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. 

A library for transfer learning by reusing parts of TensorFlow models.

TensorFlow Hub is a repository of reusable assets for machine learning with TensorFlow. In particular, it provides pre-trained SavedModels that can be reused to solve new tasks with less training time and less training data. This GitHub repository hosts the tensorflow_hub Python library to download and reuse SavedModels in your TensorFlow program with a minimum amount of code, as well as other associated code and documentation. 

An Open Source Machine Learning Framework for Everyone

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well. TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages. Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists. 

Food Consumption Prediction

Data Science

pandasby pandas-dev

pandasby pandas-dev

scipyby scipy

scipyby scipy

Data Visualization

matplotlibby matplotlib

matplotlibby matplotlib

seabornby mwaskom

seabornby mwaskom

machine learning

hubby tensorflow

hubby tensorflow

tensorflowby tensorflow

tensorflowby tensorflow