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How to Use f-strings in Python

How to Use f-strings in Python
Mohammad Waqas Shahid
Technical writer
Python
19.12.2024
Reading time: 9 min

Python f-strings, introduced in Python 3.6, revolutionized how developers format strings. These possess abilities of being precise, readable, & highly powerful which makes these a preferred method for string interpolation in Python. This manual covers all that there is so that you learn about all the different aspects about f-strings of python. It covers basic usage & goes all the way to advanced formatting techniques.

Introduction

Formatting a string is an integral part of programming in Python, helping developers to dynamically include data within strings. Among different processes of formatting strings, f-strings stand out because of their simplicity and performance benefits. f-strings are also known as formatted string literals. First came out  in Python 3.6, f-strings blend flexibility & efficiency, making them a go-to choice for many developers.

This manual will provide a detailed elaboration of f-strings, covering their syntax, features, & practical applications. By the end, you will be an expert in using f-strings effectively. It also proves much efficient for your code & is also better readable.

f-strings are also called formatted string literals.These are used for inserting expressions or variables, as well as function outputs directly. For creating an f-string, you can prefix string with either an uppercase or lowercase f. After that whatever will be placed in curly braces {} will be a part of the string. It will also be printed as if it was part of that string.

Benefits of f-strings

Using f-strings offers numerous benefits, including:

  • Enhanced Readability: Due to these there is seamless embedding of Variables & Expressions into strings
  • Improved Performance: As compared to other techniques that are used for formatting like str.format() or %-based formatting, they have faster performance.
  • Flexibility: Supports nested formatting, calling functions. & calculations.

f-strings Syntax

Its syntax is very straightforward.

f"string with {expression}"

Basic syntax example

name = "Alice"
age = 30
greeting = f"My name is {name}, and I am {age} years old."
print(greeting)

Output:

My name is Alice, and I am 30 years old.

Core functions of f-strings

Let’s dive into the essential features of f-strings.

Using Expressions Inside f-strings

F-strings not only embed  variables; they also allow the inclusion of any valid Python expression within the curly braces.

Example:

x = 10
y = 20
result = f"The sum of {x} and {y} is {x + y}."
print(result)

Output:

The sum of 10 and 20 is 30.

Formatting of numbers in f-strings

Formatted string literals provide an elegant way to display numbers with specific formatting options, like rounding, padding, or converting values to percentages.

Example:

pi = 3.14159
formatted_pi = f"Value of pi: {pi:.2f}"
print(formatted_pi)

Output:

Value of pi: 3.14

Escaping Curly Braces

For some cases, curly braces are used in the output text itself. To display literal curly braces in an f-string, use double braces {{ and }}.

Example:

template = f"Use {{braces}} to include special characters."
print(template)

Output:

Use {braces} to include special characters.

Multiline f-strings

F-strings can span multiple lines, making them useful for constructing large text blocks while maintaining readability.

Example:

title = "Python f-strings"
description = "powerful, fast, and easy to use"
message = f"""
Title: {title}
Description: f-strings are {description}.
"""
print(message)

Output:

Title: Python f-strings
Description: f-strings are powerful, fast, and easy to use.

Nesting and Combining f-strings

F-strings can contain other f-strings or be combined with traditional strings. This capability is helpful for dynamic and complex outputs.

Example:

name = "Bob"
info = f"{name.upper()}: {f'Name has {len(name)} characters'}"
print(info)

Output:

BOB: Name has 3 characters

Handling Lists and Dictionaries

With f-strings, you can directly access elements from lists or keys in dictionaries.

Example with Lists:

items = ["Python", "JavaScript", "C++"]
favorite = f"My favorite programming language is {items[0]}."
print(favorite)

Output:

My favorite programming language is Python.

Example with Dictionaries:

data = {"name": "Eve", "role": "Developer"}
message = f"{data['name']} works as a {data['role']}."
print(message)

Output:

Eve works as a Developer.

f-Strings vs. Other string Methods

Now, let's compare f-strings with other types of strings methods in python. 

% Formatting vs. f-Strings

The % operator, an older method, uses placeholders like %s for strings and %d for integers. While functional, it can be cumbersome and error-prone.

