Python Basics Recap

  • variables
  • lists
  • indexing
  • loops
  • conditionals

Dictionaries

  • JSON is simple
  • Dictionaries are defined using key:value pairs
  • Dictionaries can be nested, and mixed with lists
  • Web API’s can be accessed using the requests library

Numpy and Matplotlib Essential

  • 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

Software Package Management

  • conda virtual environments are useful for installing programs with differing requirements
  • conda config --add channels <channel> adds a new channel to your list of sources
  • conda search <package> will find all available versions of a package in your list of sources
  • conda create -n <env> <package(s)> can be used to create a virtual environment from a list of packages
  • conda install -n <env> <pacakge(s)> installs packages in a pre-existing environment
  • conda activate <env> activates the named environment, giving access to the software installed there
  • conda deactivate deactivates the current environment
  • conda env export --from-history > <file.yml> creates a portable record of your current environment
  • conda env create --file <file.yml> <env> creates a new environment from an environment file

Defensive Programming

  • try-except structures are useful for catching errors as they occur
  • assert structures are useful for forcing errors early, to avoid wasted effort

Units and Quantities

  • 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

Pandas Essential

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