Deploying Python Applications with Gunicorn
Python
23.10.2024
Reading time: 7 min
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In this article, we’ll show how to set up an Ubuntu 20.04 server and install and configure the components required for deploying Python applications. We’ll configure the WSGI server Gunicorn to interact with our application. Gunicorn will serve as an interface that converts client requests via the HTTP protocol into Python function calls executed by the application. Then, we will configure Nginx as a reverse proxy server for Gunicorn, which will forward requests to the Gunicorn server. Additionally, we will cover securing HTTP connections with an SSL certificate or using other features like load balancing, caching, etc. These details can be helpful when working with cloud services like those provided by Hostman.
Creating a Python Virtual Environment
To begin, we need to update all packages:
sudo apt updateUbuntu provides the latest version of the Python interpreter by default. Let’s check the installed version using the following command:
python3 --versionExample output:
Python 3.10.12We’ll set up a virtual environment to ensure that our project has its own dependencies, separate from other projects. First, install the virtualenv package, which allows you to create virtual environments:
sudo apt-get install python3-venv python3-devNext, create a folder for your project and navigate into it:
mkdir myapp
cd myappNow, create a virtual environment:
python3 -m venv venvAnd create a folder for your project:
mkdir appYour project directory should now contain two items: app and venv.
You can verify this using the standard Linux command to list directory contents:
lsExpected output:
myapp venvYou need to activate the virtual environment so that all subsequent components are installed locally for the project:
source venv/bin/activateInstalling and Configuring Gunicorn
Gunicorn (Green Unicorn) is a Python WSGI HTTP server for UNIX. It is compatible with various web frameworks, fast, easy to implement, and uses minimal server resources.
To install Gunicorn, run the following command:
pip install gunicornWSGI and Python
WSGI (Web Server Gateway Interface) is the standard interface between a Python application running on the server side and the web server itself, such as Nginx. A WSGI server interacts with the application, allowing you to run code when handling requests. Typically, the application is provided as an object named application in a Python module, which is made available to the server.
In the standard wsgi.py file, there is usually a callable application. For example, let’s create such a file using the nano text editor:
nano wsgi.pyAdd the following simple code to the file:
from aiohttp import web
async def index(request):
return web.Response(text="Welcome home!")
app = web.Application()
app.router.add_get('/', index)
In the code above, we import aiohttp, a library that provides an asynchronous HTTP client built on top of asyncio. HTTP requests are a classic example of where asynchronous handling is ideal, as they involve waiting for server responses, during which other code can execute efficiently. This library allows sequential requests to be made without waiting for the first response before sending a new one. It’s common to run aiohttp servers behind Nginx.
Running the Gunicorn Server
You can launch the server using the following command template:
gunicorn [OPTIONS] [WSGI_APP]Here, [WSGI_APP] consists of $(MODULE_NAME):$(VARIABLE_NAME) and [OPTIONS] is a set of parameters for configuring Gunicorn.
A simple command would look like this:
gunicorn wsgi:appTo restart Gunicorn, you can use:
sudo systemctl restart gunicornSystemd Integration
systemdis a system and service manager that allows for strict control over processes, resources, and permissions. We’ll create a socket thatsystemdwill listen to, automatically starting Gunicorn in response to traffic.
Configuring the Gunicorn Service and Socket
First, create the service configuration file:
sudo nano /etc/systemd/system/gunicorn.serviceAdd the following content to the file:
[Unit]
Description=gunicorn daemon
Requires=gunicorn.socket
After=network.target
[Service]
Type=notify
User=someuser
Group=someuser
RuntimeDirectory=gunicorn
WorkingDirectory=/home/someuser/myapp
ExecStart=/path/to/venv/bin/gunicorn wsgi:app
ExecReload=/bin/kill -s HUP $MAINPID
KillMode=mixed
TimeoutStopSec=5
PrivateTmp=true
[Install]
WantedBy=multi-user.target
Make sure to replace /path/to/venv/bin/gunicorn with the actual path to the Gunicorn executable within your virtual environment. It will likely look something like this: /home/someuser/myapp/venv/bin/gunicorn.