Example:

# % Formatting
name = "Alice"
age = 25
print("Hello, %s. You are %d years old." % (name, age))

# Equivalent f-string
print(f"Hello, {name}. You are {age} years old.")

Comparison:

  • % formatting requires tuples and placeholder matching, increasing complexity.
  • Python f-strings embed variables directly, making the code simpler and easier to read.

str.format() vs. f-Strings

The str.format() method introduced named placeholders, improving readability over % formatting. However, it still requires method calls, which can feel verbose.

Example:

# str.format()
print("Hello, {}. You are {} years old.".format(name, age))

# Equivalent f-string
print(f"Hello, {name}. You are {age} years old.")

Advanced Example: Named placeholders:

# str.format() with named placeholders
print("Hello, {name}. You are {age} years old.".format(name=name, age=age))

# Equivalent f-string
print(f"Hello, {name}. You are {age} years old.")

Comparison:

  • str.format() improves over % formatting but can still feel clunky.
  • f-strings streamline the process, especially for dynamic expressions.

String Concatenation vs. f-Strings

String concatenation combines strings using the + operator. While straightforward, it becomes inefficient for more complex formatting needs.

Example:

# String concatenation
print("Hello, " + name + ". You are " + str(age) + " years old.")

# Equivalent f-string
print(f"Hello, {name}. You are {age} years old.")

Comparison:

  • Concatenation requires explicit type conversion, increasing verbosity.
  • Python f-strings handle formatting and type conversion automatically.

Advanced Example: Including expressions:

# String concatenation
years_later = 5
print(name + " will be " + str(age + years_later) + " in " + str(years_later) + " years.")

# Equivalent f-string
print(f"{name} will be {age + years_later} in {years_later} years.")

Practical Applications of f-Strings in Python

Whether you're crafting dynamic SQL queries, improving logging efficiency, or processing data for analytics, f-strings in Python simplify your workflow and enhance code readability.

Generating Dynamic SQL Queries

In applications involving databases, f-strings perform really well to construct dynamic SQL queries by embedding variables directly into the query string.

Example:

# Generating SQL queries using f-strings
table_name = "users"
condition = "age > 30"
sql_query = f"SELECT * FROM {table_name} WHERE {condition};"
print(sql_query)
# Output: SELECT * FROM users WHERE age > 30;

By embedding variables into the SQL query string, f-strings in Python reduce the risk of syntax errors and make the code intuitive.

Enhancing Logging Statements

Logging plays an important role in debugging and monitoring applications. Python f-strings simplify logging statements, especially at the time at which dynamic data is included.

Example:

# Logging with f-strings
username = "Alice"
action = "logged in"
print(f"User {username} has {action} at 10:30 AM.")
# Output: User Alice has logged in at 10:30 AM.

Working with data for analytics purposes

Because of the use of f-strings, formatting strings dynamically based on variable content, is possible. This enables efficient and concise manipulation of data.

Example:

# Processing analytics data
metric = "conversion rate"
value = 7.5
print(f"The {metric} has increased to {value}%.")
# Output: The conversion rate has increased to 7.5%.

Crafting Dynamic File Paths

Automating the handling of a file often involves dynamically generating file paths. Python f-strings have made this process straightforward.

Example:

# Generating dynamic file paths
directory = "/data/exports"
filename = "report_2024.csv"
path = f"{directory}/{filename}"
print(path)
# Output: /data/exports/report_2024.csv

Dynamic Web Content Generation

During web development, HTML or JSON content can be generated dynamically by the use of f-strings.

Example:

# Dynamic HTML generation
title = "Welcome"
content = "This is a demo of Python f-strings in action."
html = f"<h1>{title}</h1><p>{content}</p>"
print(html)
# Output: <h1>Welcome</h1><p>This is a demo of Python f-strings in action.</p>

Automating titles of report

During reporting or analytics, titles often need to reflect about data that is being processed. f-strings in Python automate this with ease.

Example:

# Automating report titles
report_date = "December 2024"
report_title = f"Sales Report - {report_date}"
print(report_title)
# Output: Sales Report - December 2024

Advanced Formatting Features

f-strings are capable of handling alignment, width specifications, or time & date  formatting for creation of cleaner outputs.

