schmidmt / python-zoo-talk

A tutorial of Python's most useful libraries and tools.

The Python Zoo

Table of Contents

0. Prerequisites
1. Virtual Environments
2. Standard Library
3. Plotting
4. Analysis
5. Development Tools

Prerequisites

Please ensure you have Python installed, either via Conda or the python installer.

I'd suggest using one of the following:

• Miniconda - I find the Conda ecosystem to be unnecessarily confusing, but if you already use it, you might want to continue.
• Python - This is the vanilla python. It's my suggested version.

If you use Linux or MacOS, you can install python with the package manager for your system.

Virtual Environments

When Python was created, it was convention to have all modules and libraries installed globally; i.e. every program you used, could only use the same version as every other program. To address this, several different, but similar, solutions have been created. The two most important are Python's venv and Conda's Envs.

Python's venv

This is built into Python's standard library and it's documentation is here.

Creating a new Virtual Environment:

python -3 -m venv <venv>

Activating the environment depends on your shell:

Platform	Shell	Command to activate virtual environment
POSIX	bash/zsh	$ source <venv>/bin/activate
	fish	$ source <venv>/bin/activate.fish
	csh/tcsh	$ source <venv>/bin/activate.csh
	PowerShell Core	$ <venv>/bin/Activate.ps1
Windows	cmd.exe	C:\> <venv>\Scripts\activate.bat
	PowerShell	PS C:\> <venv>\Scripts\Activate.ps1

Any installs from this point are installed in the virtual environment. To store the versions used, run pip3 freeze > requirements.txt.

Deactivation:

deactivate

Conda's Virtual Environment

Create:

conda create --name myenv

Activate:

conda activate myenv

Any Conda installs from this point go into the virtual environment.

Deactivate:

conda deactivate

Standard Library

The standard library for Python contains tools which are general and performant. A full list can be found here. What follows is a sampling I think is a good place to start.

RE

Docs.

The re library contains tools to work with Regular Expressions for pattern matching. It's worth noting, not all regular expression syntax is the same.

Example

import re

expression = re.compile(r"abc")

print(expression.match("abcdef"))
print(expression.match("defghi"))

Itertools

Docs.

Itertools are useful for common enumerations to make iteration more efficient and easy to understand.

Example

import itertools

list(itertools.permutations('ABCD', 2))

Argparse

Docs

Argparse is a tool for parsing command line arguments.

Example

import argparse

parser = argparse.ArgumentParser(description="An example for Argparse")
parser.add_argument("-n", "--n-iterations", type=int, help="number of iterations to perform")
args = parser.parse_args()

print(args)

Unittest

We'll use pytest as our test runner so let's make sure that it's installed:

$ pip3 install pytest
# OR
$ conda install pytest

Unit testing is the practice of testing each "unit" of code. For example

import unittest

def collatz_steps(n: int) -> int:
    steps = 0
    while n > 1:
        if n & 1 == 0:
            n = n // 2
        else:
            n = 3 * n + 1
        steps += 1

    return steps


class CollatzStepsTest(unittest.TestCase):
    def test_base_case(self):
        self.assertEqual(collatz_steps(1), 0)

    def test_larger(self):
        self.assertEqual(collatz_steps(27), 111)

Plotting

Matplotlib

Seaborn

Plotly

Analysis

Numpy

Scipy

Pandas

Polars

Development Tools

Black

Linters

• Pylint
• Flake8

Git