Next, create the socket configuration file:
sudo nano /etc/systemd/system/gunicorn.socketAdd the following content:
[Unit]
Description=gunicorn socket
[Socket]
ListenStream=/run/gunicorn.sock
SocketUser=www-data
[Install]
WantedBy=sockets.target
Enable and start the socket with:
systemctl enable --now gunicorn.socketConfiguring Gunicorn
Let's review some useful parameters for Gunicorn in Python 3. You can find all possible parameters in the official documentation.
Sockets
-b BIND,--bind=BIND— Specifies the server socket. You can use formats like:$(HOST),$(HOST):$(PORT).
Example:
gunicorn --bind=127.0.0.1:8080 wsgi:appThis command will run your application locally on port 8080.
Worker Processes
-w WORKERS,--workers=WORKERS— Sets the number of worker processes. Typically, this number should be between 2 to 4 per server core.
Example:
gunicorn --workers=2 wsgi:appProcess Type
-k WORKERCLASS,--worker-class=WORKERCLASS— Specifies the type of worker process to run.
By default, Gunicorn uses the sync worker type, which is a simple synchronous worker that handles one request at a time. Other worker types may require additional dependencies.
Asynchronous worker processes are available using Greenlets (via Eventlet or Gevent). Greenlets are a cooperative multitasking implementation for Python. The corresponding parameters are eventlet and gevent.
We will use an asynchronous worker type compatible with aiohttp:
gunicorn wsgi:app --bind localhost:8080 --worker-class aiohttp.GunicornWebWorkerAccess Logging
You can enable access logging using the --access-logfile flag.
Example:
gunicorn wsgi:app --access-logfile access.logError Logging
To specify an error log file, use the following command:
gunicorn wsgi:app --error-logfile error.logYou can also set the verbosity level of the error log output using the --log-level flag. Available log levels in Gunicorn are:
-
debug
-
info
-
warning
-
error
-
critical
By default, the info level is set, which omits debug-level information.
Installing and Configuring Nginx
First, install Nginx with the command:
sudo apt install nginxLet’s check if the Nginx service can connect to the socket created earlier:
sudo -u www-data curl --unix-socket /run/gunicorn.sock httpIf successful, Gunicorn will automatically start, and you'll see the HTML code from the server in the terminal.
Nginx configuration involves adding config files for virtual hosts. Each proxy configuration should be stored in the /etc/nginx/sites-available directory.
To enable each proxy server, create a symbolic link to it in /etc/nginx/sites-enabled. When Nginx starts, it automatically loads all proxy servers in this directory.
Create a new configuration file:
sudo nano /etc/nginx/sites-available/myconfig.confThen create a symbolic link with the command:
sudo ln -s /etc/nginx/sites-available/myconfig.conf /etc/nginx/sites-enabledNginx must be restarted after any changes to the configuration file to apply the new settings.
First, check the syntax of the configuration file:
nginx -tThen reload Nginx:
nginx -s reloadConclusion
Gunicorn is a robust and versatile WSGI server for deploying Python applications, offering flexibility with various worker types and integration options like Nginx for load balancing and reverse proxying. Its ease of installation and configuration, combined with detailed logging and scaling options, make it an excellent choice for production environments. By utilizing Gunicorn with frameworks like aiohttp and integrating it with Nginx, you can efficiently serve Python applications with improved performance and resource management.
Python
23.10.2024
Reading time: 7 min
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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
Python
Command-Line Option and Argument Parsing using argparse in Python
Command-line interfaces (CLIs) are one of the quickest and most effective means of interacting with software. They enable you to provide commands directly which leads to quicker execution and enhanced features. Developers often build CLIs using Python for several applications, utilities, and automation scripts, ensuring they can dynamically process user input. This is where the Python argparse module steps in.
