This repository serves as a comprehensive collection of code, data, and related materials for a series of data science study notes, primarily targeting individuals learning or working with Python for data analysis and visualization. It offers practical examples and detailed explanations for various libraries and techniques, aiming to enhance users' data science skills.

How It Works

The repository is structured around distinct data science topics, each corresponding to a series of blog posts and associated code. Key areas covered include in-depth explorations of pandas functionalities, spatial data analysis with geopandas and geoplot, web application development using Python and Dash, and advanced visualization with kepler.gl. The content is presented as a curated learning path, with each "study note" linking to specific code examples and explanations.

Quick Start & Requirements

• Installation: Clone the repository using git clone https://github.com/CNFeffery/DataScienceStudyNotes.git or git clone https://gitee.com/cnfeffery/DataScienceStudyNotes.git for a domestic mirror.
• Prerequisites: Requires Python and standard data science libraries (e.g., pandas, geopandas, dash, matplotlib, kepler.gl, DuckDB). Specific versions are not explicitly stated but implied by the content.
• Resources: No specific hardware requirements are mentioned, but data analysis tasks may benefit from sufficient RAM and CPU.

Highlighted Details

• Extensive coverage of geopandas, including spatial analysis, coordinate reference systems, file I/O, visualization, and integration with PostGIS.
• Detailed tutorials on building web applications with Python and Dash, covering layout, callbacks, components, and deployment.
• Practical guides on leveraging pandas for data manipulation, including advanced functions, performance optimization, and new features.
• Examples of using kepler.gl for creating interactive path animations and time-lapse maps.
• Introduction to DuckDB for high-performance data analysis within Python.

Maintenance & Community

• The repository is maintained by CNFeffery.
• Contact email: fefferypzy@gmail.com
• Blog: https://www.cnblogs.com/feffery/

Licensing & Compatibility

• The repository does not explicitly state a license.
• Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The repository is a collection of study notes and code examples rather than a runnable application or library. Users will need to set up their own Python environment and install the necessary dependencies to execute the provided code. Some blog posts mention specific library versions (e.g., pandas 1.0.0, 0.9.0, 1.0, 1.1, 1.3, 2.0, geopandas 0.9.0, 0.10, 0.11, 0.13, 0.14, 1.0), implying that compatibility with the latest versions might require adjustments.