1.4: Python Fundamentals for AI Part-1

Python Basics: Variables, Data Types, Loops, and Conditionals

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Follow Along on Google Colab!

You can interact with this entire tutorial in a Google Colab notebook. Open the Colab notebook here to run the code yourself, experiment with examples, and see the results in real time. No setup needed—just click and start coding!

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Welcome to Your Python Adventure!

Imagine you’re about to step into a land of endless possibilities. This is the land of Artificial Intelligence (AI), where machines understand language, recognize images, and even drive cars. Your guide through this magical world? Python. It’s the language behind AI’s most exciting breakthroughs, and it’s designed to be friendly, readable, and powerful.

If you’re new to programming, you might feel a little intimidated—but don’t worry! This guide is built for absolute beginners, filled with stories, real-world analogies, and even a few jokes. By the end, you’ll be able to write basic Python code with confidence.

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Why Python for AI?

Python is like a super-tool that makes AI accessible even to those who are new to programming. Think of it as a language that lets you speak to machines, ask them to do things for you, and understand the results they give back. Python’s syntax (or the way it’s written) is like plain English, so it’s easier to learn than many other programming languages.

Python is widely used in AI because:

1. It’s easy to read and write.
2. It has a huge community and tons of libraries (pre-built code).
3. It’s flexible enough to tackle small tasks and massive projects alike.

Ready to start learning the basics? Let’s jump in!

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1. Python Basics: The Building Blocks

Imagine trying to build a house. You’d start with a foundation, right? You wouldn’t try to install windows without walls or set up furniture without floors. Python works the same way: you start with the basics—variables, data types, loops, and conditionals. These are the foundation stones of programming, the essential tools you’ll use over and over.

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1.1 Variables: Your First Python Tool

Variables are like labeled storage boxes. Think of them as virtual boxes where you store information for later. Imagine you’re packing for a trip, and you have boxes labeled “clothes,” “toiletries,” and “snacks.” Each box holds specific items, and you can access them when needed by just looking at the label.

In Python, variables work the same way. You can create variables to hold numbers, text, or other data. Later, if you want to use that data, you just refer to the variable name.

age = 25       # This variable stores the age
name = "Alice" # This variable stores a name
print(age)
print(name)

Result:

25
Alice

Here, age and name are the labels, and 25 and "Alice" are the data stored in them. You can think of these labels as your quick access point to the data they represent.

A Quick Tip on Naming Variables:

Think of your variable names as labels you’d want to be able to read later. Just like you wouldn’t label a box “A01” and expect to remember it’s for snacks, you wouldn’t want to name your age variable a. Use meaningful names like age, temperature, or greeting. Good names make code easier to read and understand.

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1.2 Data Types: Different Kinds of Storage Boxes

Not all data is created equal. Just as you wouldn’t put soup in a paper bag, you need the right kind of variable “container” for different types of data. In Python, data types define what kind of information each variable can hold, so it knows how to treat the data.

Python has several main data types:

• Integers (int): Whole numbers like 7, 15, or 2023.
• Floats (float): Decimal numbers like 3.14 or 98.6.
• Strings (str): Text, enclosed in quotes, like "Hello World!".
• Booleans (bool): True or False values like True or False.

These data types help Python understand what kind of operations it can perform on the data. Just as you wouldn’t try to eat a fork, you can’t perform certain operations on the wrong type of data.

Quick Example:

score = 100           # Integer
temperature = 98.6    # Float
greeting = "Hi!"      # String
is_logged_in = True   # Boolean

print(score)
print(temperature)
print(greeting)
print(is_logged_in)

Result:

100
98.6
Hi!
True

Why Data Types Matter: Imagine trying to add “3 apples” to “5 oranges.” Python won’t understand what you mean because those aren’t numbers it can add directly. Data types tell Python what kind of data it’s working with, so it can treat each variable correctly.

Data Type Fun Fact:

In Python, you don’t have to tell it what type of data you’re working with—it figures it out for you! It’s like Python has a superpower that lets it guess the type based on what you store in the variable.

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2. Loops: Getting Python to Repeat Itself

Loops are like giving instructions to someone who’s willing to repeat a task as many times as you need. Imagine having to clean every room in your house. Instead of cleaning one room, walking back to the list, and repeating the process, you’d likely decide on a pattern and repeat it for each room. Loops in Python let you automate repetitive actions just like that.

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2.1 For Loop: When You Know Exactly How Many Times to Repeat

A for loop is like a countdown timer that runs a set number of times. Imagine you’re preparing name cards for each guest at a dinner party. Instead of writing out each card by hand, you could use a for loop to go through each name and print a welcome message.

guests = ["Alice", "Bob", "Charlie", "Diana", "Evan"]

for guest in guests:
    print("Welcome to the party, " + guest + "!")

Result:

Welcome to the party, Alice!
Welcome to the party, Bob!
Welcome to the party, Charlie!
Welcome to the party, Diana!
Welcome to the party, Evan!

