Introducing the Jupyter Notebook

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 0x2b8f972f05f8>]

We’re going to be mainly working in the Jupyter notebook in this course.

Jupyter notebooks consist of discussion cells, referred to as “markdown cells”, and “code cells”, which contain Python. The notebooks that accompany the readings were created using the Jupyter notebook, The main text of the notebook is entered into Markdown Cells.

 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 achieve pretty layout, for example, to obtain:

bold, italic

• Bullet

Quote

We write:

**bold**, *italic*

* Bullet

> Quote

See the Markdown documentation at http://daringfireball.net/projects/markdown/

Typing code in the notebook

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 (aka enter) to enter.
• Outside a cell:
  • Use arrow keys to move around.
  • Press b to add a new cell below the cursor.
  • Press m to turn a cell from code mode to markdown mode.
  • Press shift+enter to execute the code in the block.
  • Press h to see a list of useful keys in the notebook.
• Inside a cell:
  • Press tab to suggest completions of variables. (Try it!)

These operations can also be accessed from the menu.

Supplementary material: Learn more about the notebook in the Reading More about Jupyter Notebooks

Python at the command line

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)"

« 8

Python scripts

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

« 8

We can make the script directly executable (on Linux or Mac) by inserting a hashbang and setting the permissions to execute.

 %%writefile fouteen.py
 #! /usr/bin/env python
 print(2*7)

« Writing fourteen.py

 %%bash
 chmod u+x fourteen.py

 %%bash
 ./fourteen.py

« 14

Python Libraries

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

« Writing draw_eight.py

In a real example, we could edit the file on disk using a program such as Notepad++ for Windows or Atom for Mac.

 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 import antigravity.

The %% at the beginning of a cell is called magics. There’s a large list of them available and you can create your own.

Next: Reading - More About Jupyter Notebooks