Lesson Objectives
By the end of this lesson, you will be able to:
- Understand what variables are and why they matter
- Assign and reassign variables correctly
- Follow Python naming conventions
- Read and write clean, predictable Python code
These concepts are non-negotiable for any serious use of Python in statistics, data analysis, or machine learning.
1️⃣ What Is a Variable in Python
A variable is a name bound to a value.
In Python, variables are created when you assign a value to them.
age = 40
This line means:
- create a name:
age - bind it to the value:
40
Python does not require prior type declaration.
2️⃣ Variable Assignment
Basic Assignment
name = "Michele"
height = 1.78
is_doctor = True
Python automatically infers the type.
Reassignment
Variables can be reassigned at any time:
age = 40
age = 41
The previous value is overwritten.
This flexibility is powerful, but requires discipline.
3️⃣ Dynamic Typing (Important Concept)
Python is dynamically typed:
x = 10
x = "ten"
This is allowed.
However, changing meaning mid-code is bad practice in analytical workflows.
Rule of thumb:
A variable name should always represent the same concept.
4️⃣ Naming Rules (Syntax-Level Rules)
Python variable names must:
- start with a letter or
_ - contain only letters, numbers,
_ - be case-sensitive
Valid Names
age
patient_age
_age
age2
Invalid Names
2age
patient-age
patient age
5️⃣ Naming Conventions (Professional Rules)
Python follows snake_case by convention:
patient_age
mean_value
total_score
Avoid:
PatientAge
patientAge
x1
tmp
In data analysis and ML, clear variable names are critical for reproducibility.
6️⃣ Comments in Python
Comments are ignored by Python and used to explain code.
Single-line Comment
# This is a comment
age = 40
Why Comments Matter
- explain why, not what
- document assumptions
- clarify non-obvious logic
Bad comment:
age = 40 # assign age
Good comment:
age = 40 # age at admission
7️⃣ Inspecting Variables
You can print variables:
print(age)
print(name)
You can also inspect their type:
type(age)
Expected output:
<class 'int'>
This becomes important when debugging analytical code.
8️⃣ Common Beginner Mistakes
Using reserved keywords
class = 10
Error:
SyntaxError
Python has reserved keywords (if, for, class, def, etc.).
Ambiguous variable names
x = 120
Later, no one remembers what x means — including you.
9️⃣ How This Applies to Data Analysis
In real-world analytical code, variables often represent:
- patient-level features
- statistical parameters
- model outputs
- intermediate transformations
Clear variable naming directly impacts:
- readability
- correctness
- reproducibility
FAQ — Frequently Asked Questions
Q: Do I need to declare variable types?
A: No. Python infers them automatically.
Q: Can I change a variable’s type?
A: Yes, but you usually shouldn’t in analytical code.
Q: Are variables copied or referenced?
A: Assignment binds names to objects. This matters later with collections.
Q: Why is snake_case important?
A: Consistency improves readability and collaboration.
Exercises (10)
Exercise 1
Create a variable called age and assign it a number.
Exercise 2
Create a variable name with your name.
Exercise 3
Print both variables.
Exercise 4
Reassign age with a new value.
Exercise 5
Create a variable height_m.
Exercise 6
Check the type of height_m.
Exercise 7
Create a boolean variable.
Exercise 8
Write a meaningful comment for a variable.
Exercise 9
Create two variables and swap their values.
Exercise 10
Predict the output before running:
x = 5
x = x + 2
print(x)
Solutions
Exercise 1
age = 40
Exercise 2
name = "Michele"
Exercise 3
print(age)
print(name)
Exercise 4
age = 41
Exercise 5
height_m = 1.78
Exercise 6
type(height_m)
Exercise 7
is_active = True
Exercise 8
patient_id = 1023 # unique identifier
Exercise 9
a = 1
b = 2
a, b = b, a
Exercise 10
# Output: 7
Next Lesson Preview
In Lesson 04, you will learn:
- core Python data types (
int,float,str,bool) - type conversion
- why types matter in numerical computation