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 

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

Statsmodels: statistical modeling and econometrics in Python

A Python scikit for building and analyzing recommender systems

Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. Give users perfect control over their experiments. To this end, a strong emphasis is laid on documentation, which we have tried to make as clear and precise as possible by pointing out every detail of the algorithms. Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets. Provide various ready-to-use prediction algorithms such as baseline algorithms, neighborhood methods, matrix factorization-based ( SVD, PMF, SVD++, NMF), and many others. Also, various similarity measures (cosine, MSD, pearson...) are built-in. Make it easy to implement new algorithm ideas. Provide tools to evaluate, analyse and compare the algorithms' performance. Cross-validation procedures can be run very easily using powerful CV iterators (inspired by scikit-learn excellent tools), as well as exhaustive search over a set of parameters. 

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

Team HungryHacks - Solving Food Insecurity Problems (Zero Hunger, Zero Waste)

Group Name 1

pandasby pandas-dev

pandasby pandas-dev

Group Name 2

seabornby mwaskom

seabornby mwaskom

statsmodelsby statsmodels

statsmodelsby statsmodels

matplotlibby matplotlib

matplotlibby matplotlib

Kit Solution Source

Surpriseby NicolasHug

Surpriseby NicolasHug

Kit Deployment Instructions

notebookby jupyter

notebookby jupyter

Group Name 6

next.jsby vercel

next.jsby vercel

Group Name 5

HackMaker-BuildWithAI-ML_Modelby ayaz95

HackMaker-BuildWithAI-ML_Modelby ayaz95

hackathon-buildwithaiby tawanorg

hackathon-buildwithaiby tawanorg