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Core Data Types in Python

Lesson Objectives

By the end of this lesson, you will be able to:

  • Understand Python’s core data types
  • Distinguish between numeric, textual, and logical data
  • Perform basic type conversions
  • Recognize why data types are critical in statistical and analytical code

This lesson is essential:

most bugs in analytical Python code are type-related.

What Is a Data Type

A data type defines:

  • what kind of value a variable holds
  • what operations are allowed on that value

In Python, every object has a type.

x = 10

Here:

  • 10 is an integer
  • x is bound to an object of type int

Integer (int)

Integers represent whole numbers, positive or negative.

a = 10
b = -3

Key properties:

  • no decimal point
  • arbitrary precision (no overflow in practice)

Integers are widely used for:

  • counts
  • indices
  • categorical encodings

Floating Point Numbers (float)

Floats represent real numbers with decimals.

x = 3.14
y = 2.0

Important detail:

  • floats are approximations, not exact values

Example:

print(0.1 + 0.2)

Output:

0.30000000000000004

This matters a lot in statistics and numerical analysis.

Strings (str)

Strings represent textual data.

name = "Michele"
city = 'Rome'

Strings can contain:

  • letters
  • numbers
  • symbols

They are not numbers, even if they look like numbers.

x = "10"

This is text, not an integer.

Boolean (bool)

Booleans represent logical values:

is_active = True
is_empty = False

Booleans are fundamental for:

  • conditions
  • filtering data
  • decision logic

Internally, booleans behave like integers:

True == 1
False == 0

This has implications later in modeling and data processing.

Inspecting Types

You can always inspect the type of an object:

type(10)
type(3.14)
type("hello")
type(True)

Expected output:

<class 'int'>
<class 'float'>
<class 'str'>
<class 'bool'>

This is a primary debugging tool.

Type Conversion (Casting)

Python allows explicit type conversion.

Common Conversions

int("10")
float("3.14")
str(100)
bool(1)

Dangerous Conversion

int("ten")

This raises an error.

Type conversion is explicit by design — Python avoids silent errors.

Operations and Types

The result of an operation depends on the involved types.

10 + 2      # int
10 + 2.0    # float
"10" + "2"  # string concatenation

Understanding this behavior is essential for numerical correctness.

Why Data Types Matter in Data Analysis

In analytical workflows, data types determine:

  • mathematical correctness
  • memory usage
  • performance
  • model behavior

Example:

  • "5" vs 5 can silently break an analysis
  • floats introduce rounding errors
  • booleans often encode clinical or experimental conditions

Good analysts are obsessive about types.

FAQ — Frequently Asked Questions

Q: Is Python strongly typed?

A: Yes. Every object has a type, even if you don’t declare it.

Q: Why does Python allow True == 1?

A: For historical and logical consistency, but this must be used carefully.

Q: Should I always use floats for numbers?

A: No. Use integers when values are counts or categories.

Q: Can I mix types freely?

A: Python allows it, but analytical code should minimize ambiguity.

Exercises (10)

Exercise 1

Create an integer variable called n_patients.

Exercise 2

Create a float variable called mean_age.

Exercise 3

Create a string variable called study_name.

Exercise 4

Create a boolean variable called is_valid.

Exercise 5

Check the type of all variables above.

Exercise 6

Convert a string "25" to an integer.

Exercise 7

Convert an integer 5 to a string.

Exercise 8

Predict the output:

print(5 + 2.0)

Exercise 9

Predict the output:

print("5" + "2")

Exercise 10

Explain why this is dangerous in data analysis:

value = "10"
result = value + "5"

Solutions

Exercise 1

n_patients = 120

Exercise 2

mean_age = 67.4

Exercise 3

study_name = "CABG_outcomes"

Exercise 4

is_valid = True

Exercise 5

type(n_patients)
type(mean_age)
type(study_name)
type(is_valid)

Exercise 6

int("25")

Exercise 7

str(5)

Exercise 8

# Output: 7.0

Exercise 9

# Output: "52"

Exercise 10

# String concatenation instead of numeric addition

Next Lesson Preview

In Lesson 05, we will introduce:

  • conditional logic (if, else)
  • loops (for, while)
  • control flow as the basis of algorithmic thinking

This is where Python starts to reason, not just store data.