Appendix: Python Packages

The following is general overview of packages available in Python that may be useful for data science.

For a much broader / fuller list of the Python ecosystem, check out the Awesome Python list.

Data-Science Modules

These are all external (non-standard library) packages. Many of them are available in the Ananconda distribution.

Core Packages

• scipy - mathematics, science, and engineering.

• numpy - numerical computing with arrays & array operations.

• pandas - data structures and data analysis.

• scikit-learn - machine learning and data analysis.

Text Mining

• nltk - natural language processing.

• gensim - topic modelling.

Mathematics & Statistics

• sympy - symbolic mathematics.

• statsmodels - statistical modelling.

Web Scraping

• requests - HTTP requests.

• scrapy - web scraping.

Plotting / Vizualization Libraries

• matplotlib - 2D plotting library.

• seaborn - visualization (based on matplotlib).

• bokeh - interactive visualizations.

Graph Theory / Networks

• networkx - network analysis.

• graph-tool - manipulation and analysis of graphs.

Deep Learning

• theano - mathematical operations on multi-dimensional arrays.

• tensorflow - numerical computation using data flow graphs.

• keras - a high-level neural network library.

Useful parts of the standard library

The full list of packages in the standard library is available here.

Basic Utilities

• os - miscellaneous operating system operations.

• sys - system operations.

• datetime - manipulating dates & times.

• glob - searching path names.

Useful Functions

• math - mathematical functions.

• random - (pseudo) random number generators.

• re - regular expressions.

File Formats

• json - support for working with JSON files.

• csv - support for working with CSV files.

Data Objects

• collections - container data types.

• pickle - serializing & de-serializing (saving and loading complex objects).