Variables and Data Types

1. Variables

1.1 Real World Scenario
1. Imagine your room has different containers: 📦 Box labeled Books 👕 Box labeled Clothes 🍎 Basket labeled Fruits Each container stores something and has a name so that you can find it later. Similarly, a Variable is a named container used to store data in a computer's memory for later use. 📦 name ➜ "Rabindra" 📦 age ➜ 25 📦 PI ➜ 3.14159

1.2 Definition
1. A variable is a named storage location used to store data values that can be used or modified later in a program.
1.3 Key Points
1. ✅ Created by assigning a value to a name ✅ Can be reassigned to different values later ✅ Used to store information for future use ✅ Makes programs easier to understand and maintain
1.4 Examples:
1. x = 5
2. name = "Rabindra"
3. PI = 3.14159
4. is_student = True
5. colors = ["red", "green", "blue"]
1.5 Constants
1. Sometimes we have values that should not change throughout a program. These values are called Constants. Examples: PI = 3.14159 MAX_MARKS = 100 📌 Constants are usually written in UPPERCASE letters.

2. Variable Naming Conventions

2.1 Rules
1. ✅ Should have meaningful names ✅ Can contain letters (a-z, A-Z), digits (0-9), and underscores (_) ✅ Must start with a letter or underscore (_) ✅ Python is case-sensitive ✅ snake_case is preferred — this means writing variable names in lowercase with underscores between words, like total_marks ❌ Reserved keywords cannot be used as variable names (for, while, def, etc.)

2.2 Case Sensitivity
1. name = "Ram" NAME = "Hari" Here, name and NAME are treated as two different variables due to case sensitivity.

3. Data Types

3.1 Why Do We Need Data Types?
1. Consider the following examples: print(5 + 3) # ➜ 8 print("5" + "3") # ➜ "53" print("5" + 3) # ➜ Error 🤔 Why are the results different? Because the values have different Data Types.
3.2 Definition
1. A data type specifies the type of value stored in a variable and determines the operations that can be performed on it.
3.3 Python Data Types
1. Python data types are broadly classified into: 🔹 Primitive Data Types — Store a single value (like a number or word) 🔹 Collection Data Types — Store multiple values together (like a list of items)

4. Primitive Data Types

4.1 🔢 Integer (int)
1. Whole numbers without decimal points. Examples: 10, -5, 0Example Variable: age = 25
4.2 🔢 Float (float)
1. Numbers with decimal points. Examples: 3.14, -2.5, 0.1, 3.0Example Variable: temperature = 36.59
4.3 🔤 String (str)
1. Sequence of characters enclosed in quotes. Examples: 'Python', "Hi", """Hello World""", "3.0"Example Variable: name = "Alice"
4.4 ✅ Boolean (bool)
1. Represents truth values. Possible values: True, FalseExample Variable: is_active = True
4.5 Complex (complex)
1. Numbers with real and imaginary parts. Examples: 3 + 4j, -2.5 + 3.14j, 0 + 2jExample Variable: z = 2 + 3j

5. Collection Data Types

5.1 List
1. A collection of items enclosed in square brackets [ ]. Examples: [1, 2, 3], ["apple", "banana", "cherry"], [1, "hello", 3.14]Example Variable: my_list = [True, 4, "pulp"]
5.2 Tuple
1. A collection of items enclosed in parentheses ( ). Examples: (1, 2, 3), ("apple", "banana", "cherry"), (1, "hello", 3.14)Example Variable: my_tuple = (True, 4, "pulp")
5.3 Set
1. A collection of unique items enclosed in curly braces { }. Examples: {1, 2, 3}, {"apple", "banana", "cherry"}, {1, "hello", 3.14}Example Variable: my_set = {True, 4, "pulp"}
5.4 Dictionary
1. A collection of key-value pairs enclosed in curly braces { }. Examples: {"name": "Alice", "age": 25}, {"fruit": "apple", "healthy": True}Example Variable: my_dict = {"city": "Kathmandu", "country": "Nepal"}