Explanation:

• for guest in guests: This line means “for each guest in the list called guests, do the following…”
• print("Welcome to the party, " + guest + "!"): This prints a customized welcome message for each guest.

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2.2 While Loop: When You’re Not Sure How Many Times to Repeat

Sometimes, you don’t know how many times something will need to repeat. Maybe you’re waiting for a bus, and you want to keep checking until it arrives. This is where a while loop shines—it keeps going until a specific condition is met.

waiting = True
minutes = 0

while waiting:
    print(f"Still waiting... it’s been {minutes} minutes.")
    minutes += 1
    
    if minutes >= 5:
        waiting = False
        print("Finally, the bus is here!")

Result:

Still waiting... it’s been 0 minutes.
Still waiting... it’s been 1 minutes.
Still waiting... it’s been 2 minutes.
Still waiting... it’s been 3 minutes.
Still waiting... it’s been 4 minutes.
Finally, the bus is here!

Explanation:

• while waiting: This line means “keep running this loop as long as waiting is True.”
• minutes += 1: This adds one to minutes each time the loop runs.
• if minutes >= 5: When minutes reaches 5, the loop stops because waiting becomes False.

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3. Conditionals: Making Choices Based on Conditions

Conditionals let Python make choices, just like you do in real life. Imagine you’re choosing an outfit. If it’s cold, you’ll wear a jacket. If it’s hot, you’ll go for something lighter. Python can make similar decisions using if, elif, and else.

temperature = 30

if temperature > 25:
    print("It's hot outside! Wear something light.")
elif temperature < 15:
    print("It's cold outside! Grab a jacket.")
else:
    print("It's just right.")

Result:

It's hot outside! Wear something light.

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4. Practical Exercises: Putting It All Together

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Exercise 1: Calculating the Mean of a List

Let’s say you’re tracking daily temperatures for a week and want to find the average temperature. In programming, the average is called the “mean.” Here’s how you’d calculate it using Python.

temperatures = [72, 75, 79, 80, 73, 78, 76]
total = sum(temperatures)   # Sum up all the values in the list
count = len(temperatures)   # Get the number of items in the list
mean = total / count        # Divide the total by the count
print("The average temperature is:", mean)

Result:

The average temperature is: 76.14285714285714

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Exercise 2: Summarizing a List of Values

Let’s go a step further. Imagine you’re a fitness coach, and you want to get a quick summary of the number of miles a client has walked each day in a week. You don’t just want the average—you also want to know the total miles walked and the count of days.

Let’s create a function that takes any list of numbers and gives a summary.

def summarize(values):
    count = len(values)         # Count the items in the list
    total = sum(values)         # Get the sum of all items
    mean = total / count        # Calculate the mean
    print(f"Count: {count}, Sum: {total}, Mean: {mean}")

summarize([10, 20, 30, 40, 50])  # Replace with any list of values

Result:

Count: 5, Sum: 150, Mean: 30.0

Explanation:

1. Define the Function: def summarize(values): sets up a function called summarize that can be used over and over again with any list.
2. Count, Total, and Mean: The function calculates the number of items, the sum of those items, and the average (mean) of the list.
3. Print Summary: It prints out the count, total, and mean for easy viewing.

With functions like this, you’re well on your way to organizing data in meaningful ways. You can change the numbers in the list to represent daily steps, hours studied, or even monthly expenses!

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Try It Yourself!

The best way to learn Python (or any programming language) is by doing. To make this journey more interactive, we’re hosting these exercises as Jupyter notebooks. You can open them on platforms like GitHub, Google Colab, or Kaggle, where you can type in the code, run it, and see the results in real-time—no installation required! Just click, type, and run the cells, and watch your code come alive.

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Wrapping Up: You’ve Taken Your First Steps!

Congratulations! You’ve covered a lot in a short time:

• Variables to store data,
• Data Types to classify your data,
• Loops to repeat tasks,
• Conditionals to make decisions, and
• Basic Exercises to put your skills to the test.

These are the foundational skills that every programmer uses every day. Just like learning to ride a bike, you might feel a bit wobbly at first, but with practice, you’ll start moving smoothly and confidently. Keep experimenting with these basics, and don’t be afraid to break things. Coding is all about testing, tweaking, and learning from mistakes.

Looking Ahead

In the next part, we’ll go even deeper into Python with functions, lists, and dictionaries—three tools that will give you even more control and flexibility. These will allow you to start creating your own mini-programs, setting you up for more complex projects as you move closer to the world of AI.

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Key Takeaways

1. Practice Makes Perfect: Try out these basics over and over. The more you practice, the more natural it will feel.
2. Don’t Be Afraid to Experiment: Coding is all about problem-solving, so play around with code to see what works and what doesn’t.
3. Break Big Problems into Small Steps: Even the most complex programs are just a series of small, simple steps put together.

Thank you for joining on this first step toward mastering Python for AI. See you in the next chapter, where we’ll dive deeper and start unlocking even more of Python’s potential!