The Jupyter notebook is a web-based notebook environment for interactive computing. 

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating point data Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based slicing, fancy indexing, and subsetting of large data sets Intuitive merging and joining data sets Flexible reshaping and pivoting of data sets Hierarchical labeling of axes (possible to have multiple labels per tick) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging 

The fundamental package for scientific computing with Python.

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics. 

FastAPI framework, high performance, easy to learn, fast to code, ready for production

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints. Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available. Fast to code: Increase the speed to develop features by about 200% to 300%. * Fewer bugs: Reduce about 40% of human (developer) induced errors. * Intuitive: Great editor support. Completion everywhere. Less time debugging. Easy: Designed to be easy to use and learn. Less time reading docs. Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs. Robust: Get production-ready code. With automatic interactive documentation. Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. 

The Python micro framework for building web applications.

raj Kit

Group Name 1

notebookby jupyter

notebookby jupyter

vscodeby microsoft

vscodeby microsoft

pandasby pandas-dev

pandasby pandas-dev

Group Name 2

numpyby numpy

numpyby numpy

scipyby scipy

scipyby scipy

matplotlibby matplotlib

matplotlibby matplotlib

Group Name 3

seabornby mwaskom

seabornby mwaskom

fastapiby tiangolo

fastapiby tiangolo

flaskby pallets

flaskby pallets