Welcome to University of Southampton VLC repository for python learning and development.
The aim of this repository is to provide complex examples to the Python programming language and extended libraries. It contains many multidisciplinary examples to write code for, on a range of coding topic areas, particularly for research purposes.
In this repository we break down areas of knowledge into the following categories:
- Basics: Learning the python programming language and in-built libraries
- Scientific Computing and Simulation: Learning packages
numpy,scipyanddask, with a wealth of application problem domains to explore, including Monte Carlo, Stochastic Differential Equations and more. The performance aspects of simulation are also explored in detail. - Data analysis: Learning how to load, preprocess, munge and work with
tabular relational datasets using
pandas, with additional focus on networks usingnetworkx. - Visualisation: Learning the best practices for visualising results
from a model or dataset, beginning with low-level tools like
matplotlibbut moving on to interactive tools likebokeh,plotlyandseaborn. - Machine Learning: Learning the basics of machine learning from a
mathematical, statistical and applicational basis, including classification, regression,
dimensionality reduction and clustering using
sklearn.
We recommend you explore them in the order presented, but there is no hard prerequisite for any section.
In principles we aim to cover the basics of the Python programming language and more - in this module we draw only from internal modules/packages that are in-built into the language. We start from saying 'Hello World'; a classic introduction to any programming language:
print("Hello World")All the way through to more performance-friendly implementations using the powerful itertools internal module, and performing complex string searching and extraction using Regex. This module is definitely for those either with or without previous programming experience, and will throw you in the deep end with complex tasks at the end of each notebook.
Simulation is now considered to be the third pillar of science, alongside observation and experimentation. Gaining skills to develop simulations that closely resemble real-world applications is highly desirable that not only contributes to high-paying jobs, but also to push forward the frontiers of engineering and science.
With simulation, users will be introduced to the extremely powerful NumPy package with it's C-style performance array and matrix operations.
import numpy as np
x = np.array([0., 1., 1., 3.])In addition, users will learn plenty of mathematical operations to be performed on computer such as differentiation, integration, and interpolation. We introduce SciPy for scientific computing, as well as performance-related practice using Dask to enable scalability for large problems that can be automatically distributed to supercomputer clusters.
import dask.array as da
x = da.arange(25, chunks=5).compute()Users will enjoy multi-disciplinary experience in two notebooks with complex tasks that involve building simulations taking hours to complete.
In the internet age, there is now more data than ever accessible and the list of problem domains that intelligently use data is growing exponentially. Gaining skills to preprocess and manipulate data sensibly are in high demand.
This module will introduce you to Pandas, a flexible, high-performance data structure for handling relational data, which is built on top of NumPy.
import pandas as pd
x = pd.Series(data=[3, 1, 4], index=["apples", "oranges", "bananas"])This includes complex database manipulations including merges, unions and intersects, aggregation and reshaping.
| Value | Patient | Phylum | |
|---|---|---|---|
| 0 | 632 | 1 | Firmicutes |
| 1 | 1638 | 1 | Proteobacteria |
| 2 | 433 | 2 | Firmicutes |
| 3 | 1130 | 2 | Proteobacteria |
We also explore the use of graph data using nodes and edges with the powerful NetworkX package. Users will be introduced to a number of complex tasks and examples where they manipulate datasets from multiple disciplines.
With large amounts of data, this presents challenges as to how to articulate results in an effective manner, knowledge of powerful visualisation tools and methods will enable users to present research or findings effectively.
We introduce a number of different packages, most of which are built on Matplotlib, ported from MATLAB, and include Seaborn and Plotly.
Users will become very familiar with basic plots, and will progress onto animated and interactive plots to model complex problems that change in space and time.
Inspirations:
Requirements for each subdirectory are stated in the respective README files.
We have further notebooks which weren't clearly grouped into a particular section
added in the Extras/ folder.
The course is covered as interactive Jupyter notebooks which makes things considerably easier. In order to follow this course, we recommend you download Python using the Anaconda distribution (found here) as this also provides most of the packages used in this course.
Ensure that any use of this material is appropriately referenced and in compliance with the license.
All rights reserved.
Original Owner: Gregory Parkes, see contributions and page here. Transferred over to VLC on 04/03/2020.