Data Types

Overview

Python provides rich built-in types for numbers, text, and logic. Understanding types helps you choose the right structure and avoid subtle bugs when converting or comparing values.

Syntax / Usage

# Numbers
integer = 42
floating = 3.14
complex_num = 2 + 3j

# Strings
message = "Hello"
multiline = """Line one
Line two"""

# Boolean
is_valid = True
is_empty = False

# Type inspection
type(42)           # <class 'int'>
isinstance(3.14, float)  # True

# Conversion
int("10")          # 10
str(42)            # "42"
float("3.5")       # 3.5
bool(0)            # False
bool("text")       # True (non-empty strings are truthy)

Examples

Validate and convert user age from a string:

raw_age = "25"
if raw_age.isdigit():
    age = int(raw_age)
    print(f"You are {age} years old.")
else:
    print("Invalid age.")

Format a price with two decimal places:

price = 19.5
label = f"${price:.2f}"  # "$19.50"

Common Mistakes

• Comparing floats with == instead of using tolerance (math.isclose)
• Concatenating strings and numbers without str() conversion
• Confusing is (identity) with == (equality) for small integers vs objects
• Treating empty collections as False in if without realizing 0 is also falsy

See Also

python-variables python-list-comprehension python-dictionaries