So you want to learn Python basics

What you should be able to do after this module

1. Create variables and store strings, numbers, and boolean values.
2. Use lists and dictionaries to organise simple experimental data.
3. Write if statements to make small decisions in code.
4. Loop over values and collect results.
5. Define a function so repeated analysis steps do not need to be rewritten.

How to use this module

This lesson works best if you type the examples yourself. Reading the code helps, but typing and running it is where the ideas start to stick.

It is normal if the early sections feel slow. That is not wasted time. It is where the later notebook and package modules become easier.

Variables and basic data types

A variable is a name that points to a value. In a research setting, that value might be a filename, a threshold, a list of measurements, or a label for an experimental condition.

sample_name = "control_01"
cell_count = 42
mean_signal = 137.5
is_treated = False

Here you have a string, an integer, a floating-point number, and a boolean.

Why these types matter

• Strings are useful for filenames, labels, and conditions.
• Integers are useful for counts like cells or time points.
• Floats are common for intensities, areas, and averages.
• Booleans are useful for true/false decisions such as treated or not treated.

Try printing them

print(sample_name)
print(cell_count)
print(mean_signal)
print(is_treated)

Lists and dictionaries

Lists are useful when order matters. Dictionaries are useful when values have labels.

areas = [3.2, 3.5, 4.1, 4.4]

cell_summary = {
    "cell_id": "c1",
    "condition": "treated",
    "area_um2": 4.1,
    "mean_intensity": 151,
}

A list is a good fit when you mainly care about sequence. A dictionary is a good fit when you want labelled values.

Accessing values

print(areas[0])
print(areas[2])

print(cell_summary["cell_id"])
print(cell_summary["mean_intensity"])

Conditionals

Conditionals help your code respond to simple questions, such as whether a value is above a threshold.

mean_intensity = 151

if mean_intensity > 140:
    print("bright cell")
else:
    print("dim cell")

The same pattern later appears in quality-control checks, thresholding rules, and simple analysis decisions.

Another example

area_um2 = 4.3

if area_um2 > 4.0:
    print("large cell")
else:
    print("small cell")

Loops

Loops let you repeat the same action for each measurement or file.

areas = [3.2, 3.5, 4.1, 4.4]

for area in areas:
    print(area)

This pattern becomes useful once you start working through many cells, image files, or conditions.

A simple mental model is: “for each value in this collection, do the following.” That sentence is often enough to decode a loop.

Loop with a running calculation

areas = [3.2, 3.5, 4.1, 4.4]
total_area = 0

for area in areas:
    total_area = total_area + area

print(total_area)

Defining functions

A function packages a repeatable step behind a name. That makes code easier to read and reuse.

def classify_signal(value, threshold=140):
    if value > threshold:
        return "bright"
    return "dim"


classify_signal(151)

Function anatomy

• def begins the function definition.
• value is the input argument.
• threshold=140 is a default value.
• return sends the result back.

A second small function

def mean_of_two_values(a, b):
    return (a + b) / 2

print(mean_of_two_values(10, 14))

Worked example: combine a function and a loop

intensities = [120, 151, 118, 164]

def classify_signal(value, threshold=140):
    if value > threshold:
        return "bright"
    return "dim"

for value in intensities:
    print(value, classify_signal(value))

This is an important milestone because many beginner analysis tasks are built from exactly these ingredients.

Worked example: build a small result list

intensities = [120, 151, 118, 164]
labels = []

def classify_signal(value, threshold=140):
    if value > threshold:
        return "bright"
    return "dim"

for value in intensities:
    labels.append(classify_signal(value))

print(labels)

This is a useful stepping stone toward later notebook work, where you often loop through measurements and collect outputs.

Mini exercise set

1. Create a variable called experiment_day and assign it a short text value.
2. Create a list of four intensity measurements and print the second value.
3. Write an if statement that prints "large cell" when an area is above 4.0.
4. Write a loop that prints each item in a list of sample names.
5. Define a function called double_value that returns twice its input.

Challenge exercise

Put several ideas together by writing a function that labels a list of intensities as either "bright" or "dim".

intensities = [120, 151, 118, 164]

# Write a function here and use a loop to classify each value.

If this feels hard, solve it in two pieces: first write the function for one value, then add the loop afterwards.

Stop here for today if needed

A strong stopping point is when you can read a short piece of Python and explain what each line is doing, even if you still need help writing code from scratch.

Ready for notebooks

If these ideas feel mostly comfortable, you are in a good position to open a Jupyter notebook and understand what each code cell is doing.