How to Create a Virtual Environment in Python
Python
21.03.2025
Reading time: 6 min
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This article will teach you how to create a Python virtual environment. It is useful for Python developers to avoid issues with different versions of libraries.
A simple example: You have two applications that rely on the same library, but each requires a different version.
Another example: You want to ensure that your application runs independently of library updates installed in the global Python storage.
A third example: You do not have access to this global storage.
The solution in all three cases is to create a Python virtual environment.
The module name venv is short for Virtual Environment. It is a great tool for project isolation, functioning like a sandbox. It allows you to run an application with its dependencies without interfering with other applications that use different versions of the same software. As a result, each application runs in its own virtual environment, isolated from others, increasing the overall stability of all applications.
How to Create a Virtual Environment in Python 3
Good news: You don’t need to install venv separately on Windows—it is part of the standard Python 3 library and comes with the interpreter.
On Linux, however, venv is not always included in the OS package, so you might need to install it. On Ubuntu/Debian, use the following command:
sudo apt install -y python3-venvSome Python packages require compilation from source code, so you might also need to install the following dependencies:
sudo apt install -y build-essential libssl-dev libffi-dev python3-devNow, let's see how to create a Python 3 virtual environment in Windows and Linux using venv.
Step 1: Creating the Virtual Environment
Use the following command for all operating systems:
python -m venv venvHere, -m tells Python to run the venv module. The second venv specifies the directory (venv/lib/python3.8/site-packages/, the version may vary) where Python will store all libraries and components required for isolated application execution.
Step 2: Activating the Virtual Environment
Activation differs between Windows and Linux.
On Windows, run:
venv\Scripts\activate.batOn Linux (and MacOS), use:
source venv/bin/activateIf everything is set up correctly, you will see an output like this:
(venv) root@purplegate:/var/test#Now you can start working on your project within the isolated environment!
Other Tools
Of course, venv is the most modern tool for creating virtual environments. However, it was only introduced in Python 3. So what should those working with older versions of Python do? The answer: try other tools that offer additional useful features—otherwise, we wouldn’t mention them at all. Below is a brief overview of these alternatives, followed by a more detailed look at the most popular one.
virtualenv– A simple and user-friendly tool that is widely used when deploying applications. It’s useful to learn, and we’ll provide instructions on how to use it below.pyenv– Allows you to isolate different Python versions. This is helpful if you need to run multiple versions of Python, for example, for testing purposes.virtualenvwrapper– A wrapper forvirtualenvthat helps manage virtual environments by simplifying tasks like creating, copying, and deleting environments. One of its advantages is that it allows easy switching between environments and supports various plugins for extended functionality.
Creating a Virtual Environment Using virtualenv
Let's go through the process using Linux as an example. However, running Python virtualenv on Windows is almost the same, with differences mainly in file paths and scripts, which we’ll mention separately.
Step 1: Install virtualenv
You can download the source code and install it manually, but the easiest way is to use pip. Just enter the following command in your terminal:
pip install virtualenvStep 2: Create a Virtual Environment
This step requires just a simple command:
virtualenv myenvThis will create a new directory in the current folder. Instead of myenv, you can use any other name for your environment.
Directory structure of the virtual environment:
/myenv/bin– Contains scripts for managing the environment, a copy of the Python interpreter,pip, and some package management utilities. In Windows, this folder is located at/myenv/Scripts./myenv/liband/myenv/include– Store the environment’s core libraries. Any newly installed files will go into/myenv/lib/pythonX.X/site-packages/, whereX.Xrepresents your Python version.
Step 3: Activate the Virtual Environment
Activation differs slightly between Linux and Windows.
For Linux, use:
source myenv/bin/activateFor Windows, run:
myenv\Scripts\activate.batOnce activated, you will see the virtual environment’s name in your command line prompt.
If you create the virtual environment with the --system-site-packages flag, it will have access to the system-wide package storage:
virtualenv --system-site-packages myenvKeep in mind that the system package paths differ:
- Linux:
/usr/lib/python3.8/site-packages - Windows:
\Python38\Lib\site-packages
Version numbers may vary depending on your installation.
Step 4: Deactivate the Virtual Environment
Once you’re done working in the Python virtual environment, you should exit it properly.
For Linux, run:
deactivateFor Windows, use the batch file:
myenv\Scripts\deactivate.batWhat's New?
In addition to the venv and virtualenv modules we’ve already covered, there are more modern tools that provide greater flexibility in managing Python projects, including virtual environments:
- Poetry – A package manager that helps manage application dependencies within a virtual environment. It also simplifies testing and deployment by automating many processes.
