10 Ways to Solve Everyday Problems Efficiently with Short Python Code

This article introduces 10 ways to solve everyday problems efficiently using short Python code. We’ll explain the key points in a way that’s easy for beginners to understand, so please use it as a reference.

Introduction

Python is a programming language characterized by its simple and readable syntax and abundant libraries. It’s used in a wide range of fields, including web development, data analysis, and machine learning, and it’s also very useful for automating simple everyday tasks. This time, we’ll introduce 10 specific examples of things you can do with code snippets of 10 lines or less!

1. Generating a Random Password

Use Cases

• Quickly create a password when signing up for a new service.
• Automatically generate and manage secure passwords.

Code Example

import random
import string

def generate_password(length=8):
    chars = string.ascii_letters + string.digits + string.punctuation
    return ''.join(random.choice(chars) for _ in range(length))

print(generate_password(12))

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Explanation

• string.ascii_letters includes uppercase and lowercase letters, string.digits includes numbers, and string.punctuation includes symbols.
• random.choice(chars) randomly selects one character, and this is repeated for the specified number of digits to create the password.
• The password length can be specified by the length argument.

2. Counting Specific Words in a Text File

Use Cases

• Analysis of text data or log files.
• Quickly check how many times a keyword appears.

Code Example

word = "Python"
with open("example.txt", "r") as file:
    text = file.read()
print(text.count(word))

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Explanation

• with open(“example.txt”, “r”) as file: opens the text file for reading, and file.read() retrieves the content as a string.
• The Python string method count() allows you to count the occurrences of a specific word all at once.
• When dealing with large text files, you might consider reading the file in chunks instead of using read().

3. Merging Two Dictionaries (Python 3.9+)

Use Cases

• Integrating multiple configuration files or configuration data.
• Managing key-value pairs collectively.

Code Example

dict1 = {"a": 1, "b": 2}
dict2 = {"b": 3, "c": 4}
merged_dict = dict1 | dict2
print(merged_dict)

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Explanation

• In Python 3.9 and later, merging dictionaries can be written simply using the | operator.
• If the same key exists in both the source and target dictionaries, the value from the right-hand dictionary overwrites the left (in the example above, “b” is updated from 2 to 3).

4. Removing Duplicates from a List

Use Cases

• Removing duplicate data such as user IDs or numerical values.
• Checking for and organizing duplicate elements.

Code Example

my_list = [1, 2, 2, 3, 4, 4, 5]
unique_list = list(set(my_list))
print(unique_list)

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Explanation

• The Python set type has the property of not allowing duplicates, so converting a list to a set extracts only the unique elements.
• Use list() to convert it back to a list if needed.
• If you need to maintain the order, you can use other methods (e.g., OrderedDict or dict.fromkeys).

5. Counting the Number of Lines in a File

Use Cases

• Checking the number of lines in a file before processing data.
• Quickly grasping the length of log files, etc.

Code Example

with open("example.txt", "r") as file:
    print(sum(1 for line in file))

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Explanation

• The idiom sum(1 for line in file) counts the number of lines by adding 1 for each line in the file.
• This method doesn’t put excessive strain on memory, even for large files.

6. Getting the Current Date and Time

Use Cases

• Adding timestamps when logging.
• Inserting the current date and time in automated notifications or reports.

Code Example

from datetime import datetime
now = datetime.now()
print(now.strftime("%Y-%m-%d %H:%M:%S"))

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Explanation

• datetime.now() gets the current time object.
• strftime() allows you to convert it to any desired format (e.g., Year-Month-Day Hour:Minute:Second), making it very versatile.

7. Web Scraping

Use Cases

• Automatically retrieve information from websites for analysis.
• Obtain regularly changing data and track updates.

Code Example

import requests
from bs4 import BeautifulSoup

url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
print(soup.title.string)

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Explanation

• The requests module is a library for easily making HTTP requests.
• Passing the obtained HTML to BeautifulSoup for parsing allows you to easily extract HTML elements like soup.title.string.
• In actual scraping, it’s common to specify the tags, class names, or IDs of the elements you want to extract information from.

8. Getting the Maximum and Minimum Values in a List

Use Cases

• Quickly find the minimum and maximum values from the elements of an array.
• Checking as the first step in numerical data analysis.

Code Example

numbers = [10, 20, 30, 40, 50]
print(max(numbers), min(numbers))

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Explanation

• max() and min() make it easy to get the maximum and minimum values from a list.
• They can quickly retrieve results even for lists with a large number of elements, making them useful for initial checks during data processing.

9. Merging PDFs

Use Cases

• Combining reports or documents into a single PDF file.
• Organizing and sharing multiple PDFs.

Code Example

from PyPDF2 import PdfMerger

merger = PdfMerger()
for pdf in ["file1.pdf", "file2.pdf"]:
    merger.append(pdf)
merger.write("merged.pdf")
merger.close()

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Explanation

• The PyPDF2 library allows you to merge multiple PDFs in order.
• PdfMerger.append() adds PDFs in the order they are specified, and finally, merger.write() creates the output file.
• It also has various other PDF-related functions, such as extracting and splitting pages, making it very convenient.

10. Conditionally Filtering Elements in a List

Use Cases

• Extracting elements that meet specific conditions during data analysis or cleaning.
• Extracting and processing only the necessary parts from a large list

Code Example

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = [n for n in numbers if n % 2 == 0]
print(even_numbers)

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Explanation

• List comprehensions can be used to create a new list containing only the elements that meet a condition.
• The example above filters for only even numbers (n % 2 == 0).
• It can handle more complex conditions and is very useful in data processing.