Python Fundamentals

Overview

• Teaching: 15
• Exercises: 10
• Questions:
  • What basic data types can I work with in Python?
  • How can I create a new variable in Python?
  • How do I use a function?
  • Can I change the value associated with a variable after I create it?
  • How can I get help while learning to program?
• Objectives:
  • Recognize common data types (integers, floats, strings).
  • Assign and update variables to store values.
  • Display values and add simple comments.
  • Call built‑in functions and check a value’s type.
  • Use help and read error messages to troubleshoot.

Variables

Any Python interpreter can be used as a calculator:

3 + 5 * 4

23

This is great but not very interesting. To do anything useful with data, we need to assign its value to a variable. In Python, we can assign a value to a variable, using the equals sign =. For example, we can track the weight of a patient who weighs 60 kilograms by assigning the value 60 to a variable weight_kg:

weight_kg = 60

From now on, whenever we use weight_kg, Python will substitute the value we assigned to it. In layperson’s terms, a variable is a name for a value.

In Python, variable names:

• can include letters, digits, and underscores
• cannot start with a digit
• are case sensitive.

This means that, for example:

• weight0 is a valid variable name, whereas 0weight is not
• weight and Weight are different variables

Types of Data

Python knows various types of data. Three common ones are:

• integer numbers
• floating point numbers, and
• strings.

In the example above, variable weight_kg has an integer value of 60. If we want to more precisely track the weight of our patient, we can use a floating point value by executing:

weight_kg = 60.3

To create a string, we add single or double quotes around some text. To identify and track a patient throughout our study, we can assign each person a unique identifier by storing it in a string:

patient_id = '0001'

Using Variables in Python

Once we have data stored with variable names, we can make use of it in calculations. We may want to store our patient’s weight in pounds as well as kilograms:

weight_lb = 2.2 * weight_kg

We might decide to add a prefix to our patient identifier:

patient_id = 'MB-' + patient_id

Built-in Python Functions

To carry out common tasks with data and variables in Python, the language provides us with several built-in functions. To display information to the screen, we use the print function:

print(weight_lb)

132.66

print(patient_id)

MB-0001

When we want to make use of a function, referred to as calling the function, we follow its name by parentheses. The parentheses are important: if you leave them off, the function doesn’t actually run! Sometimes you will include values or variables inside the parentheses for the function to use. In the case of print, we use the parentheses to tell the function what value we want to display. We will learn more about how functions work and how to create our own in later episodes.

We can display multiple things at once using only one print call:

print(patient_id, 'weight in kilograms:', weight_kg)

MB-0001 weight in kilograms: 60.3

We can also call a function inside of another function call. For example, Python has a built-in function called type that tells you a value’s data type:

print(type(60.3))

<class 'float'>

print(type(patient_id))

<class 'str'>

Moreover, we can do arithmetic with variables right inside the print function:

print('weight in pounds:', 2.2 * weight_kg)

weight in pounds: 132.66

The above command, however, did not change the value of weight_kg:

print(weight_kg)

60.3

To change the value of the weight_kg variable, we have to assign weight_kg a new value using the equals = sign:

weight_kg = 65.0
print('weight in kilograms is now:', weight_kg)

weight in kilograms is now: 65.0

Getting Help

Use the built-in function help to get help for a function. Every built-in function has extensive documentation that can also be found online.

help(print)

Help on built-in function print in module builtins:

print(...)
    print(value, ..., sep=' ', end='\n', file=sys.stdout, flush=False)
    
    Prints the values to a stream, or to sys.stdout by default.
    Optional keyword arguments:
    file:  a file-like object (stream); defaults to the current sys.stdout.
    sep:   string inserted between values, default a space.
    end:   string appended after the last value, default a newline.
    flush: whether to forcibly flush the stream.

This help message (the function’s “docstring”) includes a usage statement, a list of parameters accepted by the function, and their default values if they have them.

It is normal to encounter error messages while programming, whether you are learning for the first time or have been programming for many years. Let’s explore how people use them to get more help when they are stuck with their Python code.

• Search the internet: paste the last line of your error message or the word “python” and a short description of what you want to do into your favourite search engine and you will usually find several examples where other people have encountered the same problem and came looking for help.
  • StackOverflow can be particularly helpful for this: answers to questions are presented as a ranked thread ordered according to how useful other users found them to be.
  • Take care: copying and pasting code written by somebody else is risky unless you understand exactly what it is doing!
• Ask somebody “in the real world”. If you have a colleague or friend with more expertise in Python than you have, show them the problem you are having and ask them for help.

Generative AI

It is increasingly common for people to use generative AI chatbots such as ChatGPT to get help while coding. You will probably receive some useful guidance by presenting your error message to the chatbot and asking it what went wrong. However, the way this help is provided by the chatbot is different. Answers on StackOverflow have (probably) been given by a human as a direct response to the question asked. But generative AI chatbots, which are based on an advanced statistical model, respond by generating the most likely sequence of text that would follow the prompt they are given.

