Python attracts a lot of developers due to its user-friendly learning curve, setting it apart from more intricate object-oriented languages such as Java, C++, C#, and JavaScript. But Python is primarily adopted because of its usability in emerging fields such as data science, machine learning, automation, AI, cybersecurity, cloud computing, and more. These lucrative career opportunities have encouraged more and more developers to learn Python.
Using Python for day-to-day tasks involves using repetitive code quite often. And let’s be honest, it’s tedious to remember every snippet you’ve ever used. You can simplify this using Python snippets. They help make your code more efficient and consistent throughout your workflow.
We’ll explore 11 Python boilerplate snippets in this article. Along with simply providing the snippets, we will also explain how to use them.
Before that, if you are wondering how you’ll be able to save these code snippets in Python for the future and find them exactly when you need them, we are here to help!
For easy saving and retrieval, we will share the links to these Python code snippets using Pieces for Developers. Pieces Shareable Links include the code and helpful metadata like tags and related links that provide more context around the snippets and enable you to seamlessly integrate them into your development workflow. Let's dive in!
1. Regular Expression
Regular Expression is one of Python's most effective and widely used techniques for tasks such as pattern matching, string searching and replacement, string validation, and many more. While using loops and lists, takes away the efficiency of your code, Regular Expressions make the task much simpler!
Check out the Python snippet below, which demonstrates how to validate an email format regular expression
import re
def validate_email(email):
email_pattern = re.compile(r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$')
return bool(email_pattern.match(email))
# Example usage:
email_to_validate = "user@example.com"
if validate_email(email_to_validate):
print(f"{email_to_validate} is a valid email address.")
else:
print(f"{email_to_validate} is not a valid email address.")
#Output: user@example.com is a valid email address.
2. Slicing
We can extract specific portions from sequences like lists, tuples, or strings efficiently with the help of slicing. It enhances readability by enabling direct access to sub-elements without using explicit loops.
This simple boilerplate Python snippet will help you slice the list like a Pro. Check out this example code:
my_string = "Hello, World!"
# Get characters from index 7 to 12 (exclusive)
substring = my_string[7:12]
print(substring) # Output: World
# Get every second character from index 0 to 10 (exclusive)
substring = my_string[0:10:2]
print(substring) # Output: Hlo o
# Slice from the beginning up to index 5 (exclusive)
substring = my_string[:5]
print(substring) # Output: Hello
3. Anagrams
To see if two words are anagrams, we need to verify if they are made up of the same set of characters or not, irrespective of the order of the characters.
For instance, in the example provided, "listen" and "silent" are anagrams because they consist of the same set of characters, even though the order is different.
Here's a simple Python boilerplate to check if two words are anagrams:
from collections import Counter
def are_anagrams(word1, word2):
return Counter(word1) == Counter(word2)
# Example usage:
word1 = "listen"
word2 = "silent"
result = are_anagrams(word1, word2)
print(f"{word1} and {word2} are {'anagrams' if result else 'not anagrams'}")
#Output: listen and silent are anagrams
4. Capitalize the First Letter
The title() method in Python is used to convert the first letter of each word of the input string to uppercase. It then prints the output string, with the capitalized characters.
Here's a simple Python boilerplate to capitalize the first letters of a string of words:
input_string = "hello world"
capitalized_string = input_string.title()
print(capitalized_string)
#Output: Hello World
5. Difference
The difference method is used with sets to find the elements that exist in one set but not in another. set1.difference(set2) is calling the difference method on set1 and passing set2 as an argument. This method returns a new set containing elements that are in set1 but not in set2.
This method is very useful when you want to compare two sets and identify the elements that are not shared.
Check out this simple Python boilerplate using difference:
set1 = {1, 2, 3, 4, 5}
set2 = {3, 4, 5, 6, 7}
# Find the elements that are in set1 but not in set2
difference_set = set1.difference(set2)
print("Elements in set1 but not in set2:", difference_set)
# Output: Elements in set1 but not in set2: {1, 2}
6. Difference By
The difference_by function serves the purpose of finding the difference between two arrays or lists based on a specific property or key. The "_by" part indicates that the difference is computed using a function.
For example, if you have lists of objects with 'id' attributes and you want to find items that are in one list but not the other, difference_by lets you do that with a straightforward key-based approach.
This is a more readable and efficient way to find differences in lists based on specific attributes.
Here's a simple Python boilerplate snippet using difference_by:
def difference_by(a, b, fn):
b = set(map(fn, b))
return [item for item in a if fn(item) not in b]
from math import floor
difference_by( [2.1, 1.21, [2.3, 3.4], floor)
# [1.2]
difference_by([{ 'x': 2 }, { 'x': 1 }], [{ 'x': 1 }], lambda v: v['x'])
# [ { x: 2 } ]
7. Comparison Chains
In Python, comparison chains are a concise and readable way to perform multiple comparisons in a single line of code. They are used to simplify complex conditional statements, which improves code readability and eliminates nested if statements.
