As a machine learning engineer, you know how crucial it is to have a go-to language that’s capable of handling large datasets and has a multitude of libraries and frameworks. Python is the language of choice for many in the field due to its versatility and efficiency. However, like any language, there are still hidden tricks and tips that can improve your coding efficiency. In this article, we will walk you through three Python tips that every machine learning engineer should have in their toolkit.
List Comprehension
List comprehension is a powerful feature in Python that allows you to create lists in a readable and concise manner. It’s particularly helpful when you need to create a new list from an existing list or filter out elements from a list based on certain conditions. Consider the following examples:
# create a list of even numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = [x for x in numbers if x % 2 == 0]
print(even_numbers)
# create a list from a string
string = "hello world"
letters = [x for x in string]
print(letters)
# create a list with multiple conditions
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
filtered_numbers = [x for x in numbers if x % 2 == 0 or x % 3 == 0]
print(filtered_numbers)The output will be:
[2, 4, 6, 8, 10]
['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']
[2, 3, 4, 6, 8, 9, 10]In the first example, we create a new list called even_numbers that only contains even numbers from the original list numbers. We use the condition if x % 2 == 0 to filter out the odd numbers.
In the second example, we create a list of letters from the string “hello world” using list comprehension.
In the third example, we create a new list called filtered_numbers that contains numbers that are divisible by 2 or 3.
With list comprehension, you can also create lists of dictionaries, tuples, and sets.
Generator Expressions
Generator expressions are similar to list comprehension, but instead of creating a list, they create a generator object. This object is an iterator that can generate values on the fly. This technique can be useful when you’re working with large datasets and want to avoid storing all the values in memory at once. Here is an example:
# create a generator of even numbers
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = (x for x in numbers if x % 2 == 0)
for number in even_numbers:
print(number)The output will be:
2
4
6
8
10In this example, we generate only even numbers from the original list using a generator expression. By iterating with a for loop, we print the output values one by one.
Generator expressions are more memory-efficient than list comprehension because they don’t keep the entire output in memory at once. They are also useful with functions that take iterators as arguments, such as sum(), max(), and min(). Here is an example of a generator expression with a nested condition:
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_and_divisible_by_three = (x for x in numbers if x % 2 == 0 and x % 3 == 0)
for number in even_and_divisible_by_three:
print(number)The output will be:
6In this example, we create a generator expression that generates only the even numbers that are also divisible by three.
Decorators
Decorators are a powerful feature in Python that enable you to modify a function’s behaviour without changing its source code. They can be used to add functionality to existing functions, such as logging, caching, and error handling. Here’s an example:
# A decorator that logs a function's name and arguments
def log(func):
def wrapper(*args, **kwargs):
print("Calling function:", func.__name__)
print("Arguments:", args, kwargs)
return func(*args, **kwargs)
return wrapper
# A function that adds two numbers
@log
def add(a, b):
return a + b
# Call the function
add(2, 3)The output will be:
Calling function: add
Arguments: (2, 3) {}
5In this example, we define a decorator called log that takes a function as an argument and returns a new function called wrapper. The wrapper function logs the name of the function and its arguments, and then calls the original function. By using the @ syntax, we apply the decorator to the add function. When we call the add function, it automatically logs its name and arguments before returning the result.
Decorators can modify class methods as well as functions. This makes them especially useful when working on larger projects that involve object-oriented programming.
Conclusion
This article has covered three Python tips that can help machine learning engineers become more efficient: list comprehension, generator expressions, and decorators. These techniques make code more readable, efficient and save you time in the long run. By mastering these techniques, you can improve your Python programming skills and become an even more capable machine learning engineer.
