Introduction

Python is a fantastic tool that makes programming feel like a breeze. But sometimes, even the best of us can fall into little traps that make our code slower or take up more space than necessary. In this guide, we’ll walk through some common mistakes and learn how to dodge them, making our code not just work, but shine!

Choosing the Right Tool for the Job: Lists vs. Sets

• Oopsie

  Imagine you’re looking for your keys in a messy room. If you just throw everything into a big pile, it’s going to take forever to find them, right? That’s kind of like using a list in Python when you’re checking if something is there or not.

• Fix

  Now, imagine if you had a magic box that could tell you if your keys were inside in the blink of an eye. That’s what a set is like! So, when you want to check if something is in your pile of stuff, use a set, not a list.

    # Takes longer
    my_list = [1, 2, 3, 4, 5]
    if 3 in my_list:
        print("Found it!")
  
    # Super quick!
    my_set = {1, 2, 3, 4, 5}
    if 3 in my_set:
        print("Found it super quick!")
  

Keeping Things Close: Avoid Global Variables

• Oopsie

  Global variables are like putting your stuff all over the house. It might seem convenient, but it can make things messy and slow.

• Fix

  Try to keep your stuff in the room where you need it. In coding terms, that means using local variables inside your functions, not global ones.

    # Messy and a bit slow
    x = 10
    def add():
        global x
        x += 20
  
    # Nice and tidy
    def add(x):
        return x + 20
  

Making Lists the Smart Way: Use List Comprehensions

• Oopsie

  Creating lists with loops works, but it’s like writing out a shopping list by hand every single time.

• Fix

  Use list comprehensions! It’s like having a shopping list template that you can fill out super quickly.

    # Like writing it out by hand
    squares = []
    for x in range(10):
        squares.append(x*x)
  
    # Quick and easy template
    squares = [x*x for x in range(10)]
  

Stringing Things Along: Be Smart with Strings

• Oopsie

  Adding strings together in a loop is like tying a bunch of short strings together to make a long one. It works, but it takes a lot of time.

• Fix

  Use the join function! It’s like having a magic tool that ties all your strings together in one go.

    words = ["Hello", "World", "!"]
  
    # Takes a lot of time
    sentence = ""
    for word in words:
        sentence += word
  
    # Super quick!
    sentence = " ".join(words)
  

Handling Big Data: Use Generators

• Oopsie

  Trying to load a huge list of data all at once is like trying to carry all your groceries inside in one trip. You might drop something!

• Fix

  Use generators! They let you carry your data inside one item at a time, so you don’t get overwhelmed.

    # Trying to carry everything at once
    def read_file(file_name):
        with open(file_name, 'r') as f:
            return f.readlines()
  
    # One at a time, nice and easy
    def read_file(file_name):
        with open(file_name, 'r') as f:
            for line in f:
                yield line
  

Copying Wisely: Know When to Use Deep Copies

• Oopsie

  Making a deep copy of your data when you don’t need to is like buying a brand new phone just because you wanted a new case.

• Fix

  Use shallow copies when you can! It’s like just buying a new case instead of a whole new phone.

    import copy
  
    my_list = [1, 2, [3, 4]]
  
    # Like buying a new phone
    deep_copied_list = copy.deepcopy(my_list)
  
    # Just getting a new case
    shallow_copied_list = copy.copy(my_list)
  

Using the Tools Python Gives You

• Oopsie

  Writing your tools for everything is like making your hammer every time you need to hang a picture.

• Fix

  Use Python’s built-in functions and libraries! It’s like having a toolbox full of shiny tools ready to go.

    my_list = [1, 2, 3, 4, 5]
  
    # Making your own hammer
    def find_max(my_list):
        max_val = my_list[0]
        for num in my_list:
            if num > max_val:
                max_val = num
        return max_val
  
    # Using the shiny toolbox
    max_val = max(my_list)
  

Being Smart with Recursion: Avoid Overlapping Work

• Oopsie

  Using recursion for problems that repeat the same work over and over is like baking a cake, throwing it away, and then baking it again.

• Fix

  Use dynamic programming or loops for these kinds of problems. It’s like baking the cake once and then just enjoying it!

    # Baking the cake over and over
    def fibonacci(n):
        if n <= 1:
            return n
        else:
            return fibonacci(n-1) + fibonacci(n-2)
  
    # Bake it once, enjoy forever
    def fibonacci(n):
        a, b = 0, 1
        for _ in range(n):
            a, b = b, a + b
        return a
  

Being Lazy When You Can: Use Lazy Evaluation

• Oopsie

  Calculating everything right away, even if you don’t need it, is like cooking a huge meal even when you’re not hungry.

• Fix

  Use lazy evaluation and generators to calculate things only when you need them. It’s like cooking just enough food for when you’re hungry.

    # Cooking way too much food
    squares = [x*x for x in range(1000000)]
  
    # Just enough, just in time
    squares = (x*x for x in range(1000000))
  

Avoiding Mutable Default Arguments: Keep Functions Pure

• Oopsie

  Using mutable default arguments in functions is like writing in a notebook that everyone shares. Things can get messy!

• Fix

  Use immutable default arguments, or None and set the default inside the function. It’s like giving everyone their notebook.

    # Shared notebook (messy!)
    def add_item(item, my_list=[]):
        my_list.append(item)
        return my_list
  
    # Your own notebook (nice and clean)
    def add_item(item, my_list=None):
        if my_list is None:
            my_list = []
        my_list.append(item)
        return my_list
  

Choosing the Right Data Structures: Use Collections

• Oopsie

  Not using the right data structures is like trying to drink soup with a fork.

• Fix

  Use the collections module to get the right tool for the job. It’s like having a whole set of cutlery to choose from.

    from collections import defaultdict
  
    words = ["apple", "banana", "cherry", "apple", "banana", "cherry", "apple"]
  
    # Counting with a fork
    word_count = {}
    for word in words:
        if word not in word_count:
            word_count[word] = 0
        word_count[word] += 1
  
    # Counting with a spoon (so much easier!)
    word_count = defaultdict(int)
    for word in words:
        word_count[word] += 1
  

Concatenating Strings the Smart Way: Use Join

• Oopsie

  Adding strings together in a loop is like tying a bunch of short strings together to make a long one. It works, but it takes a lot of time.

• Fix

  Use the join function! It’s like having a magic tool that ties all your strings together in one go.

    words = ["Hello", "World", "!"]
  
    # Takes a lot of time
    sentence = ""
    for word in words:
        sentence += word
  
    # Super quick!
    sentence = " ".join(words)
  

Saving Memory with Slots: Use slots

• Oopsie

  Not using __slots__ in your classes is like packing your suitcase with your entire wardrobe for a weekend trip.

• Fix

  Use __slots__ to pack just what you need. It’s like having a perfectly packed suitcase that’s light and easy to carry.

    # Packing the whole wardrobe
    class Point:
        def __init__(self, x, y):
            self.x = x
            self.y = y
  
    # Just what you need
    class Point:
        __slots__ = ['x', 'y']
        def __init__(self, x, y):
            self.x = x
            self.y = y
  

Conclusion: Code Smart, Code Efficient

In the end, coding efficiently in Python is all about knowing the shortcuts and avoiding common pitfalls. By choosing the right data structures, avoiding unnecessary computations, and writing clean, readable code, you set yourself up for success. Remember, efficient code means a faster, smoother experience for both the developer and the end user. So, keep these tips in mind, and happy coding!