It expresses compound conditions more effortlessly, as a series of comparisons linked together with logical operators.
Here's a simple Python boilerplate code snippet using a comparison chain:
def check_temperature(temperature):
if 0 <= temperature <= 20:
return "It's cold outside."
elif 21 <= temperature <= 30:
return "The weather is moderate."
else:
return "It's warm outside."
# Example usage
current_temperature = 25
result = check_temperature(current_temperature)
print(result)
# Output: The weather is moderate.
8. Special Strip
The strip method is an easy way to remove leading and trailing whitespace characters (such as spaces, tabs, and newline characters) from a string. It is mostly used to clean user inputs, process text data, and ensure consistency while formatting.
The strip method works by creating a new string with leading and trailing whitespace removed. It does not modify the original string; instead, it returns a new string with the desired changes.
Here's some Python boilerplate illustrating the usage of the strip method:
# Original string with leading and trailing spaces
original_string = " Hello, World! "
# Using strip to remove leading and trailing spaces
stripped_string = original_string.strip()
# Displaying the results
print("Original String:", repr(original_string))
# Output: Original String: ' Hello, World! '
print("Stripped String:", repr(stripped_string))
# Output: Stripped String: 'Hello, World!'
9. Find the Most Frequent Element in a List
In Python, finding the most frequent element in a list is a common task, and one concise approach involves utilizing the max(set()) construct. This Python code snippet uses the properties of sets to efficiently identify the element with the highest frequency.
Utilizing sets optimizes the process by reducing the list to unique elements, ensuring that each distinct element's frequency is calculated only once.
Here's a Python boilerplate demonstrating the use of max(set()) to find the most frequent element in a list:
# Original list
my_list = [1, 2, 3, 2, 4, 2, 5, 6, 2, 7, 8, 2, 9]
# Find the most frequent element using max(set())
most_frequent_element = max(set(my_list), key=my_list.count)
# Display the result
print("Original List:", my_list)
#Output: Original List: [1, 2, 3, 2, 4, 2, 5, 6, 2, 7, 8, 2, 9]
print("Most Frequent Element:", most_frequent_element)
#Output: Most Frequent Element: 2
10. Join a List of Strings to a Single String
In Python, squashing or joining a list of strings into a single string is a common operation, often used when dealing with textual data. This Python boilerplate snippet uses a delimiter to concatenate the individual strings into a unified text.
It is preferred for its efficiency while working with large lists of strings. It is also a clear and readable way to express the intention of joining strings.
Here's a snippet in Python demonstrating how to squash a list of strings into one string using join():
# Original list of strings
string_list = ['Hello', 'World', 'in', 'Python']
# Squash the list into one string using join()
result_string = ' '.join(string_list) # Using space as the delimiter
# Display the result
print("Original List of Strings:", string_list)
#Output: Original List of Strings: ['Hello', 'World', 'in', 'Python']
print("Squashed String:", result_string)
#Output: Squashed String: Hello World in Python
11. Shuffle
The shuffle function is used to randomly rearrange the elements of a list. The shuffle function operates in place, meaning it modifies the original list directly, rather than creating a new shuffled list. It randomizes the order of elements and is a simple and effective way to introduce unpredictability.
Here's a simple Python code snippet example to shuffle the elements of a list:
import random
# Original list
my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
# Shuffling the list using random.shuffle
random.shuffle(my_list)
# Displaying the result
print("Original List:", my_list)
# Output: Original List: [3, 8, 7, 1, 9, 6, 5, 4, 2]
We’ve come to the end of our curated list of Python script boilerplate that will be super useful for Python developers or developers just getting started with data science or machine learning.
To help you learn and code efficiently, the Pieces for Developers Web Extension allows you to copy and save Python boilerplate code from anywhere on the web and use it in the blink of an eye. You can even get insights on the snippet as you save it in Pieces. Try it yourself! Save one of the code snippets above, rich with tags, context, and related links, with a single click. Let's see how it works:
As you click the "Copy and Save" button (which appears under code snippets when you have the Pieces Chrome Extension, Edge Addon, or Firefox Addon installed on your machine), the code snippet is automatically saved in the Pieces Desktop App. Saved snippets are automatically enriched with a title, suggested searches, the snippet’s website of origin, related tags, and a brief description of what the code does. Ready to get started? Install the Pieces Desktop App for free.
We hope this article helped you gain a good understanding of some useful functions in Python, along with how and when to use them. To check out more useful snippets, don’t forget to visit the Pieces Python code snippets collection. Hope you enjoyed reading the article!
Top comments (2)
Great article!
Glad you found it helpful. ☺️