There are several key libraries and packages in the Python ecosystem for data science, although the list is far from complete.

Basics

• python.org: main Python website
• Python documentation
• Python Package Index (PyPI): repository of packages

"Batteries Included" Python Distributions

• Anaconda
• Enthought
• Active State
• Intel

Data Structures and Algorithms

• The Python Standard Library (select specific version on that page)
• NumPy: multi-dimensional arrays, array-level mathematical operations, linear algebra, random numbers, etc.
• SciPy: a Python-based ecosystem of open-source software for mathematics, science, and engineering
• Pandas: dataframes and series for representing tabular data, and rich set of operations for analyzing such data
• H5py: support for HDF5-formatted data files
• Dask: distributed arrays and dataframes, scheduling of distributed workflows
• Sympy: symbolic mathematics
• NetworkX: creation, manipulation, and study of the structure, dynamics, and functions of complex networks
• igraph: a network analysis library, written in C++, with front-ends in Python, R, Mathematica

Interpreters, Notebooks and Development Environments

• IPython: powerful interactive Python shell
• Jupyter: interactive notebooks integrating code, results, graphics and documentation
• Spyder: integrated development environment (IDE) combining editing, analysis, debugging, and profiling functionality

Data Visualization

• Matplotlib: 2D plotting library that makes easy things easy and hard things possible
• Seaborn: data visualization library based on matplotlib, providing a high-level interface for informative statistical graphics
• Bokeh: interactive visualization library that targets modern web browsers for presentation
• Plotly: interactive visualization library that targets modern web browsers for presentation

Relational Databases

• sqlalchemy: Python SQL toolkit and Object Relational Mapper, providing access to SQL databases
• sqlite3: Python interface to SQLite, a C library that provides a lightweight disk-based SQL database

Scientific Computing and Statistics

• Statsmodels: classes and functions for the estimation of statistical models, conducting statistical tests, and statistical data exploration
• Scikits: add-on packages for SciPy, hosted and developed separately and independently from the main SciPy distribution, providing more specialized functionality in a large number of topic areas

Machine Learning

• Scikit-learn: many different machine learning methods in Python
• TensorFlow: Deep Learning in Python (an end-to-end open source platform for machine learning)
• PyTorch: open source machine learning framework that accelerates the path from research prototyping to production deployment
• Keras: high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
• Caffe: deep learning framework made with expression, speed, and modularity in mind

Image Processing

• Scikit-image: collection of algorithms for image processing
• Pillow: a fork of PIL, the Python Imaging Library

Natural Language Processing

• Natural Language Toolkit (NLTK): platform for building Python programs to work with human language data
• Spacy: industrial-strength natural language processing in Python
• Textblob: Python library for processing textual data
• python-Levenshtein: for computing string similarities and edit distances

Application-Specific APIs

• Tweepy: Python library for accessing the Twitter API

Systems for Big Data

• PySpark: Python interface to the Spark programming model