Example:

# Aligning text
for name, score in [("Alice", 92), ("Bob", 87)]:
    print(f"{name:<10} | {score:>5}")

# Formatting dates
from datetime import datetime
now = datetime.now()
print(f"Current time: {now:%Y-%m-%d %H:%M:%S}")

Debugging Made Easier with f-Strings

f-strings are capable of showing error messages in more informative by embedding relevant expressions or variables.

Example:

value = 42
try:
    assert value > 50, f"Value {value} is not greater than 50."
except AssertionError as e:
    print(e)

Common Errors to Avoid

During the use of f-strings, a few common pitfalls include:

  • Forgetting to prefix the string with f: This results in a plain string without any formatting. Incompatible

  • Python versions: Ensure Python 3.6 or newer is installed, as f-strings are not supported in earlier versions.

Conclusion

F-strings are a robust and versatile tool for string formatting in Python. Whether you need to include variables, perform calculations, or debug your code, f-strings simplify such types of tasks with cleaner syntax & better performance.

If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

Python
19.12.2024
Reading time: 9 min

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Useful Tips for Web Data Scraping

In one of the previous articles, we learned what parsing is and looked at examples of obtaining data from HTML pages using Python. In this guide, we continue to move in that direction and offer web scraping best practices and tips that will help you automatically extract data from most existing websites. Obtaining data automatically may be prohibited by the terms of use of websites. We do not encourage violations of these terms, the rules specified in the robots.txt file, or any other applicable legal norms. Use the methods presented here only within permitted scenarios, and respect the policies of website owners. Tip 1. Learn to Work with DevTools By the way information is delivered, websites can be divided into two groups: static and dynamic. On static websites, all data is stored in the form of fixed HTML files that are kept on the server. Their contents do not change unless the developer modifies them. 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Most often, information reaches the site in two ways: In the HTML markup of the page. This happens if the data is added to the page during backend processing. In JSON format. Such data can be requested by the frontend both during page loading and after certain user actions on the page. Tip 2. Use a Ready-Made Algorithm to Start Working with Any Donor Site Below is an action algorithm recommended for starting work with any donor site: Find a GET request with content type text/html, which the browser sends when the page is initialized.To do this, go to the page from which you need to extract data. Open the web inspector in the Network tab. Clear the requests by clicking on the trash bin icon to the left of the request search bar. Reload the page with Ctrl + R on Windows/Linux or Command + R on macOS. One of the first requests will be the needed GET request with content type text/html. Click on the request you found. Then go to the Response tab. A preview mode of the server’s response will open. The page layout may appear broken; this is normal. Try to find the required data visually in the preview mode. For example, the HTML markup of articles on Hostman is generated by the server. If you needed to automatically obtain the text of an article, then most of the work would already be done. If you can’t find it visually, go to the HTML markup view mode of the server response (not to be confused with the Elements tab). Activate search within the response with Ctrl + F on Windows or Command + F on macOS. Enter an example of data that you know is definitely on the page (for instance, the developer knows that the article contains the phrase “configuring Nginx,” so that exact phrase can be searched). The browser will highlight the substring if matches are found. Often, if the information is delivered by the server as HTML markup, selector names remain the same. For convenience, you can use the standard element search tool with the mouse: Ctrl + Shift + C on Windows or Cmd + Shift + C on macOS. Press the shortcut and select the element directly on the page. The browser will show the desired element, and its selectors can be conveniently transferred into your code. If the required data is not present, proceed to the next step. Find the requests that contain only JSON. This is easiest to do by filtering: click on the search bar for requests and enter the filter: mime-type: application/json Go through each request with the donor site’s domain and repeat the search for data, as in the previous step. If no necessary data is found, then most likely you will need to resort to browser emulation to parse the information. Tip 3. Use Quick Export of Requests In most cases, along with the request, the browser sends request headers and cookies to the server. 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23 September 2025 · 12 min to read
Python

How to Use Python time.sleep()

