Review of Python Courses (Part 24)

In Part 23, I summarized my Datacamp courses 68-70. Today I will continue with the next three.

As a reminder, I introduced you to my recent work learning Python here.

My course #71 was Improving Your Data Visualizations in Python. This course covers:

• Highlighting data
• Comparing groups
• Annotations
• Color in visualizations
• Continuous color palettes
• Categorical palettes
• Point estimate intervals
• Confidence bands
• Beyond 95% (visualizing multiple confidence bands at once)
• Visualizing the bootstrap
• Looking at the farmers market data
• Exploring the patterns
• Making your visualizations efficient
• Tweaking your plots

My course #72 was Command Line Automation in Python. Because I don’t use the shell much, I don’t see a whole lot of application here for me and I’m not sure how much I absorbed. In any case, topics covered in this course include:

• Learn the Python interpreter
• Capture IPython shell output
• Automate with SList
• Execute shell commands in subprocess (import subprocess; import os)
• Capture output of shell commands (from subprocess import Popen, PIPE)
• Sending input to processes
• Passing arguments safely to shell commands
• Dealing with file systems
• Find files matching a pattern (from pathlib import Path; import fnmatch, re)
• High-level file and directory operations (from shutil import copytree, ignore_patterns, rmtree, make_archive)
• Using pathlib (from pathlib import Path)
• Using functions for automation (from functools import wraps)
• Understand script input
• Introduction to click (import click)
• Using click to write command line tools (from click.testing import CliRunner)

My course #73 was Unit Testing for Data Science in Python. This course covers:

• Why unit test?
• Write a simple unit test using pytest
• Understanding test result report
• More benefits and test types
• Mastering assert statements
• Testing for exceptions instead of return values
• The well-tested function
• Test driven development (TDD)
• How to organize a growing set of tests?
• Mastering test execution
• Expected failures and conditional skipping
• Continuous integration and code coverage
• Beyond assertion: setup and teardown
• Mocking (from unittest.mock import call)
• Testing models
• Testing plots

I will review more courses next time.