General Python Notes and Py4E Ch 1

Chuck Severance, the author of Python for Everybody: Exploring Data Using Python 3 states in his preface:

Few of my students were planning to be professional computer programmers. Instead, they planned to be librarians, managers, lawyers, biologists, economists, etc., who happened to want to skillfully use technology in their chosen field.

As far as I am aware, this also applies to most of my students, which is part of why I like this text.

Further, in Chapter 1, Chuck states:

This book assumes that everyone needs to know how to program, and that once you know how to program you will figure out what you want to do with your newfound skills.

I’m sold! I hope you enjoy this text as much as I do!

Some General Python Notes

Python 2 vs Python 3

Python 2 was released in 2000 and quickly became extremely popular. Python 3 was released in 2008, with a few changes that broke a fair bit of Python 2 code. For example python3 requires parentheses around print statements (generally equivalent to the bash echo command)

  Python 2: print "Hello World!"
  Python 3: print ("Hello World!")

Because of these changes, many people have resisted switching to Python 3. There is still a lot of code that is Python 2 and still people writing in Python 2 (though hopefully not anymore!!).

Some of the new features of Python 3 we so good, Python 2 holdouts even made a “future” module and they do things like:

from __future__ import print_function

But the world moves on and it is a decade later! Python 2 was initially set to “end of life” in 2015, but it wasn’t until January 1, 2020 that support was officially ended and what is supposed to be THE FINAL version of Python 2, 2.7.18, was released April 20, 2020.

But you do need to know that:

• we’ll use Python 3,
• pay attention when Googling for help and
• you may run into Python 2 code, learning to recognize it (print statements are the easiest way) can help you troubleshoot incompatibilities in code you get from other sources.

Python on HiPerGator

On HiPerGator, we install and maintain several version of Python for users. There is a version that comes with the operating system, and that is what is used unless you load a module for the version you want. In general, even if you don’t care too much about the version, load a python module, because these are where most user-requested Python modules are added. As an example:

[magitz@login2 ~]$ which python
/usr/bin/python
[magitz@login2 ~]$ python --version
Python 2.7.5
[magitz@login2 ~]$ module load python
[magitz@login2 ~]$ python --version
Python 3.7.6
[magitz@login2 ~]$

The Shebang line (#!) in scripts

Remember the shebang line for bash scripts?

#!/usr/bin/bash

We need the same thing for Python scripts. Many sources on the internet will tell you to do:

#!/usr/bin/python

But that means the script will always be run with /usr/bin/python even if that isn’t the Python you want or Python isn’t installed there.

Instead, use:

#!/usr/bin/env python

/usr/bin/env is a program to look in the environment path and check for a command, python in this case. This is more portable and allows Python to be installed wherever in the user’s path.

Ways to use Python

Maybe even more so than Bash, there are a number of ways to use Python. With Bash, we started with running commands on the command line on HiPerGator and built up to writing scripts. In addition to an interactive python prompt and scripts, Python adds Jupyter notebooks and some really nice features in VSCode and other development environments. Which of these you choose to use is largely up to you, your preferences, and the task at hand.

Interactive Python Prompt

Python has an interactive command prompt, similar to the Bash command prompt:

[magitz@login3 share]$ module load python3
[magitz@login3 share]$ python3
Python 3.6.5 (default, Apr 10 2018, 00:31:24) 
[GCC 4.8.5 20150623 (Red Hat 4.8.5-16)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>

And you can start working in Python there. The Py4E text does a lot at the command prompt. Personally, I don’t find that terribly useful. Using the Python prompt is quite limiting and generally more frustrating than anything else. I rarely use the python prompt myself.

Python Scripts

As with Bash we can put Python commands in a text file and execute the script:

[magitz@login3 Examples]$ pwd
/ufrc/bsc4452/share/Class_Files/Examples
[magitz@login3 Examples]$ cat HelloWorld.py 
#!/usr/bin/env python3

# This is a simple script to print some text

print ("Hello World!!")

[magitz@login3 Examples]$ module load python
[magitz@login3 Examples]$ python HelloWorld.py 
Hello World!!
[magitz@login3 Examples]$ 

This is what I do most of the time, but requires going back and forth between editing a file and running that file. It does have the advantages of being fairly easy to run on the cluster and requiring little setup.

Editing scripts in VSCode

Similar to Bash scripts, Python scripts are plain text files. Any text editor can be used to write Python scripts. But VSCode (and others) have a number of added benefits that are worth checking out. We’ve already looked at contextual coloring, which VSCode does for many languages, including Bash and Python. This is clearly easier to read and catch errors than plain text.

Good text editors also have code suggestions (some based on machine learning algorithms that can often suggest whole blocks of code for you!) and function information popups that help you write your code. They have linters to check your syntax and flag errors. And they are configurable for how you want to code.

VSCode can also run your code and help you debug by showing you the values of variables as your code executes step by step. You can see the output in the console.

It does take some extra steps (installing the Remote Development extension from Microsoft and connecting to HiPerGator as a remote ssh server), but you can even edit and run code on HiPerGator from your local computer!

This is my main tool for development. Whether I develop locally on my own computer or remotely on HiPerGator, for larger projects, I use scripts in VSCode.

Jupyter Notebooks

Jupyter is web application that creates documents that have live code, visualizations and descriptive text formatted with markdown (used in most of these pages).

Jupyter can be run on HiPerGator by going to jhub.rc.ufl.edu (You do need to be on the UF network)

Ch 1: Why should you learn to write programs?

• p. 3: 1.2 Computer hardware architecture: This is a nice review things we covered in the Computers lecture.

• p. 4: Understanding programming: Like in the Bash section where we talked about top-down design and thinking out describing the steps to going to the grocery store, here the author makes the analogy to telling a story.

  Similarly, where the author talks about needing to learn the vocabulary and grammar of a language, we will find that you already know some of the grammar fom Bash. Programming languages use similar grammars and vocabularies.

• p. 6: 1.5 Conversing with Python: We will be using Python on HiPerGator. To get to the Python command prompt, login, load the python module and type python:

    [magitz@login3 ~]$ module load python
    [magitz@login3 ~]$ python
    Python 3.7.6 | packaged by conda-forge | (default, Mar 23 2020, 23:03:20)
    [GCC 7.3.0] on linux
    Type "help", "copyright", "credits" or "license" for more information.
    >>>
  

• p. 8: 1.6 Terminology: Interpreter and compiler: We briefly talked about compiling applications in the Linux section. This section goes more into interpreted vs compiled programming languages.

• p. 10: 1.7 What is a program: Similar to the Bash scripts we wrote, Python can be written as a script.

• p. 12: 1.10 What could possibly go wrong? A lot! As I’ve said many times, expect errors, expect things to not work. Learning to read and understand Python error messages is a skill! Note that of the three types of errors, Python will only tell you about the Syntax errors. Logic and Semantic errors are harder to find and result in your code not doing what you wanted it to do.

• p. 14: 1.11 Debugging: In addition to the methods here, we will look in class at using the VSCode debugger as one way to debug code as well as writing test cases to see that you get the expected answer.

• p. 16: 1.14 Exercises: I usually do not assign the exercises, but do encourage you to think about and try some of them. They area good way to test yourself and build more fluency with the content of the chapters.