Python Data Types

Fundamental Data Types

Python provides several built-in data types grouped into categories:

- **Numeric**: `int`, `float`, `complex`

- **Sequence**: `str`, `list`, `tuple`, `range`

- **Mapping**: `dict`

- **Set**: `set`, `frozenset`

- **Boolean**: `bool`

- **Binary**: `bytes`, `bytearray`, `memoryview`

ℹ️ Note: Use the built-in `type()` function to check the data type of any variable.

Numeric Types

⚠️ Warning: Floating-point numbers are imprecise for exact arithmetic. Use the `decimal` or `fractions` module for precise calculations.

Example

# Integers (arbitrary precision)
x = 42
y = -9876543210

# Floating-point numbers
pi = 3.14159
sci = 6.022e23  # Scientific notation

# Complex numbers
z = 3 + 5j
print(z.real, z.imag)  # 3.0 5.0

Output

3.0 5.0

Sequence Types Comparison

Type	Mutable?	Syntax	Typical Use Case
String	Immutable	`"text"`	Store and manipulate text
List	Mutable	`[1, 2, 3]`	Ordered collection of items
Tuple	Immutable	`(1, 2, 3)`	Fixed collection of items
Range	Immutable	`range(5)`	Efficient sequences of integers

Dictionaries & Sets

Example

# Dictionary (key-value pairs)
user = {
    "name": "Alice",
    "age": 30,
    "is_active": True
}

# Set (unique elements, mutable)
primes = {2, 3, 5, 7}
primes.add(11)

# Frozenset (immutable set)
constants = frozenset({3.14, 2.718})

ℹ️ Note: Dictionary keys must be immutable (e.g., strings, numbers, or tuples).

Type Conversion

⚠️ Warning: Invalid conversions (e.g., `int("abc")`) raise `ValueError`.

Example

# Explicit conversion
num_str = "123"
num_int = int(num_str)
num_float = float(num_str)

# Implicit conversion (type coercion)
result = 3 + 5.0  # int + float → float
print(type(result))

Output

<class 'float'>

Special Cases

Some built-in types represent special situations:

Example

# None type
x = None  # Represents 'no value' or 'null'

# Ellipsis object
placeholder = ...  # Commonly used in slicing or as a stub

# Boolean truthiness
print(bool(0))      # False
print(bool("Hi"))   # True
print(bool([]))     # False

Output

False
True
False

Exercises

Create variables for all fundamental types and print their types using `type()`
Convert between `str`, `int`, and `float` safely with error handling
Show how `list` can be modified while `tuple` cannot
Use `None` in a conditional statement to check for missing values

Python Data Types

Fundamental Data Types

Python provides several built-in data types grouped into categories:

- **Numeric**: `int`, `float`, `complex`

- **Sequence**: `str`, `list`, `tuple`, `range`

- **Mapping**: `dict`

- **Set**: `set`, `frozenset`

- **Boolean**: `bool`

- **Binary**: `bytes`, `bytearray`, `memoryview`

ℹ️ Note: Use the built-in `type()` function to check the data type of any variable.

Numeric Types

⚠️ Warning: Floating-point numbers are imprecise for exact arithmetic. Use the `decimal` or `fractions` module for precise calculations.

Example

# Integers (arbitrary precision)
x = 42
y = -9876543210

# Floating-point numbers
pi = 3.14159
sci = 6.022e23  # Scientific notation

# Complex numbers
z = 3 + 5j
print(z.real, z.imag)  # 3.0 5.0

Output

3.0 5.0

Sequence Types Comparison

Type	Mutable?	Syntax	Typical Use Case
String	Immutable	`"text"`	Store and manipulate text
List	Mutable	`[1, 2, 3]`	Ordered collection of items
Tuple	Immutable	`(1, 2, 3)`	Fixed collection of items
Range	Immutable	`range(5)`	Efficient sequences of integers

Dictionaries & Sets

Example

# Dictionary (key-value pairs)
user = {
    "name": "Alice",
    "age": 30,
    "is_active": True
}

# Set (unique elements, mutable)
primes = {2, 3, 5, 7}
primes.add(11)

# Frozenset (immutable set)
constants = frozenset({3.14, 2.718})

ℹ️ Note: Dictionary keys must be immutable (e.g., strings, numbers, or tuples).

Type Conversion

⚠️ Warning: Invalid conversions (e.g., `int("abc")`) raise `ValueError`.

Example

# Explicit conversion
num_str = "123"
num_int = int(num_str)
num_float = float(num_str)

# Implicit conversion (type coercion)
result = 3 + 5.0  # int + float → float
print(type(result))

Output

<class 'float'>

Special Cases

Some built-in types represent special situations:

Example

# None type
x = None  # Represents 'no value' or 'null'

# Ellipsis object
placeholder = ...  # Commonly used in slicing or as a stub

# Boolean truthiness
print(bool(0))      # False
print(bool("Hi"))   # True
print(bool([]))     # False

Output

False
True
False

Exercises

Create variables for all fundamental types and print their types using `type()`
Convert between `str`, `int`, and `float` safely with error handling
Show how `list` can be modified while `tuple` cannot
Use `None` in a conditional statement to check for missing values

Python Fundamentals

Python OOP

Python Quiz

Python Data Types

Fundamental Data Types

Numeric Types

Sequence Types Comparison

Dictionaries & Sets

Type Conversion

Special Cases

Exercises

Python Data Types

Fundamental Data Types

Numeric Types

Sequence Types Comparison

Dictionaries & Sets

Type Conversion

Special Cases

Exercises