/li>\n  <li>Figure and Axes objects<\/li>\n  <li>Plotting curves with plot(): color, style, width, title, legend<\/li>\n  <li>Scatter plots with scatter()<\/li>\n  <li>Error bars with errorbar()<\/li>\n  <li>Area filling with fill_between()<\/li>\n  <li>Histograms with hist()<\/li>\n  <li>Multiple charts with subplots() and 3D plots with mplot3d<\/li>\n  <li>Pandas plotting: plot(), bar(), barh(), hist(), box(), scatter(), pie()<\/li>\n  <li>Introduction to Seaborn: Figure-level API and Axes-level API<\/li>\n  <li>Relational plots: relplot(), lineplot(), scatterplot()<\/li>\n  <li>Distributions: displot(), histplot(), jointplot(), pairplot()<\/li>\n  <li>Categorical data: catplot(), barplot(), countplot(), boxplot(), violinplot()<\/li>\n  <li>Heatmaps: heatmap()<\/li>\n  <li>Linear regression models: lmplot()<\/li>\n  <li>Customization: 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\">Day 4 \u2014 Interactive Visualization with Plotly<\/h3>\n<ul style=\"font-size:14px;color:#FFFFFF;line-height:1.8;margin-left:20px\">\n  <li>Introduction to the Plotly library and Kaleido: exploring Plotly Express<\/li>\n  <li>Plotting curves with line(): customizing figures with title, width, height, marker, labels<\/li>\n  <li>Adding information: hover_data, hover_name, text<\/li>\n  <li>Multiple charts: facet_row, facet_col<\/li>\n  <li>Style customization: template option and default themes<\/li>\n  <li>Area charts with area(): adding patterns with pattern_shape<\/li>\n  <li>Scatter plots with scatter(): using size, size_max, opacity, symbol<\/li>\n  <li>Color bars: color_continuous_scale, update_layout(), update_coloraxes()<\/li>\n  <li>Formatting bar charts with bar() and histograms with histogram()<\/li>\n  <li>3D charts with scatter_3d() and line_3d()<\/li>\n  <li>Mapping data with line_map(), scatter_map(), line_geo(), scatter_geo(), and choropleth()<\/li>\n<\/ul><\/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>Python Libraries for Data Science Objectives Be able to choose the appropriate Python libraries for data projects Master NumPy, Pandas, [&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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