6. Data Types Affect Operations

6.1 Addition ➕
1. print(5 + 3) # ➜ 8 (int + int = int) print(3.5 + 2.5) # ➜ 6.0 (float + float = float) print("Hello" + " " + "World") # ➜ "Hello World" (str + str = str) print("5" + "3") # ➜ "53" (str + str = str) print(5 + "3") # ➜ Error (int + str = TypeError)

6.2 Subtraction ➖
1. print(5 - 3) # ➜ 2 (int - int = int) print(3.5 - 2.5) # ➜ 1.0 (float - float = float) print("Hello" - "World") # ➜ Error (str - str = TypeError) print(5 - "3") # ➜ Error (int - str = TypeError)

6.3 Multiplication ✖️
1. print(5 * 3) # ➜ 15 (int * int = int) print(3.5 * 2) # ➜ 7.0 (float * int = float) print("Hello" * 3) # ➜ "HelloHelloHello" (str * int = str) print("Hello" * "World") # ➜ Error (str * str = TypeError)

6.4 Division ➗
1. print(5 / 2) # ➜ 2.5 (int / int = float) print(3.5 / 2) # ➜ 1.75 (float / int = float) print("Hello" / 2) # ➜ Error (str / int = TypeError) print(5 / "2") # ➜ Error (int / str = TypeError) 📌 Same operator can behave differently depending on the data type. 📌 TypeError means Python doesn't allow that operation between these two data types.

7. Checking Data Types

7.1 Why Check Data Types?
1. We often need to know the data type of a variable before performing operations because the result depends on the data type.
7.2 How to Check Data Types?
1. We can check the data type of a variable using the type() function. Syntax: type(variable_name)Example: num = 10 print(type(num)) # ➜ <class 'int'> name = "Alice" print(type(name)) # ➜ <class 'str'> is_active = True print(type(is_active)) # ➜ <class 'bool'> colors = ["red", "green", "blue"] print(type(colors)) # ➜ <class 'list'>

8. Type Conversion

8.1 Why Do We Need Type Conversion?
1. Sometimes we need to convert data from one type to another before performing operations. For example, what if we want to add two numbers, but they are stored as strings? Example: num_1 = "10" num_2 = "5" print(num_1 + num_2) # ➜ "105" (str + str = str) print(int(num_1) + int(num_2)) # ➜ 15 (int + int = int) To convert a value, write the new data type's name followed by the value in brackets, like this: target_datatype(value) Some common conversions are: int() ➜ Converts to integer float() ➜ Converts to float str() ➜ Converts to string bool() ➜ Converts to boolean list() ➜ Converts to list tuple() ➜ Converts to tuple set() ➜ Converts to set dict() ➜ Converts to dictionary

8.2 Example of Type Conversion
1. num_str = "100" print(type(num_str)) num_int = int(num_str) # ➜ 100 print(type(num_int)) # ➜ <class 'int'> num_float = float(num_str) # ➜ 100.0 print(type(num_float)) # ➜ <class 'float'> num_bool = bool(num_str) # ➜ True print(type(num_bool)) # ➜ <class 'bool'> new_num = 10.0 print(type(new_num)) new_num_str = str(new_num) # ➜ "10.0" print(type(new_num_str)) # ➜ <class 'str'> ⚠️ If conversion is not possible due to invalid data, Python raises a ValueError. For example, int("apple") will raise ValueError because "apple" cannot be converted into an integer.

9. Type Conversion Practical Examples

9.1 Example 1
1. var_1 = "5" var_2 = "2.5" print(var_1 + var_2) # ➜ 52.5 var_1_float = float(var_1) var_2_float = float(var_2) print(var_1_float + var_2_float) # ➜ 7.5
9.2 Example 2
1. var_5 = "10" var_6 = 5 print(var_5 * var_6) # ➜ 1010101010 print(int(var_5) * var_6) # ➜ 50