Python for Physicists

Python programs are plain text files.

Use the Jupyter Notebook for editing and running Python.

Use the keyboard and mouse to select and edit cells.

The Notebook will turn Markdown into pretty-printed documentation.

Use variables to store values.

Use print to display values.

Variables persist between cells.

Variables must be created before they are used.

Variables can be used in calculations.

Python is case-sensitive.

Use meaningful variable names.

Every value has a type.

Use the built-in function type to find the type of a value.

Types control what operations can be done on values.

Strings can be added and multiplied.

You can convert numbers to strings or vice versa when operating on them.

You can mix integers and floats freely in operations.

Variables only change value when something is assigned to them.

A function may take zero or more arguments.

Commonly-used built-in functions include max, min, and round.

Functions may only work for certain (combinations of) arguments.

Functions may have default values for some arguments.

Use the built-in function help to get help for a function.

The Jupyter Notebook has two ways to get help.

Every function returns something.

Python reports a syntax error when it can’t understand the source of a program.

Python reports a runtime error when something goes wrong while a program is executing.

Fix syntax errors by reading the source code, and runtime errors by tracing the program’s execution.

A list stores many values in a single structure.

Use the built-in function len to find the length of a list.

Use an item’s index to fetch it from a list.

Use slicing to fetch multiple items from a list.

Lists’ values can be replaced by assigning to them.

Appending items to a list lengthens it.

Use del to remove items from a list entirely.

The empty list contains no values.

Lists may contain values of different types.

Character strings can be indexed and sliced like lists.

Character strings are immutable.

Indexing beyond the end of the collection is an error.

A for loop executes commands once for each value in a collection.

The first line of the for loop must end with a colon, and the body must be indented.

Indentation is always meaningful in Python.

A for loop is made up of a collection, a loop variable, and a body.

Loop variables can be called anything (but it is strongly advised to have a meaningful name to the looping variable).

The body of a loop can contain many statements.

Use range to iterate over a sequence of numbers.

The Accumulator pattern turns many values into one.

Use if statements to control whether or not a block of code is executed.

Conditionals are often used inside loops.

Use else to execute a block of code when an if condition is not true.

Use elif to specify additional tests.

Conditions are tested once, in order.

Create a table showing variables’ values to trace a program’s execution.

Break programs down into functions to make them easier to understand.

Define a function using def with a name, parameters, and a block of code.

Defining a function does not run it.

Arguments in call are matched to parameters in definition.

Functions may return a result to their caller using return.

The scope of a variable is the part of a program that can ‘see’ that variable.

Most of the power of a programming language is in its libraries.

A program must import a library module in order to use it.

Use help to learn about the contents of a library module.

Import specific items from a library to shorten programs.

Create an alias for a library when importing it to shorten programs.

Use the NumPy library to work with numerical data in Python

The loadtxt function is used to read in .csv data

Built-in Python functions can be used to read file headings

To save the data to memory we can assign it to a variable

An array is a central data structure of the NumPy library

Extra information about an array are stored as attributes

The savetxt function is used to write data to a file

numpy.linspace generates evenly spaced numbers over a given interval

The enumerate function allows us to have an automatic counter within a for loop

All the indexing and slicing that we’ve used on lists and strings also works on arrays.

Use array[x, y] to select a single element from a 2D array

Use data[:,x] to select a column

Use the pyplot library from matplotlib for creating simple visualizations

Basic plots can be generated quickly with matplotlib

To group similar plots we use a figure and subplots

There are many ways to plot and customise plots using matplotlib

There are often multiple ways to approach a programming task

Fit a polynomial function to data using the numpy.polyfit function

Use the scipy.constants module for physical constants

Numpy simplifies and speeds up array operations

There are Numpy functions for statistical analysis

Numpy functions can be applied across an axis

Follow standard Python style in your code.

Use assertions to check for internal errors

Use docstrings to provide online help.

Use __version__ to increase code reproducibility.

Copy/paste or use the %%writefile myfile.py notebook magic to export a single cell

Use jupyter nbconvert --to script my_notebook.ipynb to export a complete notebook

Use python3 script_name.py to run a script from the terminal

Import the sys module and use sys.argv to take a command line argument

Python supports a large community within research.