plotnine is a mature Python library that implements the Grammar of Graphics, closely modeled after R's ggplot2. It provides a declarative, layered API for building statistical visualizations: users compose plots by adding geometric objects (geoms), statistical transformations (stats), scales, coordinate systems, facets, themes, and guides using the `+` operator. The library renders all output through matplotlib, supporting a wide range of plot types (scatter, bar, line, histogram, density, boxplot, violin, contour, maps, etc.) with extensive customization via themes and scales. It includes built-in example datasets, animation support, plot composition (arranging multiple plots), and integration with Jupyter/Quarto. The codebase is well-structured with a clear separation of concerns (geoms, stats, scales, coords, facets, themes, guides, composition) and employs several reusable design patterns such as a registry metaclass for automatic subclass registration, dataclass-based scale definitions, a hierarchical themeable system, and a docstring generation decorator. External dependencies include matplotlib, pandas, numpy, scipy, statsmodels, mizani, and contourpy, with optional support for adjustText, geopandas, and skmisc. The project is actively maintained with weekly CI tests against bleeding-edge dependencies and a comprehensive test suite using baseline image comparison.
matplot2tikz is a Python library that converts matplotlib figures into PGFPlots/TikZ code for native inclusion in LaTeX or ConTeXt documents. It recursively traverses a matplotlib Figure tree, translating each artist (lines, patches, images, colorbars, legends, text, etc.) into corresponding PGFPlots commands. The library provides two main entry points: `get_tikz_code()` returns the TikZ code as a string, and `save()` writes it to a file. It also includes a `clean_figure()` function that simplifies paths and reduces data points to minimize output size while preserving visual fidelity. The architecture is modular, with dedicated modules for each artist type (e.g., `_line2d.py`, `_patch.py`, `_axes.py`), a central data class (`TikzData`) to carry configuration and state, and a recursive traversal in `_save.py`. External dependencies include matplotlib, numpy, Pillow, and webcolors. The codebase is well-tested with over 170 test files and uses modern Python tooling (ruff, mypy, tox). It is a fork of the unmaintained tikzplotlib and is actively maintained with recent commits and CI.
FigMirror is a reference-conditioned scientific figure style transfer system that iteratively generates matplotlib scripts matching the visual style of a reference figure. It operates as a multi-agent loop (Drawer/Reviewer) orchestrated by a runner backend (Codex, Claude, or Mock), with a local web UI for upload, iteration browsing, and refinement. The system is packaged as skills for both Claude Code and Codex, plus a standalone web server. While primarily an application, it has a strong research component: the prompts, aesthetic library, and evaluation benchmarks (PlotTwin-Bench) are the result of extensive empirical iteration. The codebase demonstrates high craftsmanship in its runner architecture, atomic file operations, SSE streaming, and deterministic review gates. However, it suffers from dual skill bundles (Claude/Codex) that must stay synchronized, retired artifacts, and some incomplete features (ClaudeRunner, SSE integration).
PyPlot.jl is a Julia package that provides a direct interface to the matplotlib.pyplot library from Python, enabling Julia users to create plots using Matplotlib's extensive plotting capabilities. The package uses PyCall to call Matplotlib functions with minimal overhead, passing Julia arrays without copying. It integrates with Julia's multimedia I/O system to display plots in various backends (including IJulia, Jupyter, and GUI windows) and supports automatic backend selection based on available dependencies. The architecture consists of a core module handling the PyPlot interface, separate modules for colormap management and 3D plotting, and initialization code that configures the Matplotlib backend and sets up integration with Julia's display system. Key features include support for LaTeX in plot labels via LaTeXStrings, automatic figure management in interactive environments, and compatibility with both Julia-based and Python-based GUI backends.
This repository is a 30-day Python tutorial that originated as an application-focused learning project but has evolved into a hybrid data_research resource. The core is a structured curriculum covering Python fundamentals through 30 days of markdown documentation and accompanying Python scripts. However, the repository's most substantial and unique content lies in its data folder, which contains rich datasets including presidential speeches (Obama, Trump, Michelle Obama, Melina Trump), Fortune 500 company data, Hacker News posts, country statistics, email exchanges, and weight-height CSV data. The data_research character emerges from the later days which cover web scraping (with actual scraped JSON output from Boston University), Pandas data analysis, NumPy statistics, MongoDB database operations, and API consumption. The repository includes Flask web applications for text analysis, demonstrating practical data processing pipelines. While the tutorial content is well-structured and professionally presented across multiple languages (English, German, Spanish, Chinese, Portuguese, Persian, Korean, Ukrainian), the actual executable Python code is minimal compared to the markdown documentation. The data assets are the most valuable component - they're real, substantial datasets that could be used for NLP analysis, statistical exercises, and data visualization projects. However, the repository lacks proper dependency management beyond a basic requirements.txt for the Flask app, has no environment configuration, and the data sources are not systematically documented with metadata or provenance information.
This repository is a Chinese translation (2nd edition) of Wes McKinney's "Python for Data Analysis" book, presented as markdown chapter files. It is an educational resource, not a software application or a full data pipeline. The content covers pandas, NumPy, Matplotlib, and other data analysis libraries with embedded code examples for learning purposes. It is not a project that can be executed directly; there are no Python scripts, notebooks, or data files included. The code examples are based on older library versions (circa 2017) and use deprecated APIs such as `ix` and `read_table`. External datasets referenced (e.g., tips.csv, MovieLens) are not bundled, making reproduction impossible without manual setup. The repository has not been actively maintained, and its technical drift is high due to ecosystem changes. It is best used as a reference or study guide, not as a reusable codebase.
This repository is a static collection of cheat sheets (images and PDFs) for various machine learning and data science libraries (TensorFlow, Keras, Pandas, NumPy, Scikit-learn, PySpark, etc.). It contains no executable code, no data pipelines, no notebooks, and no analytical models. The last commit was in 2019, and the repository has been inactive since. It serves purely as a reference resource for practitioners, not as a functional software project or data research artifact. There are no reusable components, no data lineage, and no reproducibility possible. The repository is effectively a curated set of external cheat sheets, not a codebase to be analyzed for architecture or engineering quality.