Explore the Data Science Stack (Python, Pandas, NumPy, Matplotlib) stack—your complete toolkit for data analysis, manipulation, and visualization. This open source ecosystem, centered around Python, enables robust and efficient workflows for data scientists. Discover top libraries and repositories, learn about their categories, and stay updated with the latest releases in the data science world.
feature.delivery is a free, web-based platform that enables developers to monitor and consolidate software releases from multiple GitHub repositories into a single, streamlined chronological view. By centralizing release information across various tools, libraries, and services, feature.delivery simplifies the process of staying informed about the latest updates in a development stack. Stay ahead of the curve with feature.delivery, the free online tool designed to help developers effortlessly track and consolidate the latest releases from multiple GitHub repositories in one clean, chronological view. Whether you're managing a complex development stack or simply want to stay up to date with your favorite open-source projects, feature.delivery streamlines release tracking so you never miss an important update. By keeping up with the latest changes, developers can quickly adopt new features, enhance performance, and maintain a competitive edge in today's fast-moving tech landscape. Say goodbye to manual tracking and hello to smarter, faster development with feature.delivery.

https://feature.delivery

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PyTables/
PyTables

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bokeh/
bokeh

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ipython/
ipython

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joblib/
joblib

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jupyter/
notebook

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matplotlib/
matplotlib

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mwaskom/
seaborn

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numpy/
numpy

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openpyxl/
openpyxl

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pandas-dev/
pandas

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plotly/
plotly.py

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python/
cpython

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scikit-learn/
scikit-learn

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scipy/
scipy

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sqlalchemy/
sqlalchemy

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statsmodels/
statsmodels

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tqdm/
tqdm

Use this link to track latest updates across the 17 repositories in Data Science Stack

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Staying up-to-date with latest features of the
Data Science Stack in 2026

How does it work?

feature.delivery is a free, web-based platform that helps developers track the latest releases from multiple GitHub repositories — all in one streamlined, chronological view. By centralizing release information across tools, libraries, and frameworks, feature.delivery makes it easier than ever to stay on top of the updates throughout your development stack.

Checkout this 1 minute intro video to see it in action

The Data Science Stack (Python, Pandas, NumPy, Matplotlib) stack offers a powerful suite of tools for data analysis, manipulation, and visualization. Leveraging the versatility of Python, the efficiency of NumPy, the flexibility of Pandas, and the visualization prowess of Matplotlib, this stack empowers data scientists and analysts to extract valuable insights from data. The stack facilitates a streamlined workflow for data preprocessing, statistical modeling, and interactive visualizations, making it an essential choice for modern data-driven projects.

Here's a breakdown of the Data Science Stack into different categories

Core Libraries

The core libraries are the backbone of the Data Science Stack, providing essential capabilities for data manipulation, scientific computing, and statistical analysis. These libraries form the foundation for most data science workflows and are widely adopted in the industry.

python/cpython

python/cpython
The official implementation of the Python programming language, used as the base for the entire data science stack.
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numpy/numpy

numpy/numpy
The fundamental package for scientific computing with Python, offering support for large, multi-dimensional arrays and matrices.
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pandas-dev/pandas

pandas-dev/pandas
An open source data analysis and manipulation tool, built on top of the Python programming language.
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scipy/scipy

scipy/scipy
A Python-based ecosystem of open-source software for mathematics, science, and engineering.
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Data Visualization

Data visualization libraries allow users to represent data insights visually, making complex analyses accessible and understandable. These libraries provide tools for creating static, animated, and interactive plots.

matplotlib/matplotlib

matplotlib/matplotlib
A comprehensive library for creating static, animated, and interactive visualizations in Python.
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mwaskom/seaborn

mwaskom/seaborn
Statistical data visualization built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics.
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bokeh/bokeh

bokeh/bokeh
Interactive visualization library that targets modern web browsers for presentation.
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plotly/plotly.py

plotly/plotly.py
A Python graphing library for making interactive, publication-quality graphs online.
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Machine Learning & Statistical Modeling

Machine learning and statistical modeling libraries enable predictive analytics and complex data modeling, supporting a wide range of algorithms and data processing utilities.

scikit-learn/scikit-learn

scikit-learn/scikit-learn
A machine learning library that features various classification, regression and clustering algorithms.
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statsmodels/statsmodels

statsmodels/statsmodels
Statistical modeling and econometrics in Python.
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Data Access & Storage

Libraries in this category facilitate reading, writing, and processing data from various sources, including flat files, databases, and web APIs, ensuring data is easily accessible for analysis.

sqlalchemy/sqlalchemy

sqlalchemy/sqlalchemy
The Python SQL toolkit and Object Relational Mapper for efficient database access.
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PyTables/PyTables

PyTables/PyTables
A package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.
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Interactive Computing & Notebooks

Interactive computing environments and notebook interfaces streamline experimentation, code sharing, and reproducibility in data science workflows.

jupyter/notebook

jupyter/notebook
Web-based notebook environment for interactive computing and data science workflows.
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ipython/ipython

ipython/ipython
A powerful interactive shell for Python, enhancing productivity and code exploration.
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Data Cleaning & Preprocessing

Data cleaning and preprocessing libraries provide essential tools for transforming raw data into a usable format, handling missing values, encoding, and normalization.

scikit-learn/scikit-learn

scikit-learn/scikit-learn
Offers robust preprocessing utilities, including standardization, normalization, and imputation.
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pandas-dev/pandas

pandas-dev/pandas
Pandas supports data cleaning with extensive data wrangling and manipulation features.
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Data Import/Export & File Formats

This category includes libraries that support reading and writing data in multiple formats such as CSV, Excel, JSON, HDF5, and more.

pandas-dev/pandas

pandas-dev/pandas
Built-in support for reading and writing a variety of file formats including CSV, Excel, JSON, and more.
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openpyxl/openpyxl

openpyxl/openpyxl
A Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files.
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Utilities & Productivity Tools

Utilities and productivity libraries streamline tasks such as progress monitoring, parallel computation, and code optimization to maximize efficiency in data science projects.

tqdm/tqdm

tqdm/tqdm
A fast, extensible progress bar for loops and data processing.
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joblib/joblib

joblib/joblib
A library for lightweight pipelining in Python, especially for machine learning processing.
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Stay ahead in data science by exploring these repositories and their latest releases. Click on the provided GitHub URLs to discover updates, new features, and community contributions that keep the Data Science Stack (Python, Pandas, NumPy, Matplotlib) stack at the forefront of data-driven innovation.