Programming with Python: Glossary

variables

lists

indexing

loops

conditionals

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 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

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

try-except structures are useful for catching errors as they occur

assert structures are useful for forcing errors early, to avoid wasted effort

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 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