seed-python

Seed files for Python code

• Command-line argument parsing
• Command-line file processing
• Data reading, manipulating, graphing with pandas

seed-command-line-arg-parsing.py

A seed for command-line argument parsing in Python.

Covers:

• Parsing arguments and options
• Showing help text
• Validating presence/absence of mandatory parameters
• Validating parameter type (e.g. expect a number)
• Handling optional parameters
• Handling short (-v) and long (--verbose) version of parameters

It uses argparse because it's available with python. If external depedencies are not a problem, consider using click (while clint and docopt are also good options, click makes a compelling case for itself).

Sources for this code and command-line programs in general:

• https://pyvideo.org/europython-2014/writing-awesome-command-line-programs-in-python.html
• https://docs.python.org/3/library/argparse.html
• https://docs.python.org/3/howto/argparse.html

This program takes two arguments and a few options (argparse gives --help|-h for free):

number1 number1 [--operation add|multiply] (default 'add') [--verbose|-v]

The program adds or multiplies the first two numbers, as specified. Verbose does what is expected: print more information.

seed-command-line-file-processing.py

A seed for processing files in Python.

Covers:

• Process more than one file with file globbing
• Working with pipes (redirect stdin and stdout)
• Accepting command line options with file names
• Mutually exclusive command line options with a default option

Does NOT cover:

• Non-text (binary) files

Sources for this code:

• https://docs.python.org/3/library/fileinput.html
• The command line parsing seed program seed-command-line-arg-parsing.py

Examples:

file.txt --tolower
file.txt --toupper
file.txt (defaults to '--tolower')
*.txt --tolower  (file globbing)
cat file.txt | thisscript.py (read from stdin)

seed-data-read-graph.py

A seed for data loading, parsing, graphing in Python, using pandas

Covers:

• Read data from files
• Manipulate the data
• Filter data
• Visualize data in graphs

Sources:

• https://towardsdatascience.com/exploratory-data-analysis-with-pandas-and-jupyter-notebooks-36008090d813
• https://jeffdelaney.me/blog/useful-snippets-in-pandas/
• https://pandas.pydata.org/pandas-docs/stable/tutorials.html
• https://pandas.pydata.org/pandas-docs/stable/cookbook.html

License

This project is licensed under the MIT License - see the LICENSE.md file for details