--- title: Variables, Naming Rules, and Basic Syntax date: 2026-01-01T12:12:13Z modified: 2026-01-03T16:01:40Z permalink: "https://www.micheledpierri.com/python/variables/" type: page status: publish excerpt: "" wpid: 2462 featured_image: "https://www.micheledpierri.com/wp-content/uploads/2026/01/Python_03.webp" featured_image_alt: Children dressed in lab coats are studying a python timestamp: 2026-01-03T16:01:40Z tags: [] --- ## 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 ``` 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 ``` 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 ``` 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" ``` 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 ``` age patient_age _age age2 ``` ### Invalid Names 2age patient-age patient age ``` 2age patient-age patient age ``` --- ## 5️⃣ Naming Conventions (Professional Rules) Python follows **snake\_case** by convention: patient_age mean_value total_score ``` patient_age mean_value total_score ``` Avoid: PatientAge patientAge x1 tmp ``` 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 ``` # 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 ``` age = 40 # assign age ``` Good comment: age = 40 # age at admission ``` age = 40 # age at admission ``` --- ## 7️⃣ Inspecting Variables You can print variables: print(age) print(name) ``` print(age) print(name) ``` You can also inspect their type: type(age) ``` type(age) ``` Expected output: ``` 'int'> ``` This becomes important when debugging analytical code. --- ## 8️⃣ Common Beginner Mistakes ### Using reserved keywords class = 10 ``` class = 10 ``` Error: SyntaxError ``` SyntaxError ``` Python has reserved keywords (`if`, `for`, `class`, `def`, etc.). ### Ambiguous variable names x = 120 ``` 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 ``` age = 40 ``` ### Exercise 2 name = "Michele" ``` name = "Michele" ``` ### Exercise 3 print(age) print(name) ``` print(age) print(name) ``` ### Exercise 4 age = 41 ``` age = 41 ``` ### Exercise 5 height_m = 1.78 ``` height_m = 1.78 ``` ### Exercise 6 type(height_m) ``` type(height_m) ``` ### Exercise 7 is_active = True ``` is_active = True ``` ### Exercise 8 patient_id = 1023 # unique identifier ``` patient_id = 1023 # unique identifier ``` ### Exercise 9 a = 1 b = 2 a, b = b, a ``` a = 1 b = 2 a, b = b, a ``` ### Exercise 10 # Output: 7 ``` # 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 ## This is where **bugs begin — and how to prevent them**.