The argparse Python module streamlines the process of managing command-line inputs, enabling developers to create interactive and user-friendly utilities. As part of the standard library, it allows programmers to define, process, and validate inputs seamlessly without the need for complex logic.
This article will discuss some of the most important concepts, useful examples, and advanced features of the argparse module so that you can start building solid command-line tools right away.
How to Use Python argparse for Command-Line Interfaces
This is how to use argparse in your Python script:
Step 1: Import Module
First import the module into your Python parser script:
import argparse
This inclusion enables parsing .py arg inputs from the command line.
Step 2: Create an ArgumentParser Object
The ArgumentParser class is the most minimal class of the Python argumentparser module's API. To use it, begin by creating an instance of the class:
parser = argparse.ArgumentParser(description="A Hostman tutorial on Python argparse.")
Here:
description describes what the program does and will be displayed when someone runs --help.
Step 3: Add Inputs and Options
Define the parameters and features your program accepts via add_argument() function:
parser.add_argument('filename', type=str, help="Name of the file to process")
parser.add_argument('--verbose', action='store_true', help="Enable verbose mode")
Here:
filename is a mandatory option.
--verbose is optional, to allow you to set the flag to make it verbose.
Step 4: Parse User Inputs
Process the user-provided inputs by invoking the parse_args() Python method:
args = parser.parse_args()
This stores the command-line values as attributes of the args object for further use in your Python script.
Step 5: Access Processed Data
Access the inputs and options for further use in your program:
For example:
print(f"File to process: {args.filename}")
if args.verbose:
print("Verbose mode enabled")
else:
print("Verbose mode disabled")
Example CLI Usage
Here are some scenarios to run this script:
File Processing Without Verbose Mode
python3 file.py example.txt
File Processing With Verbose Mode
python3 file.py example.txt --verbose
Display Help
If you need to see what arguments the script accepts or their description, use the --help argument:
python3 file.py --help
Common Examples of argparse Usage
Let's explore a few practical examples of the module.
Example 1: Adding Default Values
Sometimes, optional inputs in command-line interfaces need predefined values for smoother execution. With this module, you can set a default value that applies when someone doesn’t provide input.
This script sets a default timeout of 30 seconds if you don’t specify the --timeout parameter.
import argparse
# Create the argument parser
parser = argparse.ArgumentParser(description="Demonstrating default argument values.")
# Pass an optional argument with a default value
parser.add_argument('--timeout', type=int, default=30, help="Timeout in seconds (default: 30)")
# Interpret the arguments
args = parser.parse_args()
# Retrieve and print the timeout value
print(f"Timeout value: {args.timeout} seconds")
Explanation
Importing Module: Importing the argparse module.
Creating the ArgumentParser Instance: An ArgumentParser object is created with a description so that a short description of the program purpose is provided. This description is displayed when the user runs the program via the --help option.
Including --timeout: The --timeout option is not obligatory (indicated by the -- prefix). The type=int makes the argument for --timeout an integer. The default=30 is provided so that in case the user does not enter a value, then the timeout would be 30 seconds. The help parameter adds a description to the argument, and it will also appear in the help documentation.
Parsing Process: The parse_args() function processes user inputs and makes them accessible as attributes of the args object. In our example, we access args.timeout and print out its value.
Case 1: Default Value Used
If the --timeout option is not specified, the default value of 30 seconds is used:
python file.py
Case 2: Custom Value Provided
For a custom value for --timeout (e.g., 60 seconds), apply:
python file.py --timeout 60
Example 2: Utilizing Choices
The argparse choices parameter allows you to restrict an argument to a set of beforehand known valid values. This is useful if your program features some specific modes, options, or settings to check.
Here, we will specify a --mode option with two default values: basic and advanced.
import argparse
# Creating argument parser
parser = argparse.ArgumentParser(description="Demonstrating the use of choices in argparse.")
