Last updated on 2023-05-02 | Edit this page


Running and Quitting

  • Python files have the .py extension.
  • Can be written in a text file or a Jupyter Notebook.
    • Jupyter notebooks have the extension .ipynb
    • Jupyter notebooks can be opened from Anaconda or through the command line by entering $ jupyter notebook
      • Markdown and HTML are allowed in markdown cells for documenting code.

Variables and Assignment

  • Variables are stored using =.
    • Strings are defined in quotations '...'.
    • Integers and floating point numbers are defined without quotations.
  • Variables can contain letters, digits, and underscores _.
    • Cannot start with a digit.
    • Variables that start with underscores should be avoided.
  • Use print(...) to display values as text.
  • Can use indexing on strings.
    • Indexing starts at 0.
    • Position is given in square brackets [position] following the variable name.
    • Take a slice using [start:stop]. This makes a copy of part of the original string.
      • start is the index of the first element.
      • stop is the index of the element after the last desired element.
  • Use len(...) to find the length of a variable or string.

Data Types and Type Conversion

  • Each value has a type. This controls what can be done with it.
    • int represents an integer
    • float represents a floating point number.
    • str represents a string.
  • To determine a variables type, use the built-in function type(...), including the variable name in the parenthesis.
  • Modifying strings:
    • Use + to concatenate strings.
    • Use * to repeat a string.
    • Numbers and strings cannot be added to on another.
      • Convert string to integer: int(...).
      • Convert integer to string: str(...).

Built-in Functions and Help

  • To add a comment, place # before the thing you do not with to be executed.
  • Commonly used built-in functions:
    • min() finds the smallest value.
    • max() finds the largest value.
    • round() rounds off a floating point number.
    • help() displays documentation for the function in the parenthesis.
      • Other ways to get help include holding down shift and pressing tab in Jupyter Notebooks.


  • Importing a library:
    • Use import ... to load a library.
    • Refer to this library by using module_name.thing_name.
      • . indicates ‘part of’.
  • To import a specific item from a library: from ... import ...
  • To import a library using an alias: import ... as ...
  • Importing the math library: import math
    • Example of referring to an item with the module’s name: math.cos(math.pi).
  • Importing the plotting library as an alias: import matplotlib as mpl

Reading Tabular Data into DataFrames

  • Use the pandas library to do statistics on tabular data. Load with import pandas as pd.
    • To read in a csv: pd.read_csv(), including the path name in the parenthesis.
      • To specify a column’s values should be used as row headings: pd.read_csv('path', index_col='column name'), where path and column name should be replaced with the relevant values.
  • To get more information about a DataFrame, use, replacing DataFrame with the variable name of your DataFrame.
  • Use DataFrame.columns to view the column names.
  • Use DataFrame.T to transpose a DataFrame.
  • Use DataFrame.describe to get summary statistics about your data.

Pandas DataFrames

  • Select data using [i,j]
    • To select by entry position: DataFrame.iloc[..., ...]
      • This is inclusive of everything except the final index.
    • To select by entry label: DataFrame.loc[..., ...]
      • Can select multiple rows or columns by listing labels.
      • This is inclusive to both ends.
    • Use : to select all rows or columns.
  • Can also select data based on values using True and False. This is a Boolean mask.
    • mask = subset > 10000
    • We can then use this to select values.
  • To use a select-apply-combine operation we use data.apply(lambda x: x > x.mean()) where mean() can be any operation the user would like to be applied to x.


  • The most widely used plotting library is matplotlib.
    • Usually imported using import matplotlib.pyplot as plt.
    • To plot we use the command plt.plot(time, position).
    • To create a legend use plt.legend(['label1', 'label2'], loc='upper left')
      • Can also define labels within the plot statements by using plt.plot(time, position, label='label'). To make the legend show up, use plt.legend()
    • To label x and y axis plt.xlabel('label') and plt.ylabel('label') are used.
  • Pandas DataFrames can be used to plot by using DataFrame.plot(). Any operations that can be used on a DataFrame can be applied while plotting.
    • To plot a bar plot data.plot(kind='bar')


import matplotlib.puplot as plot
plt.plot(time, position, label='label')
plt.xlabel('x axis label')
plt.ylabel('y axis label')


  • Defined within [...] and separated by ,.
    • An empty list can be created by using [].
  • Can use len(...) to determine how many values are in a list.
  • Can index just as done in previous lessons.
    • Indexing can be used to reassign values list_name[0] = newvalue.
  • To add an item to a list use list_name.append(), with the item to append in the parenthesis.
  • To combine two lists use list_name_1.extend(list_name_2).
  • To remove an item from a list use del list_name[index].

For Loops

  • Start a for loop with for number in [1, 2, 3]:, with the following lines indented.
    • [1, 2, 3] is considered the collection.
    • number is the loop variable.
    • The action following the collection is the body.
  • To iterate over a sequence of numbers use range(start, end)


for number in range(0,5):


  • Defined similarly to a loop, using if variable conditional value:.
    • For example, if variable > 5:.
  • Use elif: for additional tests.
  • Use else: for when if statement is not true.
  • Can combine more than one conditional by using and or or.
  • Often used in combination with for loops.
  • Conditions that can be used:
    • == equal to.
    • >= greater than or equal to.
    • <= less than or equal to.
    • > greater than.
    • < less than.


for m in [3, 6, 7, 2, 8]:
    if m > 5:
        print(m, 'is large')
    elif m == 5:
        print(m, 'is 5')
        print(m, 'is small')

Looping Over Data Sets

  • Use a for loop: for filename in [file1, file2]:
  • To find a set of files using a pattern use glob.glob
    • Must import first using import glob.
    • * indicates “match zero or more characters”
    • ? indicates “match exactly one character”
      • For example: glob.glob(*.txt) will find all files that end with .txt in the current directory.
  • Combine these by writing a loop using: for filename in glob.glob(*.txt):


for filename in glob.glob(*.txt):
  data = pd.read_csv(filename)

Writing Functions

  • Define a function using def function_name(parameters):. Replace parameters with the variables to use when the function is executed.
  • Run by using function_name(parameters).
  • To return a result to the caller use return ... in the function.


def add_numbers(a, b):
    result = a + b
    return result

add_numbers(1, 4)

Variable Scope

  • A local variable is defined in a function and can only be seen and used within that function.
  • A global variable is defined outside of a function and can be seen or used anywhere after definition.

Programming Style

  • Document your code.
  • Use clear and meaningful variable names.
  • Follow the PEP8 style guide when setting up your code.
  • Use assertions to check for internal errors.
  • Use docstrings to provide help.


{:auto_ids} Arguments : Values passed to functions.

A container holding elements of the same type.
An object composed of True and False.
The way Pandas represents a table; a collection of series.
An item in a list or an array. For a string, these are the individual characters.
A block of code that can be called and re-used elsewhere.
Global variable
A variable defined outside of a function that can be used anywhere.
The position of a given element.
Jupyter Notebook
Interactive coding environment allowing a combination of code and markdown.
A collection of files containing functions used by other programs.
Local Variable
A variable defined inside of a function that can only be used inside of that function.
A boolean object used for selecting data from another object.
An action tied to a particular object. Called by using object.method.
The files within a library containing functions used by other programs.
Variables used when executing a function.
A Pandas data structure to represent a column.
A part of a string.
Names for values.