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Mastering Data Visualization with Matplotlib and Seaborn
Mastering Data Visualization with Matplotlib and Seaborn
Chapter 1: Introduction to Data VisualizationImportance of data visualization in analyticsOverview of Matplotlib and Seaborn librariesSetting up the environment and installing necessary packagesChapter 2: Basic Plotting with MatplotlibAnatomy of a Matplotlib figureCreating simple line plots, scatter plots, and bar chartsCustomizing axes, labels, and titlesChapter 3: Advanced Customization with MatplotlibFine-tuning colors, markers, and line stylesAdding grids, legends, and annotationsControlling figure size, aspect ratio, and layoutChapter 4: Creating Subplots and MultiplotsUnderstanding subplot grids and layoutsCreating multiple plots in a single figureCustomizing each subplot independentlyChapter 5: Exploring Seaborn’s High-Level InterfaceIntroduction to Seaborn’s built-in dataset and theme stylesSimple visualizations using sns.scatterplot, sns.barplot, and sns.lineplotComparing Seaborn with MatplotlibChapter 6: Visualizing Distributions with SeabornUsing sns.histplot, sns.kdeplot, and sns.boxplot for distribution analysisCreating joint plots and pair plotsWorking with categorical variablesChapter 7: Statistical Data Visualization with SeabornVisualizing relationships between variables with sns.regplot and sns.lmplotPlotting regression models and confidence intervalsAdvanced statistical plotting with sns.heatmapChapter 8: Working with Color Palettes and StylesCustomizing Seaborn color palettes and Matplotlib colormapsChoosing the right palette for your dataCreating visually appealing styles and themes
Chapter 9: Time Series and Date-Based PlotsPlotting time series data with Matplotlib and SeabornFormatting date ticks and labelsUsing rolling averages and smoothing techniquesChapter 10: Working with 3D Plots in MatplotlibCreating 3D plots using Axes3DVisualizing surfaces, wireframes, and scatter plots in 3DEnhancing 3D plots with color mappingChapter 11: Handling Large Datasets in VisualizationsOptimizing Matplotlib and Seaborn for large datasetsReducing memory consumption and processing timeUsing sampled data and aggregation techniquesChapter 12: Integrating Pandas with Matplotlib and SeabornPlotting directly from Pandas dataframesUsing pd.plot() and sns.lineplot() for quick visualizationCustomizing plots using Pandas and Seaborn togetherChapter 13: Interactive Plots with MatplotlibAdding interactive elements using matplotlib.widgetsBuilding interactive visualizations with sliders, buttons, and text boxesIntroduction to external tools like Plotly for enhanced interactivityChapter 14: Saving
Chapter 1: Introduction to Data VisualizationImportance of data visualization in analyticsOverview of Matplotlib and Seaborn librariesSetting up the environment and installing necessary packagesChapter 2: Basic Plotting with MatplotlibAnatomy of a Matplotlib figureCreating simple line plots, scatter plots, and bar chartsCustomizing axes, labels, and titlesChapter 3: Advanced Customization with MatplotlibFine-tuning colors, markers, and line stylesAdding grids, legends, and annotationsControlling figure size, aspect ratio, and layoutChapter 4: Creating Subplots and MultiplotsUnderstanding subplot grids and layoutsCreating multiple plots in a single figureCustomizing each subplot independentlyChapter 5: Exploring Seaborn’s High-Level InterfaceIntroduction to Seaborn’s built-in dataset and theme stylesSimple visualizations using sns.scatterplot, sns.barplot, and sns.lineplotComparing Seaborn with MatplotlibChapter 6: Visualizing Distributions with SeabornUsing sns.histplot, sns.kdeplot, and sns.boxplot for distribution analysisCreating joint plots and pair plotsWorking with categorical variablesChapter 7: Statistical Data Visualization with SeabornVisualizing relationships between variables with sns.regplot and sns.lmplotPlotting regression models and confidence intervalsAdvanced statistical plotting with sns.heatmapChapter 8: Working with Color Palettes and StylesCustomizing Seaborn color palettes and Matplotlib colormapsChoosing the right palette for your dataCreating visually appealing styles and themes
Chapter 9: Time Series and Date-Based PlotsPlotting time series data with Matplotlib and SeabornFormatting date ticks and labelsUsing rolling averages and smoothing techniquesChapter 10: Working with 3D Plots in MatplotlibCreating 3D plots using Axes3DVisualizing surfaces, wireframes, and scatter plots in 3DEnhancing 3D plots with color mappingChapter 11: Handling Large Datasets in VisualizationsOptimizing Matplotlib and Seaborn for large datasetsReducing memory consumption and processing timeUsing sampled data and aggregation techniquesChapter 12: Integrating Pandas with Matplotlib and SeabornPlotting directly from Pandas dataframesUsing pd.plot() and sns.lineplot() for quick visualizationCustomizing plots using Pandas and Seaborn togetherChapter 13: Interactive Plots with MatplotlibAdding interactive elements using matplotlib.widgetsBuilding interactive visualizations with sliders, buttons, and text boxesIntroduction to external tools like Plotly for enhanced interactivityChapter 14: Saving
116 pages, Kindle Edition
Published October 2, 2024
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