02_data_types.md

Data Types & Dynamic Typing

📄 Related code file: 02_data_types.py

Overview

Every value in Python has a type that determines what you can do with it. Python is dynamically typed, meaning you don't declare types explicitly — Python figures them out automatically, and a variable can even change type during a program's life.

Dynamic Typing

In statically typed languages (like Java or C++), you must declare a variable's type. In Python, you just assign a value:

x = 42           # x is an int
x = "forty-two"  # now x is a str
x = 3.14         # now x is a float
x = True         # now x is a bool

The type belongs to the value, not the variable. Use type() to check:

print(type(42))        # <class 'int'>
print(type("hello"))   # <class 'str'>
print(type(3.14))      # <class 'float'>

The Built-in Data Types

Numbers

integer_num = 100         # int — whole numbers
float_num = 3.14159       # float — decimal numbers
complex_num = 3 + 4j      # complex — real + imaginary

Text (Strings)

text = "Python Programming"
multiline = """This is a
multiline string"""

Boolean

is_python_fun = True
is_difficult = False

Collections

Type	Syntax	Ordered?	Mutable?	Duplicates?
list	[1, 2, 3]	✅	✅	✅
tuple	(1, 2, 3)	✅	❌	✅
dict	{"a": 1}	✅*	✅	Keys unique
set	{1, 2, 3}	❌	✅	❌

my_list = [1, 2, 3, "mixed", True]   # ordered, changeable
my_tuple = (1, 2, 3)                  # ordered, unchangeable
my_dict = {"name": "Alice", "age": 25}  # key-value pairs
my_set = {1, 2, 3, 3}                # unique elements → {1, 2, 3}

*Dictionaries preserve insertion order since Python 3.7.

Type Checking and Conversion

Checking types safely with isinstance()

value = 42
if isinstance(value, int):
    print("It's an integer")

isinstance() is preferred over type(value) == int because it also handles subclasses correctly.

Type conversion (casting)

int("123")      # 123  (str → int)
str(123)        # "123" (int → str)
float("3.14")   # 3.14 (str → float)
list("abc")     # ['a', 'b', 'c']
bool(0)         # False
bool(1)         # True

Common Mistakes

❌ Mixing incompatible types:

"Age: " + 25        # TypeError: can only concatenate str to str
# Fix:
"Age: " + str(25)   # "Age: 25"

❌ Assuming int division gives an int:

10 / 3   # 3.333... (always a float with /)
10 // 3  # 3 (use // for integer division)

❌ Confusing type() comparison with isinstance():

type(True) == int   # False
isinstance(True, int)  # True (bool is a subclass of int!)

Best Practices

✅ Use isinstance() for type checks. ✅ Convert input explicitly — input() always returns a string. ✅ Use tuples for fixed collections, lists for changeable ones. ✅ Use descriptive variable names that hint at the type (names_list, age_count).

Exercises

1. Predict the type: What does type(3 / 1) return? Verify by running it.

2. Convert and calculate: The variable age = "30" is a string. Convert it to an integer and add 5.

3. Build a collection: Create a dictionary representing a book with keys title, author, and year. Print each value with its type.

4. Deduplicate: Given nums = [1, 2, 2, 3, 3, 3], use a set to find the unique values.

💡 Solution hints

# Exercise 2
age = "30"
print(int(age) + 5)   # 35

# Exercise 4
nums = [1, 2, 2, 3, 3, 3]
unique = set(nums)    # {1, 2, 3}

Key Takeaways

• Python is dynamically typed — no need to declare types.
• Core types: int, float, str, bool, list, tuple, dict, set.
• Use type() to inspect and isinstance() to check types.
• Convert between types with int(), str(), float(), list(), etc.

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