**Python Fundamentals: Variables, Data Types, and Operators
This lesson introduces the foundational elements of Python programming: variables, data types, and operators. You'll learn how to store and manipulate data, which are essential skills for any data scientist. By the end, you'll be able to write basic Python code that performs simple calculations and data handling tasks.
Learning Objectives
- Define and use variables to store data in Python.
- Identify and differentiate between common Python data types (integers, floats, strings, booleans).
- Utilize arithmetic, comparison, and logical operators in Python expressions.
- Understand the concept of type conversion and how to perform it.
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Lesson Content
Variables: Your Data Containers
Variables are named storage locations in your computer's memory that hold data. Think of them as labeled boxes where you can put different things (data). In Python, you don't need to declare the variable's type explicitly; Python figures it out based on the value you assign to it.
Example:
age = 30 # An integer
name = "Alice" # A string
pi = 3.14159 # A float
print(age)
print(name)
print(pi)
Here, age, name, and pi are variables. The values assigned to them (30, "Alice", and 3.14159) are the data the variables store.
Data Types: The Kind of Data
Python supports several built-in data types. Understanding these types is crucial because they determine what operations you can perform on your data.
- Integers (int): Whole numbers (e.g., 10, -5, 0)
- Floats (float): Numbers with decimal points (e.g., 3.14, -2.5)
- Strings (str): Sequences of characters (e.g., "hello", "Python") - enclosed in single or double quotes.
- Booleans (bool): Represent truth values:
TrueorFalse.
Example:
num_students = 25 # int
price = 19.99 # float
course_name = "Data Science Basics" # str
is_active = True # bool
print(type(num_students)) # Output: <class 'int'>
print(type(price)) # Output: <class 'float'>
print(type(course_name)) # Output: <class 'str'>
print(type(is_active)) # Output: <class 'bool'>
Operators: Working with Data
Operators are special symbols that perform operations on values. Python has several types of operators:
- Arithmetic Operators:
+(addition),-(subtraction),*(multiplication),/(division),**(exponentiation),%(modulo - remainder). - Comparison Operators:
==(equal to),!=(not equal to),>(greater than),<(less than),>=(greater than or equal to),<=(less than or equal to). - Logical Operators:
and(both are true),or(at least one is true),not(negates a boolean value).
Example:
a = 10
b = 3
print(a + b) # Output: 13
print(a / b) # Output: 3.3333333333333335
print(a % b) # Output: 1
print(a > b and b < 5) # Output: True
print(not(a < b)) # Output: True
Type Conversion: Changing Data Types
Sometimes, you need to change a variable's data type. Python allows you to convert between types using built-in functions:
int(): Converts to an integer (if possible).float(): Converts to a float.str(): Converts to a string.bool(): Converts to a boolean (e.g., non-zero numbers become True, 0 becomes False).
Example:
number_string = "10" # String
number_int = int(number_string) # Convert to integer
print(type(number_int)) # Output: <class 'int'>
print(number_int + 5) # Output: 15
float_number = 3.14
int_number = int(float_number)
print(int_number) #Output: 3
Deep Dive
Explore advanced insights, examples, and bonus exercises to deepen understanding.
Day 2: Python Fundamentals - Expanding Your Toolkit
Welcome back! Yesterday, we laid the groundwork for Python programming by exploring variables, data types, and operators. Today, we'll delve deeper, providing more insights, practical examples, and exciting challenges to solidify your understanding and prepare you for more complex data science tasks. Let's make you a Python power user!
Deep Dive: Data Types and Their Implications
While we've covered the basics, let's explore the nuances of data types. Understanding the *internal representation* of these types is crucial for optimizing your code and avoiding unexpected behavior.
