Python:
- Python:
the Python interpreter
- Matplotlib:
Python 2D plotting library (https://matplotlib.org/)
- networkx:
Python package for creating and manipulating complex networks (https://networkx.github.io/)
- NumPy:
fundamental package for scientific computing with Python (https://www.numpy.org/)
- Pandas:
high-performance, easy-to-use data structures and data analysis tools (https://pandas.pydata.org/)
- scikit-learn:
machine learning in Python (http://scikit-learn.org/)
- seaborn:
statistical data visualization (https://seaborn.pydata.org/)
- statsmodels:
implementation of different statistical models and tests (https://www.statsmodels.org/)
Install the following:
- cartopy:
a library providing cartographic tools for Python (http://scitools.org.uk/cartopy/).
Only required if you want to run all examples from the book
- graphviz:
Application to visualize graphs (https://www.graphviz.org/)
- python-graphviz:
Python interface for graphviz (https://graphviz.readthedocs.io/en/stable/)
- pydotplus:
Python interface to graphviz’s dot language. Required to visualize
decision trees (http://pydotplus.readthedocs.io/)
- gmaps:
Python interface to Google maps. See appendix for details about installing
this package (https://github.com/pbugnion/gmaps)
- nltk:
Natural language processing toolkit. Required for more advanced text
mining applications (https://www.nltk.org/)
- mlxtend:
machine learning library that provides access to association rules mining
algorithms (https://github.com/rasbt/mlxtend)
- scikit-surprise:
a library for recommender systems (http://surpriselib.com/)
- squarify:
algorithm to layout tree map visualizations (https://github.com/laserson/squarify)
- twython: pure Python wrapper for the Twitter API. Supports both normal and streaming Twitter APIs (https://twython.readthedocs.io/en/latest/)