- Pipenv – Another package manager that integrates
pipandvirtualenv, along with several other useful tools. It is designed to make environment and package management easier, as many developers eventually encounter version control issues in their projects.
Each of these tools deserves a deep dive, but for now, let’s focus on the key features of both.
Poetry: The Essentials
Poetry handles all aspects of managing libraries within a virtual environment, including installing, updating, and publishing them. The functionality of pip alone is often insufficient for these tasks.
Additionally, Poetry allows you to create and package applications with a single command (replace myproject with your actual project name):
poetry new myprojectIf you want to initialize an existing directory as a Poetry project, use:
poetry initPoetry can also:
- Publish projects to private repositories
- Track and manage dependencies
- Enforce version control
- Simplify working on private virtual servers by ensuring reliable project isolation
Pipenv: The Essentials
In simple terms, Pipenv is like pip + virtualenv, but with enhanced features. It eliminates the need for the traditional and sometimes cumbersome requirements.txt file.
Instead, Pipenv uses:
- Pipfile.lock – Ensures package version consistency, which enhances security.
- Pipfile – A more advanced replacement for
requirements.txt. Unlike its predecessor, Pipfile updates automatically as package versions change, which is particularly useful for teams, reducing dependency conflicts.
Now you have a complete set of tools at your disposal, and managing multiple dependencies with different versions should no longer be a challenge!
Python
21.03.2025
Reading time: 6 min
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Python
Python Static Method
A static method in Python is bound to the class itself rather than any instance of that class. So, you can call it without first creating an object and without access to instance data (self).
To create a static method we need to use a decorator, specifically @staticmethod. It will tell Python to call the method on the class rather than an instance.
Static methods are excellent for utility or helper functions that are logically connected to the class but don't need to access any of its properties.
When To Use & Not to Use a Python Static Method
Static methods are frequently used in real-world code for tasks like input validation, data formatting, and calculations—especially when that logic naturally belongs with a class but doesn't need its state.
Here's an example from a User class that checks email format:
class User:
@staticmethod
def is_valid_email(email):
return "@" in email and "." in email
This method doesn't depend on any part of the User instance, but conceptually belongs in the class. It can be used anywhere as User.is_valid_email(email), keeping your code cleaner and more organized.
If the logic requires access to or modification of instance attributes or class-level data, avoid using a static method as it won't help here. For instance, if you are working with user settings or need to monitor object creation, you will require a class method or an instance method instead.
class Counter:
count = 0
@classmethod
def increment(cls):
cls.count += 1
In this example, using a static method would prevent access to cls.count, making it useless for this kind of task.
Python Static Method vs Class Method
Though they look similar, class and static methods in Python have different uses; so, let's now quickly review their differences.
Defined inside a class, a class method is connected to that class rather than an instance. Conventionally called cls, the class itself is the first parameter; so, it can access and change class-level data. Factory patterns, alternate constructors, or any activity applicable to the class as a whole and not individual instances are often implemented via class methods.
Conversely, a static method is defined within a class but does not start with either self or cls parameters. It is just a regular function that “lives” inside a class but doesn’t interact with the class or its instances. For utility tasks that are conceptually related to the class but don’t depend on its state, static methods are perfect.
Here's a quick breakdown of the Python class/static methods differences:
Feature
Class Method
Static Method
Binding
Bound to the class
Not bound to class or instance
First parameter
cls (class itself)
None (no self or cls)
Access to class/instance data
Yes
No
Common use cases
Factory methods, class-level behavior
Utility/helper functions
Decorator
@classmethod
@staticmethod
Python Static Method vs Regular Functions
You might ask: why not just define a function outside the class instead of using a static method? The answer is structure. A static method keeps related logic grouped within the class, even if it doesn't interact with the class or its instances.
# Regular function
def is_even(n):
return n % 2 == 0
# Static method inside a class
class NumberUtils:
@staticmethod
def is_even(n):
return n % 2 == 0
Both functions do the same thing, but placing is_even inside NumberUtils helps keep utility logic organized and easier to find later.
Let’s proceed to the hands-on Python static method examples.
Example #1
Imagine that we have a MathUtils class that contains a static method for calculating the factorial:
class MathUtils:
@staticmethod
def factorial(n):
if n == 0:
return 1
else:
return n * MathUtils.factorial(n-1)
Next, let's enter:
print(MathUtils.factorial(5))120
We get the factorial of 5, which is 120. Here, the factorial static method does not use any attributes of the class instance, only the input argument n. And we called it using the MathUtils.factorial(n) syntax without creating an instance of the MathUtils class.
In Python, static methods apply not only in classes but also in modules and packages. The @staticmethod decorator marks a function you define inside a class if it does not interact with instance-specific data. The function exists on its own; it is related to the class logically but is independent of its internal state.
Managed solution for Backend development
Example #2
Let's say we're working with a StringUtils module with a static method for checking if a string is a palindrome. The code will be:
def is_palindrome(string): return string == string[::-1]
This function doesn't rely on any instance-specific data — it simply performs a check on the input. That makes it a good candidate for a static method. To organize it within a class and signal that it doesn't depend on the class state, we can use the @staticmethod decorator like this:
class StringUtils: @staticmethod def is_palindrome(string): return string == string[::-1]
Let's enter for verification:
print(StringUtils.is_palindrome("deed"))True
print(StringUtils.is_palindrome("deer"))False
That's correct, the first word is a palindrome, so the interpreter outputs True, but the second word is not, and we get False.
So, we can call the is_palindrome method through the StringUtils class using the StringUtils.is_palindrome(string) syntax instead of importing the is_palindrome function and calling it directly.
-
Python static method and class instance also differ in that the static cannot affect the state of an instance. Since they do not have access to the instance, they cannot alter attribute values, which makes sense. Instance methods are how one may modify the instance state of a class.
Example #3
Let's look at another example. Suppose we have a Person class that has an age attribute and a static is_adult method that checks the value against the age of majority:
class Person: def __init__(self, age): self.age = age @staticmethod def is_adult(age): return age >= 21
Next, let's create an age variable with a value of 24, call the is_adult static method from the Person class with this value and store its result in the is_adult variable, like this:
age = 24is_adult = Person.is_adult(age)
Now to test this, let's enter:
print(is_adult)True
Since the age matches the condition specified in the static method, we get True. In the example, the is_adult static method serves as an auxiliary tool—a helper function—accepting the age argument but without access to the age attribute of the Person class instance.
Conclusion
Static methods improve code readability and make it possible to reuse it. They are also more convenient when compared to standard Python functions. Static methods are convenient as, unlike functions, they do not call for a separate import. Therefore, applying Python class static methods can help you streamline and work with your code greatly. And, as you've probably seen from the examples above, they are quite easy to master.
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16 April 2025 · 6 min to read
Python
Input in Python
Python provides interactive capabilities through various tools, one of which is the input() function. Its primary purpose is to receive user input. This function makes Python programs meaningful because without user interaction, applications would have limited utility.
How the Python Input Works
This function operates as follows:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
First, the user is asked to enter their name, then their age. Both inputs are captured using a special operator that stores the entered values in the variables user_name and user_age. These values can then be used in the program.
For example, we can create an age-based access condition for a website (by converting the age input to an integer using int()) and display a welcome message using the entered name:
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
So, what happens when int() receives an empty value? If the user presses Enter without entering anything, let's see what happens by extending the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
Pressing Enter moves the program to the next line of code. If there is no next line, the program exits. The last line can be written as:
input('Press Enter to exit')
If there are no more lines in the program, it will exit. Here is the complete version of the program:
user_name = input('Enter your name: ')
user_age = int(input('How old are you? '))
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
input('Press Enter to exit')
If the user enters an acceptable age, they will see the message inside the else block. Otherwise, they will see only the if block message and the final exit prompt. The input() function is used four times in this program, and in the last two cases, it does not store any values but serves to move to the next part of the code or exit the program.
input() in the Python Interpreter
The above example is a complete program, but you can also execute it line by line in the Python interpreter. However, in this case, you must enter data immediately to continue:
>>> user_name = input('Enter your name: ')
Enter your name: Jamie
>>> user_age = int(input('How old are you? '))
How old are you? 18
The code will still execute, and values will be stored in variables. This method allows testing specific code blocks. However, keep in mind that values are retained only until you exit the interactive mode. It is recommended to save your code in a .py file.
Input Conversion Methods: int(), float(), split()
Sometimes, we need to convert user input into a specific data type, such as an integer, a floating-point number, or a list.
Integer conversion (int())
We've already seen this in a previous example:
user_age = int(input('How old are you? '))
The int() function converts input into an integer, allowing Python to process it as a numeric type. By default, numbers entered by users are treated as strings, so Python requires explicit conversion. A more detailed approach would be:
user_age = input('How old are you? ')
user_age = int(user_age)
The first method is shorter and more convenient, but the second method is useful for understanding function behavior.
Floating-point conversion (float())
To convert user input into a floating-point number, use float():
height = float(input('Enter your height (e.g., 1.72): '))
weight = float(input('Enter your weight (e.g., 80.3): '))
Or using a more detailed approach:
height = input('Enter your height (e.g., 1.72): ')
height = float(height)
weight = input('Enter your weight (e.g., 80.3): ')
weight = float(weight)
Now, the program can perform calculations with floating-point numbers.
Converting Input into a List (split())
The split() method converts input text into a list of words:
animals = input('Enter your favorite animals separated by spaces: ').split()
print('Here they are as a list:', animals)
Example output:
Enter your favorite animals separated by spaces: cat dog rabbit fox bear
Here they are as a list: ['cat', 'dog', 'rabbit', 'fox', 'bear']
Handling Input Errors
Users often make mistakes while entering data or may intentionally enter incorrect characters. In such cases, incorrect input can cause the program to crash:
>>> height = float(input('Enter your height (e.g., 1.72): '))
Enter your height (e.g., 1.72): 1m72
Traceback (most recent call last):
File "<pyshell#2>", line 1, in <module>
height = float(input('Enter your height (e.g., 1.72): '))
ValueError: could not convert string to float: '1m72'
The error message indicates that Python cannot convert the string into a float. To prevent such crashes, we use the try-except block:
try:
height = float(input('Enter your height (e.g., 1.72): '))
except ValueError:
height = float(input('Please enter your height in the correct format: '))
We can also modify our initial age-input program to be more robust:
try:
user_age = int(input('How old are you? '))
except ValueError:
user_age = int(input('Please enter a number: '))
However, the program will still crash if the user enters incorrect data again. To make it more resilient, we can use a while loop:
while True:
try:
height = float(input('Enter your height (e.g., 1.72): '))
break
except ValueError:
print('Let’s try again.')
continue
print('Thank you!')
Here, we use a while loop with break and continue. The program works as follows:
If the input is correct, the loop breaks, and the program proceeds to the final message: print('Thank you!').
If the program cannot convert input to a float, it catches an exception (ValueError) and displays the message "Let’s try again."
The continue statement prevents the program from crashing and loops back to request input again.
Now, the user must enter valid data before proceeding.
Here is the complete code for a more resilient program:
user_name = input('Enter your name: ')
while True:
try:
user_age = int(input('How old are you? '))
break
except ValueError:
print('Are you sure?')
continue
if user_age < 18:
print('Sorry, access is restricted to adults only')
else:
print('Welcome to the site,', user_name, '!')
input('Press Enter to go to the menu')
print('Welcome to the menu')
input('Press Enter to exit')
This program still allows unrealistic inputs (e.g., 3 meters tall or 300 years old). To enforce realistic values, additional range checks would be needed, but that is beyond the scope of this article.
08 April 2025 · 6 min to read
Python
Operators in Python
Python operators are tools used to perform various actions with variables, as well as numerical and other values called operands—objects on which operations are performed. There are several types of Python operators:
Arithmetic
Comparison
Assignment
Identity
Membership
Logical
Bitwise
This article will examine each of them in detail and provide examples.
Arithmetic Operators
For addition, subtraction, multiplication, and division, we use the Python operators +, -, *, and / respectively:
>>> 24 + 28
52
>>> 24 - 28
-4
>>> 24 * 28
672
>>> 24 / 28
0.8571428571428571
For exponentiation, ** is used:
>>> 5 ** 2
25
>>> 5 ** 3
125
>>> 5 ** 4
625
For floor division (integer division without remainder), // is used:
>>> 61 // 12
5
>>> 52 // 22
2
>>> 75 // 3
25
>>> 77 // 3
25
The % operator returns the remainder (modulo division):
>>> 62 % 6
2
>>> 65 % 9
2
>>> 48 % 5
3
>>> 48 % 12
0
Comparison Operators
Python has six comparison operators: >, <, >=, <=, ==, !=.
Note that equality in Python is written as ==, because a single = is used for assignment. The != operator is used for "not equal to." When comparing values, Python will return True or False depending on whether the expressions are true or false.
>>> 26 > 58
False
>>> 26 < 58
True
>>> 26 >= 26
True
>>> 58 <= 57
False
>>> 50 == 50
True
>>> 50 != 50
False
>>> 50 != 51
True
Assignment Operators
A single = is used for assigning values to variables:
>>> b = 5
>>> variants = 20
Python also provides convenient shorthand operators that combine arithmetic operations with assignment: +=, -=, *=, /=, //=, %=. For example:
>>> cars = 5
>>> cars += 7
>>> cars
12
This is equivalent to:
>>> cars = cars + 7
>>> cars
12
The first version is more compact. Other assignment operators work similarly:
>>> train = 11
>>> train -= 2
>>> train
9
>>> moto = 3
>>> moto *= 7
>>> moto
21
>>> plain = 8
>>> plain /= 4
>>> plain
2.0
Notice that in the last case, the result is a floating-point number (float), not an integer (int).
Identity Operators
Python has two identity operators: is and is not. The results are True or False, similar to comparison operators.
>>> 55 is 55
True
>>> 55 is 56
False
>>> 55 is not 55
False
>>> 55 is not 56
True
>>> 55 is '55'
False
>>> '55' is "55"
True
In the last two examples:
55 is '55' returned False because an integer and a string were compared.
'55' is "55" returned True because both operands are strings.
Python does not differentiate between single and double quotes, so the identity check was successful.
Membership Operators
There are only two membership operators in Python: in and not in. They check whether a certain value exists within a sequence. For example:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'house' in wordlist
False
>>> 'assistant' in wordlist
True
>>> 'assistant' and 'streetcar' in wordlist
True
In the last case, a logical operator (and) was used, which leads us to the next topic.
Logical Operators
Python has three logical operators: and, or, and not.
and returns True only if all operands are true. It can process any number of values. Using an example from the previous section:
>>> wordlist = ('assistant', 'streetcar', 'fraudster', 'dancer', 'heat', 'blank', 'compass', 'commerce', 'judgment', 'approach')
>>> 'assistant' and 'streetcar' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' in wordlist
True
>>> 'fraudster' and 'dancer' and 'heat' and 'blank' and 'house' in wordlist
False
Since 'house' is not in the sequence, the result is False. These operations also work with numerical values:
>>> numbers = 54 > 55 and 22 > 21
>>> print(numbers)
False
One of the expressions is false, and and requires all conditions to be true.
or works differently: it returns True if at least one operand is true. If we replace and with or in the previous example, we get:
>>> numbers = 54 > 55 or 22 > 21
>>> print(numbers)
True
Here, 22 > 21 is true, so the overall expression evaluates to True, even though 54 > 55 is false.
not reverses logical values:
>>> first = True
>>> second = False
>>> print(not first)
False
>>> print(not second)
True
As seen in the example, not flips True to False and vice versa.
Bitwise Operators
Bitwise operators are used in Python to manipulate objects at the bit level. There are five of them (shift operators belong to the same type, as they differ only in shift direction):
& (AND)
| (OR)
^ (XOR)
~ (NOT)
<< and >> (shift operators)
Bitwise operators are based on Boolean logic principles and work as follows:
& (AND) returns 1 if both operands contain 1; otherwise, it returns 0:
>>> 1 & 1
1
>>> 1 & 0
0
>>> 0 & 1
0
>>> 0 & 0
0
| (OR) returns 1 if at least one operand contains 1, otherwise 0:
>>> 1 | 1
1
>>> 1 | 0
1
>>> 0 | 1
1
>>> 0 | 0
0
^ (XOR) returns 1 if the operands are different and 0 if they are the same:
>>> 1 ^ 1
0
>>> 1 ^ 0
1
>>> 0 ^ 1
1
>>> 0 ^ 0
0
~ (NOT) inverts bits, turning positive values into negative ones with a shift of one:
>>> ~5
-6
>>> ~-5
4
>>> ~7
-8
>>> ~-7
6
>>> ~9
-10
>>> ~-9
8
<< and >> shift bits by a specified number of positions:
>>> 1 << 1
2
>>> 1 >> 1
0
To understand shifts, let’s break down values into bits:
0 = 00
1 = 01
2 = 10
Shifting 1 left by one bit gives 2, while shifting right results in 0. What happens if we shift by two positions?
>>> 1 << 2
4
>>> 1 >> 2
0
1 = 001
2 = 010
4 = 100
Shifting 1 two places to the left results in 4 (100 in binary). Shifting right always results in zero because bits are discarded.
For more details, refer to our article on bitwise operators.
Difference Between Operators and Functions
You may have noticed that we have included no functions in this overview. The confusion between operators and functions arises because both perform similar actions—transforming objects. However:
Functions are broader and can operate on strings, entire blocks of code, and more.
Operators work only with individual values and variables.
In Python, a function can consist of a block of operators, but operators can never contain functions.
08 April 2025 · 6 min to read
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