# Adding the --mode argument with predefined choices
parser.add_argument('--mode', choices=['basic', 'advanced'], help="Choose the mode of operation")
# Parse the arguments
args = parser.parse_args()
# Access and display the selected mode
if args.mode:
print(f"Mode selected: {args.mode}")
else:
print("No mode selected. Please choose 'basic' or 'advanced'.")
Adding --mode: The choices argument indicates that valid options for the --mode are basic and advanced. The application will fail when the user supplies an input other than in choices.
Help Text: The help parameter gives valuable information when the --help command is executed.
Case 1: Valid Input
To specify a valid value for --mode, utilize:
python3 file.py --mode basic
Case 2: No Input Provided
For running the program without specifying a mode:
python3 file.py
Case 3: Invalid Input
If a value is provided that is not in the predefined choices:
python3 file.py --mode intermediate
Example 3: Handling Multiple Values
The nargs option causes an argument to accept more than one input. This is useful whenever your program requires a list of values for processing, i.e., numbers, filenames, or options.
Here we will show how to use nargs='+' to accept a --numbers option that can take multiple integers.
import argparse
# Create an ArgumentParser object
parser = argparse.ArgumentParser(description="Demonstrating how to handle multiple values using argparse.")
# Add the --numbers argument with nargs='+'
parser.add_argument('--numbers', nargs='+', type=int, help="List of numbers to process")
# Parse the arguments
args = parser.parse_args()
# Access and display the numbers
if args.numbers:
print(f"Numbers provided: {args.numbers}")
print(f"Sum of numbers: {sum(args.numbers)}")
else:
print("No numbers provided. Please use --numbers followed by a list of integers.")
Adding the --numbers Option: The user can provide a list of values as arguments for --numbers. type=int interprets the input as an integer. If a non-integer input is provided, the program raises an exception. The help parameter gives the information.
Parsing Phase: After parsing the arguments, the input to --numbers is stored in the form of a list in args.numbers.
Utilizing the Input: You just need to iterate over the list, calculate statistics (e.g., sum, mean), or any other calculation on the input.
Case 1: Providing Multiple Numbers
To specify multiple integers for the --numbers parameter, execute:
python3 file.py --numbers 10 20 30
Case 2: Providing a Single Number
If just one integer is specified, run:
python3 file.py --numbers 5
Case 3: No Input Provided
If the script is run without --numbers:
python3 file.py
Case 4: Invalid Input
In case of inputting a non-integer value:
python3 file.py --numbers 10 abc 20
Example 4: Required Optional Arguments
Optional arguments (those that begin with the --) are not mandatory by default. But there are times when you would like them to be mandatory for your script to work properly. You can achieve this by passing the required=True parameter when defining the argument.
In this script, --config specifies a path to a configuration file. By leveraging required=True, the script enforces that a value for --config must be provided. If omitted, the program will throw an error.
import argparse
# Create an ArgumentParser object
parser = argparse.ArgumentParser(description="Demonstrating required optional arguments in argparse.")
# Add the --config argument
parser.add_argument('--config', required=True, help="Path to the configuration file")
# Parse the arguments
args = parser.parse_args()
# Access and display the provided configuration file path
print(f"Configuration file path: {args.config}")
Adding the --config Option: --config is considered optional since it starts with --. However, thanks to the required=True parameter, users must include it when they run the script. The help parameter clarifies what this parameter does, and you'll see this information in the help message when you use --help.
Parsing: The parse_args() method takes care of processing the arguments. If someone forgets to include --config, the program will stop and show a clear error message.
Accessing the Input: The value you provide for --config gets stored in args.config. You can then use this in your script to work with the configuration file.
Case 1: Valid Input
For providing a valid path to the configuration file, use:
python3 file.py --config settings.json
Case 2: Missing the Required Argument
For running the script without specifying --config, apply:
python3 file.py
Advanced Features
While argparse excels at handling basic command-line arguments, it also provides advanced features that enhance the functionality and usability of your CLIs. These features ensure your scripts are scalable, readable, and easy to maintain. Below are some advanced capabilities you can leverage.
