home

day 1: getting started with python

  • overview of day 1
time section concepts outcomes
09-10 1.1.1 anaconda3 understand install location, create and update virtual environments
1.1.2 jupyter download course material, open notebooks, edit in web browser
1.1.3 testing execute the hello world example in jupyter python env
break
10-11 1.2.1 python syntax practice data structures, conditional statements, flow control
1.2.2 functions understand modules
1.2.3 files, directories navigate the file system, read, write data files
break
11-12 1.3.1 tabular data understand how to read, write, and process survey data
1.3.2 subset, substitute and reshape
1.3.3 summarize aggregate, combine, join operations
12-13 lunch anaconda, continued try to resolve anaconda, psychopy installation problems
  • mindset: excel on steroids

section 1.1.1: anaconda

  • check anaconda3 installation (backup with google colab)
  • anaconda console, conda update, create venv, install scipy
  • python interpreter prompt, spyder ide, script editor
# update anaconda, spyder
conda update anaconda
conda install spyder=5.0.5
# update packages in env
conda update --all
# list envs
conda env list
# start jupyter notebook
jupyter notebook

section 1.1.2: jupyter

  • download course github repository
  • launch jupyter notebook, connect anaconda python kernel

section 1.1.3: testing

  • test interactive python commands in jupyter notebooks
  • navigate file system, working dir
In [2]:
# comment: test anaconda3 and jupyter notebooks
print("hello world")

hello world

Interpreter

Python is an interpreted language* which can be used in two ways:

  • "Interactive" Mode: It functions like an "advanced calculator", executing one command at a time:
user:host:~$ python
Python 3.5.1 (default, Oct 23 2015, 18:05:06)
[GCC 4.8.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> 2 + 2
4
>>> print("Hello World")
Hello World
  • "Scripting" Mode: Executing a series of "commands" saved in text file, usually with a .py extension after the name of your file:
user:host:~$ python my_script.py
Hello World

Using interactive Python in Jupyter-style notebooks

A convenient and powerful way to use interactive-mode Python is via a Jupyter Notebook, or similar browser-based interface.

This particularly lends itself to data analysis since the notebook records a history of commands and shows output and graphs immediately in the browser.

There are several ways you can run a Jupyter(-style) notebook - locally installed on your computer or hosted as a service on the web. Today we will use a Jupyter notebook service provided by Google: https://colab.research.google.com (Colaboratory).

Jupyter-style notebooks: a quick tour

Go to https://colab.research.google.com and login with your Google account.

Select NEW NOTEBOOK → NEW PYTHON 3 NOTEBOOK - a new notebook will be created.

Type some Python code in the top cell, eg:

print("Hello Jupyter !")

Shift-Enter to run the contents of the cell

You can add new cells.

Insert → Insert Code Cell

NOTE: When the text on the left hand of the cell is: In [*] (with an asterisk rather than a number), the cell is still running. It's usually best to wait until one cell has finished running before running the next.

Let's begin writing some code in our notebook.

In [ ]: