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products:ict:python:data_visualization_with_matplotlib_and_seaborn

1. Introduction to Data Visualization:

The importance of data visualization in data analysis and communication. Overview of Matplotlib and Seaborn as Python visualization libraries. 2. Setting Up the Environment:

Installing Matplotlib and Seaborn. Configuring plotting settings and styles. 3. Basic Plots:

3.1. Line Plots:

Creating line plots to visualize trends over time or continuous data. Customizing line styles, colors, and markers. 3.2. Bar Plots:

Creating bar plots for categorical data. Customizing bar colors, widths, and labels. Grouped and stacked bar plots. 3.3. Scatter Plots:

Creating scatter plots to explore relationships between two continuous variables. Adding labels, colors, and markers to data points. 4. Customizing Plots:

4.1. Titles, Labels, and Legends:

Adding titles and axis labels to plots. Creating and customizing legends. 4.2. Colors and Colormaps:

Choosing and customizing colors for plot elements. Working with colormaps to represent data. 4.3. Axes and Ticks:

Customizing axis scales, limits, and ticks. Creating secondary axes. 4.4. Annotations and Text:

Adding text, arrows, and annotations to highlight specific data points or regions. 5. Subplots and Multi-plot Layouts:

Creating multiple plots within a single figure. Customizing subplot arrangement and spacing. 6. Advanced Plot Types:

6.1. Heatmaps:

Creating heatmaps to visualize data density. Customizing color mapping and annotations. 6.2. Box Plots and Violin Plots:

Creating box plots and violin plots to show data distributions. Customizing whiskers, outliers, and orientation. 6.3. Pair Plots:

Generating pair plots for visualizing pairwise relationships in datasets. Customizing pair plot aesthetics. 7. Seaborn Themes and Styles:

Utilizing built-in themes and styles to improve the overall appearance of plots. 8. Saving and Exporting Plots:

Saving plots in various formats (PNG, JPEG, PDF). Exporting plots for use in reports and presentations. 9. Interactive Plots (Optional):

Introduction to interactive plotting libraries like Plotly for web-based visualizations. 10. Real-world Data Visualization Projects: - Practical exercises and projects that involve creating meaningful visualizations from real-world datasets.

11. Best Practices in Data Visualization: - Guidelines for creating effective, honest, and informative visualizations. - Avoiding common pitfalls and misrepresentations.

12. Ethical Considerations in Data Visualization: - Addressing ethical concerns related to data visualization, including bias and misleading representations.

products/ict/python/data_visualization_with_matplotlib_and_seaborn.txt · Last modified: 2023/09/11 14:42 by wikiadmin