-
Integers (
int): Python integers have arbitrary precision, meaning they can be as large or small as your system's memory allows. This is different from some languages that have fixed-size integers. This flexibility makes Python excellent for handling large datasets. -
Floats (
float): Floating-point numbers are stored using the IEEE 754 standard, which has limitations on precision. Be mindful of this when comparing floats for equality (use a tolerance check instead). For example,0.1 + 0.2 != 0.3due to the way floats are represented. -
Strings (
str): Strings are immutable sequences of Unicode characters. This immutability means you can't directly change a string; instead, you create a new one. This is a core concept that affects string manipulation techniques. -
Booleans (
bool): Represent truth values (TrueorFalse). Under the hood,Trueis typically represented as 1 andFalseas 0, but this shouldn't be relied upon.
The way data is *interpreted* by Python is dictated by its data type. Understanding the internal representation of these types directly impacts how your programs perform.
Bonus Exercises
Practice makes perfect! Try these exercises to hone your skills:
- Exercise 1: Time Conversion. Write a Python program that takes an input of hours and converts it to seconds. Handle potential user input errors (e.g., negative hours). Print a helpful message if the input is invalid.
-
Exercise 2: Comparison Operators. Create a program that asks the user for two numbers. Then, use comparison operators (
==,!=,<,>,<=,>=) to compare the numbers and print the results (e.g., "Number 1 is greater than Number 2: True"). - Exercise 3: String Manipulation. Write a Python program that takes a user's full name (first and last) as input and prints a formatted greeting like "Hello, [First Name] [Last Name]!". Hint: use string concatenation or f-strings.
Real-World Connections: Data Wrangling and Cleaning
In data science, a significant portion of the work involves data wrangling and cleaning. Understanding data types and operators is fundamental to this process.
- Type Conversion: Converting data types (e.g., strings to integers) is crucial when dealing with datasets where data might be incorrectly formatted. For example, you might need to convert strings representing numbers into numerical types for calculations.
- Operators: You'll use arithmetic, comparison, and logical operators to filter, transform, and validate data. For example, to filter data points based on a specific condition or compute new features.
- Example: Imagine you're analyzing sales data. You might need to convert the 'price' column (which is likely a string) to a float and then calculate the total revenue using arithmetic operators. You might use comparison operators to filter out records where the sales price is invalid.
Challenge Yourself: Building a Basic Calculator
Create a basic calculator program that takes two numbers and an operator (+, -, *, /) as input from the user. The program should perform the calculation and display the result. Include error handling for division by zero and invalid operators. Think about how to incorporate type conversion.
Further Learning
Continue your Python journey with these topics:
- String Formatting: Explore more advanced techniques for formatting strings (e.g., f-strings,
.format()method). - Lists and Tuples: Introduce the concept of sequences (lists and tuples) - the fundamental data structures for storing collections of data.
- Control Flow (if/else statements): Start learning how to create conditional logic in your code.
- Official Python Documentation: Deep dive into the official Python documentation for a comprehensive reference.
Interactive Exercises
Variable Practice
Create three variables: `name` (string), `age` (integer), and `height` (float). Assign your own values to these variables. Then, print each variable's value to the console.
Operator Challenge
Write a Python program that calculates the area of a rectangle. Prompt the user to enter the length and width as input (using `input()`), convert the input to numbers, and then calculate the area (area = length * width). Finally, print the calculated area.
Type Conversion Exploration
Create a variable `price_string` with the value "19.99". Convert this variable to a float using `float()`. Then, multiply the float value by 2 and print the result. What happens if you try to convert a string like "hello" to an integer? Try it out and observe the error.
Practical Application
Imagine you are building a simple calculator for a small business. You need to take input for the price of an item, calculate the sales tax (e.g., 7% of the price), and display the final price. Use variables, data types, and operators to create this program. You can use input() to get the item price from the user and the sales tax as a fixed amount.
Key Takeaways
Variables store data and are essential for any Python program.
Understanding data types (int, float, str, bool) is crucial for working with different kinds of information.
Operators perform operations on data, enabling calculations and comparisons.
Type conversion allows you to change the data type of a variable when needed.
Next Steps
In the next lesson, we'll delve into control flow: making decisions in your code using `if`, `elif`, and `else` statements.
Also, we'll learn about loops using `for` and `while` loops.
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