The document outlines three key Python data cleaning resources: a recipe-style codebase for handling missing values and outliers, a low-code data preparation library with a clean API, and a comprehensive framework for profiling and building data cleaning pipelines. Each resource emphasizes modular design, reusability, and well-documented functions.
Comprehensive guide to Python programming covering installation, basic concepts, variables, data types, control flow, loops, input/output, data structures, functions, modules, error handling, object-oriented programming, file handling, datetime manipulation, random number generation, regular expressions, JSON handling, and database connections.
NumPy is a powerful Python library for numerical computing, offering n-dimensional array processing and various mathematical functions. It enhances performance with optimized C code while maintaining Python's flexibility. The tutorial covers array creation, manipulation, matrix operations, linear algebra, and random data handling, making it accessible for programmers of all backgrounds.
Matplotlib offers two main interfaces for plotting: the Pyplot interface, which is user-friendly for simple plots but has limitations in customization, and the Object-Oriented interface, which provides greater control and is better suited for complex visualizations. Key features include basic plot customization, advanced layouts, interactive features, and specialized plots like bar charts, histograms, scatter plots, and pie charts. Users can also manage axes, legends, and annotations effectively, while utilizing various styling options and colormaps for enhanced visual appeal.
Study group focused on the Pandas library for data analysis in Python, with links to resources for mastering data wrangling.
Seaborn provides various types of plots to visualize data relationships, including relational plots for scatter and line visualizations, categorical plots, distribution plots, matrix plots, regression plots, timeseries plots, and pairwise plots, along with options for customization and styling.
📖 1. Python Data Cleaning Cookbook
A comprehensive, recipe-style codebase supporting the Python Data Cleaning Cookbook by Packt. It includes practical examples for handling:
- Missing values
- Outliers
- Data profiling
- Reusable functions and classes for pipelines Reddit+11GitHub+11GitHub+11
✅ What to adopt:
- Recipe-based structure — organize code in small, understandable files.
- Use of functions and classes for reusability.
⚙️ 2. sfu-db/dataprep
An open-source, low-code data preparation library:
- Offers modules like
dataprep.cleanfor cleaning tasks - Clean, consistent API design
- Focus on ease-of-use, modularity, and documentation arXiv+3Stack Overflow+3Reddit+3GitHubGitHub
✅ What to adopt:
- Clear module breakdown (e.g.,
.clean,.eda) - Well-documented functions and consistent naming conventions.
🧹 3. VIDA-NYU/openclean
A full-featured data profiling and cleaning framework:
- Toolkit for profiling and building data cleaning pipelines
- Extensible and modular design, with comprehensive APIs Reddit+3Stack Overflow+3Reddit+3Stack Overflow+5GitHub+5arXiv+5GitHub+1Reddit+1
✅ What to adopt:
- Pipeline architecture—chain modular operations.
- Separation of concerns: profiling vs. cleaning modules.