Handling Boolean Flags
Boolean flags allow toggling features (on/off) without requiring user input. Use the action='store_true' or action='store_false' parameters to implement these flags.
parser.add_argument('--debug', action='store_true', help="Enable debugging mode")
Including --debug enables debugging mode, useful for many Python argparse examples.
Grouping Related Arguments
Use add_argument_group() to organize related arguments, improving readability in complex CLIs.
group = parser.add_argument_group('File Operations')
group.add_argument('--input', type=str, help="Input file")
group.add_argument('--output', type=str, help="Output file")
Grouped arguments appear under their own section in the --help documentation.
Mutually Exclusive Arguments
To ensure users select only one of several conflicting options, use the add_mutually_exclusive_group() method.
group = parser.add_mutually_exclusive_group()
group.add_argument('--json', action='store_true', help="Output in JSON format")
group.add_argument('--xml', action='store_true', help="Output in XML format")
This ensures one can choose either JSON or XML, but not both.
Conclusion
The argparse Python module simplifies creating reliable CLIs for handling Python program command line arguments. From the most basic option of just providing an input to more complex ones like setting choices and nargs, developers can build user-friendly and robust CLIs. Following the best practices of giving proper names to arguments and writing good docstrings would help you in making your scripts user-friendly and easier to maintain.
21 July 2025 · 10 min to read
Python
How to Get the Length of a List in Python
Lists in Python are used almost everywhere. In this tutorial we will look at four ways to find the length of a Python list: by using built‑in functions, recursion, and a loop. Knowing the length of a list is most often required to iterate through it and perform various operations on it.
len() function
len() is a built‑in Python function for finding the length of a list. It takes one argument—the list itself—and returns an integer equal to the list’s length. The same function also works with other iterable objects, such as strings.
Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany"]
count = len(Country_list)
print("There are", count, "countries")
Output:
There are 4 countries
Finding the Length of a List with a Loop
You can determine a list’s length in Python with a for loop. The idea is to traverse the entire list while incrementing a counter by 1 on each iteration. Let’s wrap this in a separate function:
def list_length(list):
counter = 0
for i in list:
counter = counter + 1
return counter
Country_list = ["The United States of America", "Cyprus", "Netherlands", "Germany", "Japan"]
count = list_length(Country_list)
print("There are", count, "countries")
Output:
There are 5 countries
Finding the Length of a List with Recursion
The same task can be solved with recursion:
def list_length_recursive(list):
if not list:
return 0
return 1 + list_length_recursive(list[1:])
Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland"]
count = list_length_recursive(Country_list)
print("There are", count, "countries")
Output:
There are 6 countries
How it works. The function list_length_recursive() receives a list as input.
If the list is empty, it returns 0—the length of an empty list.
Otherwise it calls itself recursively with the argument list[1:], a slice of the original list starting from index 1 (i.e., the list without the element at index 0). The result of that call is added to 1. With each recursive step the returned value grows by one while the list shrinks by one element.
length_hint() function
The length_hint() function lives in the operator module. That module contains functions analogous to Python’s internal operators: addition, subtraction, comparison, and so on. length_hint() returns the length of iterable objects such as strings, tuples, dictionaries, and lists. It works similarly to len():
from operator import length_hint
Country_list = ["The United States of America", "Cyprus", "Netherlands","Germany", "Japan", "Poland", "Sweden"]
count = length_hint(Country_list)
print("There are", count, "countries")
Output:
There are 7 countries
Note that length_hint() must be imported before use.
Conclusion
In this guide we covered four ways to determine the length of a list in Python. Under equal conditions the most efficient method is len(). The other approaches are justified mainly when you are implementing custom classes similar to list.
17 July 2025 · 3 min to read
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