About Python
Table of Contents
- Overview
- What’s Python?
- Scientific Programming
- Learn More
##Overview
In this lecture we will
Outline what Python is Showcase some of its abilities Compare it to some other languages At this stage it’s not our intention that you try to replicate all you see
We will work through what follows at a slow pace later in the lecture series
Our only objective for this lecture is to give you some feel of what Python is, and what it can do
What’s Python?
Python is a general purpose programming language conceived in 1989 by Dutch programmer Guido van Rossum
Python is free and open source, with development coordinated through the Python Software Foundation
Python has experienced rapid adoption in the last decade, and is now one of the most popular programming languages
Common Uses
Python is a general purpose language used in almost all application domains
communications web development CGI and graphical user interfaces games multimedia, data processing, security, etc., etc., etc. Used extensively by Internet service and high tech companies such as
Google Dropbox Reddit YouTube Walt Disney Animation, etc., etc. Often used to teach computer science and programming
For reasons we will discuss, Python is particularly popular within the scientific community
academia, NASA, CERN, Wall St., etc., etc. Relative Popularity The following chart, produced using Stack Overflow Trends, shows one measure of the relative popularity of Python
The figure indicates not only that Python is widely used but also that adoption of Python has accelerated significantly since 2012
We suspect this is driven at least in part by uptake in the scientific domain, particularly in rapidly growing fields like data science
For example, the popularity of pandas, a library for data analysis with Python has exploded, as seen here
(The corresponding time path for MATLAB is shown for comparison)
Note that pandas takes off in 2012, which is the same year that we seek Python’s popularity begin to spike in the first figure
Overall, it’s clear that
Python is one of the most popular programming languages worldwide Python is a major tool for scientific computing, accounting for a rapidly rising share of scientific work around the globe Features Python is a high level language suitable for rapid development
It has a relatively small core language supported by many libraries
Other features:
A multiparadigm language, in that multiple programming styles are supported (procedural, object-oriented, functional, etc.) Interpreted rather than compiled Syntax and Design One nice feature of Python is its elegant syntax — we’ll see many examples later on
Elegant code might sound superfluous but in fact it’s highly beneficial because it makes the syntax easy to read and easy to remember
Remembering how to read from files, sort dictionaries and other such routine tasks means that you don’t need to break your flow in order to hunt down correct syntax
Closely related to elegant syntax is elegant design
Features like iterators, generators, decorators, list comprehensions, etc. make Python highly expressive, allowing you to get more done with less code
Namespaces improve productivity by cutting down on bugs and syntax errors
Scientific Programming
Python has become one of the core languages of scientific computing
It’s either the dominant player or a major player in
Machine learning and data science Astronomy Artificial intelligence Chemistry Computational biology Meteorology etc., etc. Its popularity in economics is also beginning to rise
This section briefly showcases some examples of Python for scientific programming
All of these topics will be covered in detail later on
Numerical programming
Fundamental matrix and array processing capabilities are provided by the excellent NumPy library
NumPy provides the basic array data type plus some simple processing operations
For example, let’s build some arrays
import numpy as np # Load the library
a = np.linspace(-np.pi, np.pi, 100) # Create even grid from -π to π b = np.cos(a) # Apply cosine to each element of a c = np.sin(a) # Apply sin to each element of a Now let’s take the inner product:
b @ c 2.706168622523819e-16 The number you see here might vary slightly but it’s essentially zero
(For older versions of Python and NumPy you need to use the np.dot function)
The SciPy library is built on top of NumPy and provides additional functionality
For example, let’s calculate ∫2−2ϕ(z)dz where ϕ is the standard normal density
from scipy.stats import norm from scipy.integrate import quad
ϕ = norm() value, error = quad(ϕ.pdf, -2, 2) # Integrate using Gaussian quadrature value 0.9544997361036417 SciPy includes many of the standard routines used in
linear algebra integration interpolation optimization distributions and random number generation signal processing etc., etc. Graphics The most popular and comprehensive Python library for creating figures and graphs is Matplotlib
Plots, histograms, contour images, 3D, bar charts, etc., etc. Output in many formats (PDF, PNG, EPS, etc.) LaTeX integration Example 2D plot with embedded LaTeX annotations
Example contour plot
Example 3D plot
More examples can be found in the Matplotlib thumbnail gallery
Other graphics libraries include
Plotly Bokeh VPython — 3D graphics and animations
Symbolic Algebra
It’s useful to be able to manipulate symbolic expressions, as in Mathematica or Maple
The SymPy library provides this functionality from within the Python shell
from sympy import Symbol
x, y = Symbol(‘x’), Symbol(‘y’) # Treat ‘x’ and ‘y’ as algebraic symbols x + x + x + y 3*x + y We can manipulate expressions
expression = (x + y)** 2
expression.expand() x2 + 2xy + y2 solve polynomials
from sympy import solve
solve(x*2 + x + 2) [-1/2 - sqrt(7)I/2, -1/2 + sqrt(7)*I/2] and calculate limits, derivatives and integrals
from sympy import limit, sin, diff
limit(1 / x, x, 0) oo limit(sin(x) / x, x, 0) 1 diff(sin(x), x) cos(x) The beauty of importing this functionality into Python is that we are working within a fully fledged programming language
Can easily create tables of derivatives, generate LaTeX output, add it to figures, etc., etc.
