- In this first session, we will introduce Python.
- This course is about programming for data analysis and visualisation in research.
- It's not mainly about Python.
- But we have to use some language.
- Python is quick to program in
- Python is popular in research, and has lots of libraries for science
- Python interfaces well with faster languages
- Python is free, so you'll never have a problem getting hold of it, wherever you go.
- Not just labour saving
- Scripted research can be tested and reproduced
Programs are a rigorous way of describing data analysis for other researchers, as well as for computers.
Computational research suffers from people assuming each other's data manipulation is correct. By sharing codes, which are much more easy for a non-author to understand than spreadsheets, we can avoid the "SIRO" problem. The old saw "Garbage in Garbage out" is not the real problem for science:
- Sensible input
- Reasonable output
The easiest way to get started using Python, and one of the best for research data work, is the Jupyter Notebook.
In the notebook, you can easily mix code with discussion and commentary, and mix code with the results of that code; including graphs and other data visualisations.
### Make plot %matplotlib inline import math import numpy as np import matplotlib.pyplot as plt theta = np.arange(0, 4 * math.pi, 0.1) eight = plt.figure() axes = eight.add_axes([0, 0, 1, 1]) axes.plot(0.5 * np.sin(theta), np.cos(theta / 2))
[<matplotlib.lines.Line2D at 0x7f58f9f60a00>]
These notes are created using Jupyter notebooks and you may want to use it during the course. However, Jupyter notebooks won't be used for most of the activities and exercises done in class. To get hold of a copy of the notebook, follow the setup instructions shown on the course website, or use the installation in Desktop@UCL (available in the teaching cluster rooms or anywhere).
Jupyter notebooks consist of discussion cells, referred to as "markdown cells", and "code cells", which contain Python. This document has been created using Jupyter notebook, and this very cell is a Markdown Cell.
print("This cell is a code cell")
This cell is a code cell
Code cell inputs are numbered, and show the output below.
Markdown cells contain text which uses a simple format to achive pretty layout, for example, to obtain:
**bold**, *italic* * Bullet > Quote
See the Markdown documentation at This Hyperlink
When working with the notebook, you can either be in a cell, typing its contents, or outside cells, moving around the notebook.
- When in a cell, press escape to leave it. When moving around outside cells, press return to enter.
- Outside a cell:
- Use arrow keys to move around.
bto add a new cell below the cursor.
mto turn a cell from code mode to markdown mode.
enterto calculate the code in the block.
hto see a list of useful keys in the notebook.
- Inside a cell:
tabto suggest completions of variables. (Try it!)
Data science experts tend to use a "command line environment" to work. You'll be able to learn this at our "Software Carpentry" workshops, which cover other skills for computationally based research.
%%bash # Above line tells Python to execute this cell as *shell code* # not Python, as if we were in a command line # This is called a 'cell magic' python -c "print(2 * 4)"
Once you get good at programming, you'll want to be able to write your own full programs in Python, which work just like any other program on your computer. Here are some examples:
%%bash echo "print(2 * 4)" > eight.py python eight.py
We can make the script directly executable (on Linux or Mac) by inserting a [hashbang](https://en.wikipedia.org/wiki/Shebang_(Unix%29) and setting the permissions to execute.
%%writefile fourteen.py #! /usr/bin/env python print(2 * 7)
%%bash chmod u+x fourteen.py ./fourteen.py
We can write our own python libraries, called modules which we can import into the notebook and invoke:
%%writefile draw_eight.py # Above line tells the notebook to treat the rest of this # cell as content for a file on disk. import math import numpy as np import matplotlib.pyplot as plt def make_figure(): theta = np.arange(0, 4 * math.pi, 0.1) eight = plt.figure() axes = eight.add_axes([0, 0, 1, 1]) axes.plot(0.5 * np.sin(theta), np.cos(theta / 2)) return eight
import draw_eight # Load the library file we just wrote to disk
image = draw_eight.make_figure()
There is a huge variety of available packages to do pretty much anything. For instance, try
%% at the beginning of a cell is called magics. There's a large list of them available and you can create your own.