Installing Anaconda Python on Ubuntu
Python
23.10.2024
Reading time: 7 min
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Anaconda is a popular platform for data processing and machine learning. It supports Python and R programming languages and is used for large-scale data processing, predictive analytics, and scientific computing. You can install it on a local machine or scalable cloud servers from Hostman.
The Python distribution comes with 250 open-source data packages, and you can install over 7,500 additional packages from Anaconda repositories. It also includes the Conda package manager and the graphical user interface, Anaconda Navigator.
In this guide, you'll learn how to install the Anaconda distribution on the latest versions of Ubuntu.
Downloading the Anaconda Distribution
There are three options to download the Anaconda script:
-
Download via a browser.
-
Download using
wget. -
Download using
curl.
To download the distribution via a browser, go to the official Anaconda website in the Distribution section. Enter your email, and you’ll receive a download link. Download the 64-bit (x86) Installer for Linux.
To download the distribution using the wget utility, use the following command:
wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh --output anaconda.shWhen downloading, it’s important to specify the correct version. This command requests version 2024.06, which is the latest at the time of writing. If you need another version, specify its number (e.g., 2022.05). You can find the version number and changes on the Release Notes page in the documentation.
Pay attention to the syntax. In the end, we specify --output anaconda.sh, which is an optional argument that renames the file Anaconda3-2024.06-Linux-x86_64.sh to anaconda.sh for convenience, so you don’t have to type a long, complex filename during installation.
To verify the integrity of the data, compare the cryptographic hash using the checksum (optional step). To see the SHA-256 checksum, run:
sha256sum anaconda.shThe terminal will display a line of numbers and letters. Compare this checksum with the one provided on the Anaconda website for the corresponding version. If the hash doesn’t match, the file may not have been downloaded completely. Download it again and recheck the checksum.
Installing Anaconda
To work with Anaconda, you can use the graphical interface Navigator. For it to work correctly on Ubuntu, you need to install additional packages:
sudo apt install libgl1-mesa-glx libegl1-mesa libxrandr2 libxss1 libxcursor1 libxcomposite1 libasound2 libxi6 libxtst6If you don’t plan to use the graphical interface, these packages are not necessary.
Now that you have the distribution file, it's time to deploy the package manager with all components. Regardless of how you downloaded the distribution, deployment is done with a single command:
bash anaconda.shThe installation runs in dialog mode. First, you’ll be prompted to press Enter to continue. Then, press Enter to read the license agreement. If you agree to the terms, type 'yes' and press Enter again.
The next step is choosing the installation location. You can accept the default directory by pressing Enter. If you want to specify a different folder, enter the full path.
Installing Anaconda takes a few minutes. After completion, you'll be prompted to initialize Anaconda. Type 'yes' and press Enter. The installation wizard will automatically make the necessary changes to all required directories.
Activating the Installation
Activation refers to adding a new PATH variable. This allows the system to recognize commands given to Anaconda and its components. To activate, run the following command:
source ~/.bashrcAfter activation, the environment variables will be updated. Visually, this change is reflected by the appearance of the word base before the username.
To confirm the installation was successful, run:
conda listThe screen will display a list of all the installed Anaconda components.
Setting Up a Virtual Environment
By default, Anaconda uses the base environment for work. Working in a single environment can be inconvenient if you have multiple projects with different packages and versions. Anaconda Python virtual environments solve this issue. For each environment, you can specify the Python version, as well as the composition and versions of all packages.
For example, if you have a project on a Hostman server that uses Python 3.9, you can create a dedicated virtual environment for it with the following command:
conda create -n new_env python=3.9The syntax is quite simple:
-
create: the command to create a virtual environment; -
-n: the argument followed by the name of the new environment, in this case,new_env; -
python=3.9: specifies the version of Python to be used inside the virtual environment.
After running the command, information about the packages to be installed will be displayed. If you agree with the list, type 'yes' and press Enter.
To switch to the environment, you need to activate it:
conda activate new_envTo exit the environment, deactivate it:
conda deactivateInside the environment, you can install the packages needed for your project. There are two ways to do this:
-
Activate the environment and install packages within it.
-
Specify the environment's name when installing a package. For example:
conda install --name new_env numpyThis command can be run from the base environment, but the numpy library will be installed inside new_env.
You can create as many virtual environments as needed for your Anaconda projects. To display a full list of environments, use the command:
conda info --envsThe current environment will be marked with an asterisk (*).
Updating Anaconda
Updating Anaconda is a simple task. Open the terminal and run the following command:
conda update --allIf updates are available for Anaconda in Python 3, they will be displayed in a list. To confirm the installation of updates, type 'y' and press Enter.
You can also update package manager components individually. For example, if you know there’s a new version of the conda command-line utility, update it with the command:
conda update condaTo update the entire distribution without checking the list of updates, use this command:
conda update anacondaRemember to check for updates periodically to ensure you're using the latest versions of tools.
Complete Removal of Anaconda
There are two ways to uninstall the Anaconda package manager. Let’s review both.
Method 1: Manual Removal
Remove the installation directory and all other files created during installation with the following command:
rm -rf ~/anaconda3 ~/.condarc ~/.conda ~/.continuumMethod 2: Using anaconda-clean
This method is a bit more automated. You can use the anaconda-clean module to ensure all components are completely removed from the system. It helps remove configuration files. After that, you just need to delete the anaconda3 directory.
First, install the module:
install anaconda-cleanTo confirm the removal, enter 'y' in the prompt and press Enter.
Run the module after installation with the following command:
anaconda-cleanThe uninstaller will ask for confirmation before deleting each component. To avoid having to enter 'y' repeatedly, add the flag for automatic confirmation:
anaconda-clean --yesAfter the cleanup, a backup folder will appear in the user's home directory. Inside, you’ll find a backup of the last saved state, in case you change your mind and want to restore Anaconda on Ubuntu.
Once the cleaning module has finished, you can finally delete the package manager directory:
rm -rf ~/anaconda3To completely remove all traces of Anaconda from the system, also delete the PATH entry from the .bashrc file. This entry is added by default during installation.
Open the .bashrc file in any text editor. In this example, we'll use nano:
nano ~/.bashrcLook for the lines where conda is initialized. If Anaconda was installed recently, these lines will be near the end of the file. To speed up the search, use the Ctrl+W key combination. The lines will look something like this:
# >>> conda initialize >>>
# !! Contents within this block are managed by 'conda init' !!
__conda_setup="$('/home/linux/anaconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)"
if [ $? -eq 0 ]; then
eval "$__conda_setup"
else
if [ -f "/home/linux/anaconda3/etc/profile.d/conda.sh" ]; then
. "/home/linux/anaconda3/etc/profile.d/conda.sh"
else
export PATH="/home/linux/anaconda3/bin:$PATH"
fi
fi
unset __conda_setup
# <<< conda initialize <<<
Delete or comment out these lines in the file. To save changes in nano, press Ctrl + X and confirm the overwrite. The removal of Anaconda is now complete.
Conclusion
In this tutorial, we covered the key steps from installing Anaconda to fully removing it. Now you can properly install the package manager in your system, keep it up to date, and, if needed, completely remove the software components from Ubuntu.
Python
23.10.2024
Reading time: 7 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.
On our app platform you can find Python applications, such as Celery, Django, FastAPI and Flask.
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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