Statistics Python’s data manipulation and statistics libraries have improved rapidly over the last few years
Pandas One of the most popular libraries for working with data is pandas
Pandas is fast, efficient, flexible and well designed
Here’s a simple example, using some fake data
import pandas as pd np.random.seed(1234)
data = np.random.randn(5, 2) # 5x2 matrix of N(0, 1) random draws dates = pd.date_range(‘28/12/2010’, periods=5)
df = pd.DataFrame(data, columns=(‘price’, ‘weight’), index=dates) print(df) price weight 2010-12-28 0.471435 -1.190976 2010-12-29 1.432707 -0.312652 2010-12-30 -0.720589 0.887163 2010-12-31 0.859588 -0.636524 2011-01-01 0.015696 -2.242685 df.mean() price 0.411768 weight -0.699135 dtype: float64 Other Useful Statistics Libraries
statsmodels — various statistical routines
scikit-learn — machine learning in Python (sponsored by Google, among others)
pyMC — for Bayesian data analysis
pystan Bayesian analysis based on stan Networks and Graphs Python has many libraries for studying graphs
One well-known example is NetworkX
Standard graph algorithms for analyzing network structure, etc. Plotting routines etc., etc. Here’s some example code that generates and plots a random graph, with node color determined by shortest path length from a central node
import networkx as nx import matplotlib.pyplot as plt %matplotlib inline np.random.seed(1234)
Generate random graph
p = dict((i,(np.random.uniform(0, 1),np.random.uniform(0, 1))) for i in range(200)) G = nx.random_geometric_graph(200, 0.12, pos=p) pos = nx.get_node_attributes(G, ‘pos’)
find node nearest the center point (0.5, 0.5)
dists = [(x - 0.5)2 + (y - 0.5)2 for x, y in list(pos.values())] ncenter = np.argmin(dists)
Plot graph, coloring by path length from central node
p = nx.single_source_shortest_path_length(G, ncenter) plt.figure() nx.draw_networkx_edges(G, pos, alpha=0.4) nx.draw_networkx_nodes(G, pos, nodelist=list(p.keys()), node_size=120, alpha=0.5, node_color=list(p.values()), cmap=plt.cm.jet_r) plt.show()
Cloud Computing Running your Python code on massive servers in the cloud is becoming easier and easier
A nice example is Anaconda Enterprise
See also
Amazon Elastic Compute Cloud
The Google App Engine (Python, Java, PHP or Go)
Pythonanywhere
Sagemath Cloud Parallel Processing Apart from the cloud computing options listed above, you might like to consider
Parallel computing through IPython clusters
The Starcluster interface to Amazon’s EC2
GPU programming through PyCuda, PyOpenCL, Theano or similar
Other Developments
There are many other interesting developments with scientific programming in Python
Some representative examples include
Jupyter — Python in your browser with code cells, embedded images, etc.
Numba — Make Python run at the same speed as native machine code!
Blaze — a generalization of NumPy
PyTables — manage large data sets
CVXPY — convex optimization in Python Learn More Browse some Python projects on GitHub Have a look at some of the Jupyter notebooks people have shared on various scientific topics
Visit the Python Package Index View some of the question people are asking about Python on Stackoverflow Keep up to date on what’s happening in the Python community with the Python subreddit