Key Points

Python Basics Recap

Assign values to variables using =
Generate lists using square brackets []
Use indexes inside [], starting at 0, to select characters from strings and items from lists
Use for to loop through items in iterable objects
Make conditional expressions using ==, !=, >, <, >= and <=
Use f'{}' to embed formatted variables inside strings
Avoid use of generative AI for this course

NumPy arrays are not matrix objects
Array masks can be created using conditional statements
NumPy arrays can be masked to hide data you don’t want to include in an analysis
NumPy libraries are available for reading a lot of different file formats

astropy.units library provides unit support
Quantity objects are created by multiplying values by the desired units
The .to() function can be used to convert units
The .decompose() function can be used to convert to the base (irreducible) units
Equivalences can be found using the .find_equivalent_units() function
Specific equivalence libraries can be defined using the equivalences= keyword
Import quantity-support from astropy.visualization to integrate units with matplotlib for data plotting
The pint library provides similar unit support, but is better for working with temperature increments

CSV data is loaded using the read_csv() function to create a pandas DataFrame object
The describe() function gives a quick analysis of the DataFrame
loc[<index>,<column>] indexes the DataFrame by the index and column labels
iloc[<index>,<column>] indexes the DataFrame using numerical indices
The data can be sliced by providing index and/or column indices as ranges or lists of values
The built-in plot() function can be used to plot the data using the matplotlib library