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Example: import threading event = threading.Event() def worker():     print("Starting work in thread")     event.wait()     print("Event received, resuming work") thread = threading.Thread(target=worker) thread.start() import time time.sleep(3) event.set() In this case, the thread is blocked until event.set() is called. You can still use time.sleep() to set a maximum pause, but unlike plain sleep(), this approach allows more flexible control. The thread can be "woken up" immediately without waiting for the full interval. asyncio.sleep() for Asynchronous Programs In asynchronous Python programming (asyncio module), asyncio.sleep() is used. Unlike time.sleep(), it doesn’t block the entire thread but only suspends the current coroutine, allowing the event loop to continue running other tasks. Example: import asyncio async def main(): print("Start async work") await asyncio.sleep(2) print("2 seconds passed, resuming") asyncio.run(main()) This is especially useful when you have multiple asynchronous functions that should run in parallel without interfering with each other. If you use regular time.sleep() in async code, it will block the entire event loop, causing other coroutines to wait too. Common Issues When Using time.sleep()  The time.sleep() function is simple, but misusing it can cause unexpected problems. It’s important to understand how it affects program execution so you don’t block important processes. Blocking the Main Thread The main feature of time.sleep() is that it blocks the thread where it was called. If you use it in the main thread of a GUI application (for example, Tkinter or PyQt), the interface will stop responding, creating a "frozen" effect. To avoid this, use time.sleep() only in separate threads or switch to asynchronous approaches like asyncio.sleep() for non-blocking delays. In GUI applications, it’s better to use timers (QTimer, after, etc.), which call functions at intervals without blocking the interface. Use in Multithreaded and Asynchronous Code In multithreaded code, time.sleep() can be called independently in each thread, but note that it doesn’t automatically release the Global Interpreter Lock (GIL). While other threads can still run during one thread’s sleep, in Python this depends on OS-level thread scheduling. In asynchronous code, time.sleep() should be used cautiously. If called inside an event loop (like with asyncio.run()), it blocks the entire loop, defeating the benefits of async programming. Instead, use asyncio.sleep(), which hands control back to the scheduler, letting other coroutines run in the background. Real-World Example of Using time.sleep() Imagine you’re writing a script to periodically poll an external API, which, according to its rules, must not be called more than once every 30 seconds. If requests are too frequent, the server may return errors or block your IP. Solution using time.sleep(): import time def poll_api(): print("Making API request...") def main(): while True: poll_api() time.sleep(30) if __name__ == "__main__": main() Here, after each request, we pause for 30 seconds with time.sleep(). This ensures no more than two requests per minute, respecting the limits. Async alternative: import asyncio async def poll_api(): print("Making API request...") async def main(): while True: await poll_api() await asyncio.sleep(30) if __name__ == "__main__": asyncio.run(main()) This version doesn’t block the entire program, allowing other requests or tasks to run in the same async environment. It’s more flexible and scalable. Conclusion Organizing pauses and delays is an important aspect of Python development. time.sleep() is the first and most obvious tool for this, but choosing between time.sleep(), asyncio.sleep(), and other methods should depend on your project’s architecture. In single-threaded scripts and console utilities, time.sleep() is perfectly fine, but for multithreaded and asynchronous applications, other mechanisms are better. Key recommendations: Use time.sleep() for short delays in tests, pauses between requests, or interface demonstrations. Don’t block the main thread of GUI applications to avoid a "frozen" interface. In async code, replace time.sleep() with asyncio.sleep() to keep the event loop efficient. In multithreaded programs, remember only the current thread pauses, but GIL affects scheduling. In special cases, use threading.Event() or input() to wait for events or user actions.
19 September 2025 · 8 min to read
Python

