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Data Science & Notebooks projects

Notebooks, data pipelines, ETL, and analytics — turning raw data into insight.

20 projects tracked.

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Top 20 in Data Science & Notebooks

PopScore over the last 7 day(s)

  • 1

    scrapy/scrapy

    Scrapy, a fast high-level web crawling & scraping framework for Python.

    HealthyEnterprise
    Pop
    0
    Python#python#scraping#crawling
    53.9k10.6k
  • 2

    docling-project/docling

    Get your documents ready for gen AI

    HealthyEnterprise
    Pop
    0
    Python#ai#convert#documents
    65.0k4.8k
  • 3

    duckdb/duckdb

    An in-process SQL OLAP database management system.

    SlowingEnterprise
    Pop
    0
    C++#olap#embedded#analytics
    24.4k1.8k
  • 4

    pola-rs/polars

    Dataframes powered by a multithreaded, vectorized query engine, written in Rust.

    SlowingEnterprise
    Pop
    0
    Rust#dataframes#rust#olap
    30.2k1.8k
  • 5

    streamlit/streamlit

    Streamlit — the fastest way to build data apps in Python.

    SlowingEnterprise
    Pop
    0
    Python#dashboard#data-app#ml-ui
    36.4k3.2k
  • 6

    gradio-app/gradio

    Build and share delightful machine learning apps, all in Python.

    SlowingEnterprise
    Pop
    0
    Python#dashboard#ml-ui#demos
    38.4k2.4k
  • 7

    apache/airflow

    Apache Airflow — a platform to programmatically author, schedule, and monitor workflows.

    SlowingEnterprise
    Pop
    0
    Python#workflow#etl#pipelines
    38.2k14.2k
  • 8

    ray-project/ray

    Ray is a unified framework for scaling AI and Python applications.

    SlowingEnterprise
    Pop
    0
    Python#distributed#ml#scaling
    35.4k5.8k
  • 9

    mlflow/mlflow

    Open source platform for the machine learning lifecycle.

    SlowingEnterprise
    Pop
    0
    Python#mlops#tracking#experiments
    20.4k4.2k
  • 10

    explosion/spacy

    Industrial-strength NLP in Python.

    SlowingEnterprise
    Pop
    0
    Python#nlp#ml#python
    30.4k4.5k
  • 11

    pandas-dev/pandas

    Flexible and powerful data analysis / manipulation library for Python.

    SlowingEnterprise
    Pop
    0
    Python#dataframes#analysis#python
    44.8k18.4k
  • 12

    numpy/numpy

    The fundamental package for scientific computing with Python.

    SlowingEnterprise
    Pop
    0
    C#numerical#arrays#python
    28.4k9.2k
  • 13

    scikit-learn/scikit-learn

    Machine learning in Python.

    SlowingEnterprise
    Pop
    0
    Python#machine-learning#python#models
    60.4k25.2k
  • 14

    plotly/plotly.py

    The interactive graphing library for Python.

    SlowingMature
    Pop
    0
    Python#visualization#charts#interactive
    16.4k2.5k
  • 15

    PrefectHQ/prefect

    Prefect is a workflow orchestration tool for Python.

    SlowingMature
    Pop
    0
    Python#workflow#etl#orchestration
    16.2k1.4k
  • 16

    jupyter/jupyter

    Jupyter metapackage for installation and documentation.

    SlowingMature
    Pop
    0
    TypeScript#notebook#repl#kernels
    14.8k4.4k
  • 17

    mwaskom/seaborn

    Statistical data visualization in Python.

    SlowingMature
    Pop
    0
    Python#visualization#stats#matplotlib
    12.4k2.0k
  • 18

    dagster-io/dagster

    An orchestration platform for the development, production, and observation of data assets.

    SlowingEnterprise
    Pop
    0
    Python#workflow#etl#orchestration
    12.4k1.4k
  • 19

    dask/dask

    Parallel computing with task scheduling for Python.

    SlowingMature
    Pop
    0
    Python#parallel#dataframes#scaling
    12.4k1.7k
  • 20

    smol-ai/smol-course

    A hands-on course on aligning small language models.

    SlowingMature
    Pop
    0
    Jupyter Notebook#llm#course#alignment
    6.4k580
    AIHands-on, code-first course for aligning small LLMs (SFT + DPO + eval) on a single GPU.expand
    Hands-on, code-first course for aligning small LLMs (SFT + DPO + eval) on a single GPU.

How GitPop ranks projects

Read the full methodology
What is PopScore?
A 0–100 score combining star velocity (35%), fork engagement (20%), activity (25%), mentions (10%), and a noise filter (10%). Details →
Why not just use GitHub Trending?
Trending is dominated by awesome-* lists and tutorial repos. We weight commits and merged PRs above raw stars, and apply a noise penalty for content-only repos. Details →
How is the AI Snapshot generated?
An LLM summarizes each repo's README into strict JSON — TL;DR, 3 key features, difficulty rating. Cached and refreshed automatically. Details →