While responses from generative AI tools can often be helpful, they are not always reliable. These tools sometimes generate plausible but incorrect or misleading information, so (just as with an answer found on the internet) it is essential to verify their accuracy. You need the knowledge and skills to be able to understand these responses, to judge whether or not they are accurate, and to fix any errors in the code it offers you.

In addition to asking for help, programmers can use generative AI tools to generate code from scratch; extend, improve and reorganise existing code; translate code between programming languages; figure out what terms to use in a search of the internet; and more. However, there are drawbacks that you should be aware of.

The models used by these tools have been “trained” on very large volumes of data, much of it taken from the internet, and the responses they produce reflect that training data, and may recapitulate its inaccuracies or biases. The environmental costs (energy and water use) of LLMs are a lot higher than other technologies, both during development (known as training) and when an individual user uses one (also called inference). For more information see the AI Environmental Impact Primer developed by researchers at HuggingFace, an AI hosting platform. Concerns also exist about the way the data for this training was obtained, with questions raised about whether the people developing the LLMs had permission to use it. Other ethical concerns have also been raised, such as reports that workers were exploited during the training process.

We recommend that you avoid getting help from generative AI during the workshop for several reasons:

1. For most problems you will encounter at this stage, help and answers can be found among the first results returned by searching the internet.
2. The foundational knowledge and skills you will learn in this lesson by writing and fixing your own programs are essential to be able to evaluate the correctness and safety of any code you receive from online help or a generative AI chatbot. If you choose to use these tools in the future, the expertise you gain from learning and practising these fundamentals on your own will help you use them more effectively.
3. As you start out with programming, the mistakes you make will be the kinds that have also been made – and overcome! – by everybody else who learned to program before you. Since these mistakes and the questions you are likely to have at this stage are common, they are also better represented than other, more specialised problems and tasks in the data that was used to train generative AI tools. This means that a generative AI chatbot is more likely to produce accurate responses to questions that novices ask, which could give you a false impression of how reliable they will be when you are ready to do things that are more advanced.

Variables as Sticky Notes

A variable in Python is analogous to a sticky note with a name written on it: assigning a value to a variable is like putting that sticky note on a particular value.

Using this analogy, we can investigate how assigning a value to one variable does not change values of other, seemingly related, variables. For example, let’s store the subject’s weight in pounds in its own variable:

# There are 2.2 pounds per kilogram
weight_lb = 2.2 * weight_kg
print('weight in kilograms:', weight_kg, 'and in pounds:', weight_lb)

weight in kilograms: 65.0 and in pounds: 143.0

Everything in a line of code following the ‘#’ symbol is a comment that is ignored by Python. Comments allow programmers to leave explanatory notes for other programmers or their future selves.

Similar to above, the expression 2.2 * weight_kg is evaluated to 143.0, and then this value is assigned to the variable weight_lb (i.e. the sticky note weight_lb is placed on 143.0). At this point, each variable is “stuck” to completely distinct and unrelated values.

Let’s now change weight_kg:

weight_kg = 100.0
print('weight in kilograms is now:', weight_kg, 'and weight in pounds is still:', weight_lb)

weight in kilograms is now: 100.0 and weight in pounds is still: 143.0

weight in kilograms is now: 100.0 and weight in pounds is still: 143.0

Since weight_lb doesn’t “remember” where its value comes from, it is not updated when we change weight_kg.

Challenge: Check Your Understanding

What values do the variables mass and age have after each of the following statements? Test your answer by executing the lines.

mass = 47.5
age = 122
mass = mass * 2.0
age = age - 20

Solution

mass = 47.5
age = 122
mass = mass * 2.0
age = age - 20
print(mass)

95.0

print(age)

102

Challenge: Sorting Out References

Python allows you to assign multiple values to multiple variables in one line by separating the variables and values with commas. What does the following program print out?

first, second = 'Grace', 'Hopper'
third, fourth = second, first
print(third, fourth)

Solution

Hopper Grace

Challenge: Seeing Data Types

What are the data types of the following variables?

planet = 'Earth'
apples = 5
distance = 10.5

Solution

planet = 'Earth'
apples = 5
distance = 10.5

print(type(planet))

<class 'str'>

print(type(apples))

<class 'int'>

print(type(distance))

<class 'float'>

Key Points

• Basic data types in Python include integers, strings, and floating-point numbers.
• Use variable = value to assign a value to a variable in order to record it in memory.
• Variables are created on demand whenever a value is assigned to them.
• Use print(something) to display the value of something.
• Use # some kind of explanation to add comments to programs.
• Built-in functions are always available to use.
• Use help(thing) to view help for something.
• Error messages provide information about what has gone wrong with your program and where.

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