Milad Alizadeh introduces 'kitcat', a new matplotlib backend designed for plotting directly within compatible terminals. Frustrated with Jupyter notebooks as a development environment, the author sought a leaner, script-based workflow for matplotlib plotting. Kitcat leverages the graphic protocol popularized by Kitty, allowing image data to be sent via escape codes to terminals supporting this functionality, such as Yazi.
The backend encodes PNG pixel data into base64 and transmits it, supporting chunked data for larger images. While acknowledging the existence of 'matplotlib-backend-kitty', kitcat aims for broader compatibility across terminals supporting the protocol. Future development may include support for iTerm2 animations and the Sixel graphics standard.
The backend encodes PNG pixel data into base64 and transmits it, supporting chunked data for larger images. While acknowledging the existence of 'matplotlib-backend-kitty', kitcat aims for broader compatibility across terminals supporting the protocol. Future development may include support for iTerm2 animations and the Sixel graphics standard.
"Talk to your data. Instantly analyze, visualize, and transform."
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
A guide to building a front-end data application using Taipy, comparing it to Streamlit and Gradio, and providing a step-by-step implementation of a sales performance dashboard.
The article details the author's investigation into slow Python tool startup times. They used the `python -X importtime` feature to identify import bottlenecks and visualized the resulting data using Kevin Michel's `python-importtime-graph` tool, revealing a dense treemap of import times.
This example demonstrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) using scikit-learn, showing how to generate synthetic clusters, compute DBSCAN clustering, and visualize the results, including core and non-core samples.
This article introduces Streamlit, a Python library for building data dashboards, as a solution for Python programmers to create graphical front-ends without needing to delve into CSS, HTML, or JavaScript. The author, a seasoned data engineer, explains how Streamlit and similar tools enable the creation of attractive dashboards, marking a shift from traditional tools like Tableau or Quicksight. This piece serves as the first in a series focusing on Streamlit, with future articles planned on Gradio and Taipy. The author aims to replicate similar layouts and functionalities across dashboards using consistent data.
PySpecSDR is a Python-based Software Defined Radio (SDR) spectrum analyzer with real-time visualization, demodulation, and signal analysis capabilities.
A step-by-step guide on understanding and implementing t-SNE for visualizing high-dimensional data using Python.
Shiny for Python lets you build interactive web applications with ease. It utilizes reactive programming for efficient and dynamic visualizations.
This article introduces Path-Swarm and Super-Swarm, new techniques for creating swarm charts using circle arrangements for data visualization. The author, Nick Gerend, discusses two primary swarm techniques and some extensions for rapid visual exploration of data. Written for Towards Data Science.
