So You’ve Got the Basics Down… Now What?

As a seasoned software engineer who’s seen their fair share of coding journeys (both smooth and bumpy!), I remember that initial feeling of “I know how to code!” followed by “But what do I even DO with this knowledge?” 🤯

You’ve mastered the fundamentals of Python. You can print “Hello, World!” to the console, manipulate lists like a pro, and maybe even have a basic grasp on loops and functions.

That’s awesome! 🎉 You’re officially past the “getting your feet wet” stage and ready to dive into something more challenging. But where do you start?

Fear not, fellow coder! This blog post is designed for those who’ve already taken their first steps in Python and are eager to build upon them. Let’s explore some cool things we can do with our newfound skills.

1. What Makes You “Intermediate”?

Being an intermediate Python programmer isn’t just about knowing more lines of code; it’s about understanding and applying fundamental concepts in a more nuanced way. Here are some areas where I’ve seen Pythonistas level up their game:

• Understanding Data Structures: You’re not just using lists, you’re exploring dictionaries (key-value pairs), sets (unique elements), and even tuples and lists to store complex data types like strings, numbers, and functions.

# Example of using a dictionary in Python to store student grades

student_grades = {
    "Alice": [90, 85, 70, 88],  
    "Bob": [82, 90, 88, 85],
    "Charlie": {"Python": 90, "Math": 85, "Science": 95}
}

# Now we can easily access grades by name using a key
print(student_grades["Alice"][0]) # Prints: 90 (Prints the grade of Alice)

• List Comprehension:

You’re probably already familiar with basic list operations, but did you know about this elegant Python feature? It allows you to create new lists by applying expressions to existing iterables like lists or tuples. Think of it as a compact way to write loops within your lists!

2. Common Mistakes & Challenges at This Stage:

As you progress in your “Python journey”, you’ll likely encounter some common challenges along the way. Here are a few examples from my experience:

• Confusing mutable and immutable data structures: I’ve seen many developers trip up on the difference between lists (mutable) and tuples (immutable). Remember, lists can be modified after creation, while tuples cannot!

# This creates a new list with each letter of "hello" in uppercase. This is correct!

my_string = "hello"
uppercase_string = [letter.upper() for letter in my_string]
print(uppercase_string) # Prints: ['H', 'E', 'L', 'L', 'O'] 

# This tries to change the string inside the tuple, resulting in an error!

my_tuple = "hello" # Remember, strings are immutable too!
# my_tuple[0] = "H".upper()  # TypeError: 'str' object does not support item assignment

• Incorrectly using == for comparing objects: Be careful! In Python, == checks for value equality, while is compares object identity. Always use is to check if two variables refer to the same object in memory.

• Modifying a list while iterating over it: This can lead to unexpected behavior as you are changing the size of the list during the iteration process.

# Example: Trying to modify a list directly within a loop that's iterating over it.

my_list = [1, 2, 3]
for i in range(len(my_list)):
    if i == 1:
        my_list.append(5) # This will change the length of my_list during iteration
        # my_list[1] = 5 # Incorrect: modifies the list while iterating

• Using range() for slicing: Remember, range() is used to generate a sequence of numbers, not for manipulating lists. Don’t use it directly with list comprehensions!

3. Beyond the Basics: Where Your Skills Will Grow

Moving from beginner to intermediate Python involves tackling more complex challenges like:

• Working with different data structures: As your code grows, you’ll learn how to effectively use lists, dictionaries, and sets for storing and manipulating various types of data in your programs.

# Example: Using a list comprehension to create a dictionary
numbers = [1, 2, 3, 4, 5]
squared_numbers = {num**2 for num in numbers} # This creates a dictionary using a set comprehension

• Understanding object-oriented programming (OOP): Learn the fundamentals of OOP like classes and objects to organize your code into reusable components.

class MyClass:
    def __init__(self, name, value): # Defining a class with an initialization method 
        self.name = name
        self.value = value

my_object = MyClass("MyObject", 42) # Creating an object of MyClass

# This is NOT how you'd typically initialize a list in a Pythonic way!

# Here's why:

```python
numbers = [1, 2, 3]  # More Pythonic way to create a list of numbers
# Using list slicing:

for i in range(len(numbers)):
    print(numbers[i]**2) # This prints the square of each element
# instead of using a set comprehension!

# Accessing the list comprehension elements
```python
my_list = {x**2 for x in [1, 2, 3] if x % 2 == 0} # Pythonic way
my_dict = {"even": 2, "odd": 1}

As you can see in the code examples above, even seemingly small changes in how we access and manipulate data can make a big difference in your code.

Python’s intermediate level will allow you to:

• Write more concise and readable code: You’ll learn about Pythonic best practices!
• Create modular programs: Building upon the knowledge of basic data structures, you can create more organized and reusable code.
• Understand and work with modules: This is like having a toolbox full of pre-built functions that are readily available for your use.

Let’s face it, we all know those early days of coding can be tough. You might be thinking, “I know Python syntax, but what now?”

Fear not! Learning about these more complex concepts and how to apply them efficiently is the hallmark of moving beyond “beginner” status.