A collection of Python external modules and their usage.
A Collection of Python Modules
A collection of Python external modules and their usage, by category. The names are the module names as written in the code import ... , not the package name.
How to install the packages can be found in their respective homepages/documentations. Built-in modules are not included.
Communication Protocol
Module name
Description
paramiko
Python implementation of the SSH protocol, interface to connect via SSH.
requests
HTTP library.
Computer Vision / Image Processing / Video Processing
Module name
Description
cv2 (OpenCV)
Library for computer vision.
moviepy
Library for video processing.
PIL (Pillow)
Library for image processing.
Cryptography
Module name
Description
cryptography
Cryptographic recipes and primitives, and some common cryptographic algorithms.
Finance / Stocks / Investors Exchange (IEX)
Module name
Description
iex
Python wrapper for the IEX API.
iexfinance
Python SDK for IEX Cloud API.
pyEX
Python interface to IEX Cloud API.
Graph Theory
Module name
Description
cvxgraphalgs
Convex optimization algorithms to solve problems on graphs.
networkx
Build and draw graphs and basic graph algorithms.
Graphical User Interface (GUI)
Module name
Description
PyQt4, PyQt5
Python binding to the Qt GUI toolkit.
tkinter
Python binding to the Tk GUI toolkit.
wx (wxPython)
Python wrapper for wxWidgets.
Machine Learning
Module name
Description
autokeras
A simpler deep learning framework based on Keras.
keras
A simple deep learning API based on TensorFlow 2.0.
pytorch
Machine learning library mainly for applications, e.g. computer vision and natural language processing.
sklearn
Machine learning library for beginners to experiment with.
tensorflow
Core machine learning library for building and training neural networks.
Numerical Computing / Mathematical Optimization
Module name
Description
cvxpy
Models and algorithms for convex optimization problems.
matplotlib
Visualization tools for plotting graphs and drawing figures.
numpy
Arrays, matrices, and linear algebra.
pandas
Data analysis and data manipulation, suitable for data science.
scipy
Routines for scientific programming, e.g. numerical integration, interpolation, optimization, etc.