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Panda is a frontend design resource for CSS frameworks, UI components, and web interfaces.
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panda-css.com
Panda CSS is a styling engine that generates atomic CSS from written styles at build time with no runtime cost. It supports design tokens, semantic tokens and themes, and recipes with variants, all type-safe. It runs through the CLI, PostCSS or a bundler, generating a typed styled-system directory for import.
pandabomber.gg
PandaBomber is a gaming resource for guides, communities, tools, game discovery, or reference links.
pandacd.io
PandaCD is a file-sharing community where artists share their own music under Creative Commons and artist-permitted licences. Users can pay what they want with cryptocurrency, and the site lists torrents filtered by tags, licence and payment option.
paperpanda.app
Paper Panda is an academic paper search platform that provides access to 250 million research papers. Users can locate paper PDFs, verify open access status, explore related citations, and access full-text PDFs in one click. The service reduces time spent navigating paywalls, login requirements, and redirect pages when retrieving research content.
github.com
DataComPy is a package for comparing two DataFrames or tables. It supports Pandas, Polars, Spark (classic and Spark Connect), and Snowflake, and was originally created as a replacement for SAS PROC COMPARE for Pandas DataFrames. It prints stats and lets users adjust required match accuracy.
pandas.pydata.org
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. It is described as a data analysis and manipulation tool built on top of Python. Documentation, a user guide and an API reference are available.
github.com
Create HTML profiling reports from pandas DataFrame objects.
github.com
An intuitive GUI for Pandas DataFrames.
github.com
NumPy and Pandas interface to Big Data.
codecademy.com
Codecademy's Data Scientist: Machine Learning Specialist Career Path is a beginner-level course covering Python 3, SQL, pandas, scikit-learn, Matplotlib, and TensorFlow. The path is listed as taking 95 hours on average and includes 59 projects with no prerequisites, and is described as teaching analysis of data, drawing predictions with machine learning, and building neural networks.
github.com
d3py is a plotting library for Python based on D3. It builds on d3.js and the pandas DataFrame module to plot data from the command line or simple scripts into a browser window. Its README describes the code as an experiment, notes that it is not actively developed, and points to vincent as an alternative.
dask.org
Dask is an open-source Python library for parallel computing maintained by contributors across dozens of companies. The site describes uses including Dask DataFrames for pandas, parallel for loops, large arrays with NumPy and Xarray, and machine learning. It can run on a laptop or be deployed on resource managers such as Kubernetes or cloud.
dataschool.io
Data School is a blog about data science and machine learning. Its posts cover topics such as the difference between AI and machine learning, learning scikit-learn, AI progress, and using pandas for calculations. Posts are tagged by subject including Python, AI, and machine learning.
github.com
可循环值守和多人录制的直播录制软件，支持抖音、TikTok、Youtube、快手、虎牙、斗鱼、B站、小红书、pandatv、sooplive、flextv、popkontv、twitcasting、winktv、百度、微博、酷狗、17Live、Twitch、Acfun、CHZZK、shopee等40+平台直播录制
github.com
PandasAI is an open-source library that enables conversational data analysis using large language models and retrieval-augmented generation. It allows users to interact with databases or datalakes supporting SQL, CSV, and parquet file formats.
github.com
HDBSCAN is a high performance implementation of hierarchical density-based clustering. It performs DBSCAN over varying epsilon values and integrates results to find a clustering with the best stability over epsilon, allowing clusters of varying densities. The package inherits from sklearn classes and supports arrays, pandas dataframes, sparse matrices or distance matrices as input.
github.com
A GitHub repository holding the notebooks and code for the book "Introduction to Machine Learning with Python" by Andreas Mueller and Sarah Guido, together with the mglearn library of helper functions for figures and datasets. The book requires scikit-learn 0.20.0, and the code also needs numpy, scipy, matplotlib, pandas and pillow.
github.com
Koalas, from Databricks, implements the pandas DataFrame API on top of Apache Spark. The repository is marked deprecated and in maintenance mode; Koalas supports Apache Spark 3.1 and below, and PySpark is recommended directly for Spark 3.2 and above. It is available under the Apache-2.0 license.
github.com
Lux is a Python library for automated data exploration. When a pandas dataframe is printed in a Jupyter notebook, Lux recommends a set of visualizations highlighting trends and patterns, shown in an interactive widget for browsing and exploring the dataset.
github.com
Mars is an open source tensor-based unified framework for large-scale data computation. It scales numpy, pandas and scikit-learn, offering tensor, DataFrame and learn interfaces that resemble the original libraries. A session can be started locally or connected to an existing Mars cluster.
hairysun.com
Matt Harrison is a personal blog authored by Matt Harrison. The blog covers topics including Amazon book piracy, the Effective Pandas book, Python and data science courses, and general software development content.
mimi-panda.com
Mimi Panda is a service for converting images into coloring pages and paint by numbers, using graphic techniques and algorithms developed by its team. It offers free coloring pages and a paint by numbers converter, an API service for enterprise clients, and a subscription-based Mimi Profile service with higher quality and a personal account.
github.com
ML/AI Interview Prep is a GitHub-hosted resource collection for machine learning and artificial intelligence technical interview preparation. It includes over 500 interview questions and answers with accompanying code, cheat sheets for NumPy, Pandas, PyTorch, and scikit-learn, and system design content targeted at candidates for roles at companies including Google, Meta, and OpenAI.