Python数据分析与科学计算数据可视化篇:Matplotlib和Seaborn 从入门到实战

Unlock the Power of Data Visualization with Python

This course provides a comprehensive guide to Python data visualization, equipping you with the skills to transform raw data into compelling visual stories. You'll learn to effectively communicate insights and patterns using industry-standard tools.

Here's what you'll master:

• Data Visualization Fundamentals: We begin with essential pre-requisites for data visualization and an overview of key Python data visualization libraries.
• Setting Up Your Environment: Learn how to install the necessary libraries using pip, along with IPython, Anaconda, and explore installation instructions for Windows, Linux, and macOS. Get familiar with the IPython shell, Jupyter Notebook, and Spyder IDEs.
• Matplotlib Mastery: Dive deep into Matplotlib, a foundational plotting library. You'll understand the building blocks of charts, create your first plots, and learn to generate line graphs, bar charts, pie charts, scatter plots, and subplots. We'll cover customizing line colors and styles, saving figures, and displaying both English and Chinese characters and negative signs.
• Seaborn for Advanced Visualizations: Explore Seaborn, a high-level interface for creating informative and aesthetically pleasing statistical graphics. Discover Seaborn's built-in datasets and learn to control plot styles. You'll create various plot types including categorical plots, bar plots, box plots, violin plots, strip plots, swarm plots, faceted plots, correlation plots, correlation scatter plots, correlation line plots, distribution plots, Dist plots, joint plots, KDE plots, and heatmaps. We'll also cover linear regression plots (regplot) and faceted linear regression plots (lmplot).
• Real-World Project: Stock Data Visualization: Apply your skills to a practical project! We'll retrieve historical stock data for Kweichow Moutai, and create insightful visualizations like OHLC line charts, volume line charts, and K-line charts to analyze stock performance.

Prerequisites:

To get the most out of this course, it's helpful to have a basic understanding of:

• Python fundamentals
• Numpy library
• Pandas library