How to Delete Characters from a String in Python

When writing Python code, developers often need to modify string data. Common string modifications include: Removing specific characters from a sequence Replacing characters with others Changing letter case Joining substrings into a single sequence In this guide, we will focus on the first transformation—deleting characters from a string in Python. It’s important to note that strings in Python are immutable, meaning that any method or function that modifies a string will return a new string object with the changes applied. Methods for Deleting Characters from a String This section covers the main methods in Python used for deleting characters from a string. We will explore the following methods: replace() translate() re.sub() For each method, we will explain the syntax and provide practical examples. replace() The first Pyhton method we will discuss is replace(). It is used to replace specific characters in a string with others. Since strings are immutable, replace() returns a new string object with the modifications applied. Syntax: original_string.replace(old, new[, count]) Where: original_string – The string where modifications will take place old – The substring to be replaced new – The substring that will replace old count (optional) – The number of occurrences to replace (if omitted, all occurrences will be replaced) First, let’s remove all spaces from the string "H o s t m a n": example_str = "H o s t m a n" result_str = example_str.replace(" ", "") print(result_str) Output: Hostman We can also use the replace() method to remove newline characters (\n). example_str = "\nHostman\nVPS" print(f'Original string: {example_str}') result_str = example_str.replace("\n", " ") print(f'String after adjustments: {result_str}') Output: Original string: Hostman VPS String after adjustments: Hostman VPS The replace() method has an optional third argument, which specifies the number of replacements to perform. example_str = "Hostman VPS Hostman VPS Hostman VPS" print(f'Original string: {example_str}') result_str = example_str.replace("Hostman VPS", "", 2) print(f'String after adjustments: {result_str}') Output: Original string: Hostman VPS Hostman VPS Hostman VPS String after adjustments: Hostman VPS Here, only two occurrences of "Hostman VPS" were removed, while the third occurrence remained unchanged. We have now explored the replace() method and demonstrated its usage in different situations. Next, let’s see how we can delete and modify characters in a string using translate(). translate( The Python translate() method functions similarly to replace() but with additional flexibility. Instead of replacing characters one at a time, it allows mapping multiple characters using a dictionary or translation table. The method returns a new string object with the modifications applied. Syntax: original_string.translate(mapping_table) In the first example, let’s remove all occurrences of the $ symbol in a string and replace them with spaces: example_str = "Hostman$Cloud$—$Cloud$Service$Provider." print(f'Original string: {example_str}') result_str = example_str.translate({ord('$'): ' '}) print(f'String after adjustments: {result_str}') Output: Original string: Hostman$Cloud$—$Cloud$Service$Provider. String after adjustments: Hostman Cloud — Cloud Service Provider. To improve code readability, we can define the mapping table before calling translate(). This is useful when dealing with multiple replacements: example_str = "\nHostman%Cloud$—$Cloud$Service$Provider.\n" print(f'Original string: {example_str}') # Define translation table example_table = {ord('\n'): None, ord('$'): ' ', ord('%'): ' '} result_str = example_str.translate(example_table) print(f'String after adjustments: {result_str}') Output: Original string: Hostman%Cloud$—$Cloud$Service$Provider. String after adjustments: Hostman Cloud — Cloud Service Provider. re.sub() In addition to replace() and translate(), we can use regular expressions for more advanced character removal and replacement. Python's built-in re module provides the sub() method, which searches for a pattern in a string and replaces it. Syntax: re.sub(pattern, replacement, original_string [, count=0, flags=0]) pattern – The regular expression pattern to match replacement – The string or character that will replace the matched pattern original_string – The string where modifications will take place count (optional) – Limits the number of replacements (default is 0, meaning replace all occurrences) flags (optional) – Used to modify the behavior of the regex search Let's remove all whitespace characters (\s) using the sub() method from the re module: import re example_str = "H o s t m a n" print(f'Original string: {example_str}') result_str = re.sub('\s', '', example_str) print(f'String after adjustments: {result_str}') Output: Original string: H o s t m a nString after adjustments: Hostman Using Slices to Remove Characters In addition to using various methods to delete characters, Python also allows the use of slices. As we know, slices extract a sequence of characters from a string. To delete characters from a string by index in Python, we can use the following slice: example_str = "\nHostman \nVPS" print(f'Original string: {example_str}') result_str = example_str[1:9] + example_str[10:] print(f'String after adjustments: {result_str}') In this example, we used slices to remove newline characters. The output of the code: Original string:HostmanVPSString after adjustments: Hostman VPS Apart from using two slice parameters, you can also use a third one, which specifies the step size for index increments. For example, if we set the step to 2, it will remove every odd-indexed character in the string. Keep in mind that indexing starts at 0. Example: example_str = "Hostman Cloud" print(f'Original string: {example_str}') result_str = example_str[::2] print(f'String after adjustments: {result_str}') Output: Original string: Hostman CloudString after adjustments: HsmnCod Conclusion In this guide, we learned how to delete characters from a string in Python using different methods, including regular expressions and slices. The choice of method depends on the specific task. For example, the replace() method is suitable for simpler cases, while re.sub() is better for more complex situations.
23 August 2025 · 5 min to read

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