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How to Install Python on Windows 10

How to Install Python on Windows 10
Hostman Team
Technical writer
Python Windows
18.10.2024
Reading time: 5 min

Python is a high-level programming language used by millions of programmers and developers. It is intuitive, offers many useful tools and libraries, and is essential for working with and analyzing large datasets. However, Python is not pre-installed on Windows operating systems. This guide will walk you through installing Python on Windows 10.

Which Version to Choose

There are two main versions of Python: Python 2 and Python 3, and they are patible.

  • Python 3 was released in 2008 to address issues found in Python 2. It offers more straightforward, intuitive syntax, a wide range of useful libraries (especially for data analysis), and a large community supports it.

  • Python 2 is no longer supported, unlike Python 3. Therefore, for new projects, you only need Python 3. However, if you need to work on projects written in Python 2, you might still need this version, so we will also explain how to install it.

How to Install Python 2

To install Python 2 on Windows 10:

  1. Open your browser and go to the official website python.org.

  2. Go to the Downloads section.

  3. In the Downloads section, find the section for specific releases and locate the version you need.

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  1. The last Python 2 release is 2.7.18. Click on Download and check the related files.

  2. For your operating system, download the 64-bit installer (it is the last file in the list).

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  1. Once the file is downloaded, open it.

  2. Before starting the installation, the installer will allow you to choose the installation path and additional tools. Select the necessary options and start the installation.

How to Install Python 3 on Windows

There are several ways to install Python, each with its own features, advantages, and disadvantages:

  • Full Installation: Installs all components of Python, which is ideal for most projects.

  • Microsoft Store Installation: Suitable for development environments and running scripts.

  • NuGet Package Installation: Python comes as a ZIP file with the .nupkg extension, designed for continuous integration systems. It does not include the user interface. Ideal for building packages and running scripts.

  • Embeddable Package: Installs a minimal version of Python, often used as part of a larger application or project.

Full Installation Using the Official Installer

The steps for installing Python 3 from the official website are similar to those for Python 2. Here’s how to install Python 3 on Windows 10:

  1. Open your browser and go to python.org.

  2. Go to the Downloads section.

  3. Click on Download Python x.x.x (the latest version).

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  1. Once you click it, the installer will start downloading.

  2. After downloading, open the installer.

  3. If Python 3 is already installed, the installer will offer to update it to the version you just downloaded. If Python is not installed, it will offer a fresh installation. Make sure to check the following options:

    • Installing for all users.

    • Add Python to PATH (this allows you to use Python from the command line).

  4. There are two installation options:

    • Install now:
      • Administrator rights are not required.

      • Python will be installed in your user directory.

      • Standard libraries, test suites, a launcher, and pip will be installed.

    • Customize Installation:
      • May require administrator rights.

      • Python will be installed in the Program Files directory.

      • Additional features can be installed.

      • The Python standard library can be precompiled into bytecode.

  1. Choose the option that suits you and start the installation. After it finishes, disable the MAX_PATH length limitation to avoid errors related to file path length.

To check if the installation was successful, run the command python --version in the command line. If everything went well, you should see an output like this:

Python 3.10.8

Python is now installed and ready to use.

Installing Python via Microsoft Store

To install Python from the Microsoft Store:

  1. Open the Microsoft Store application.

  2. In the search bar, type Python 3.x, specifying the version you want (e.g., "Python 3.10").

  3. Click Get.

  4. The download and installation will begin automatically.

Installing Python Using NuGet

To install Python on Windows via NuGet:

  1. Go to the official website and navigate to the Downloads section. Select the recommended version and download it.

  2. After opening the downloaded file, installation will take just a few seconds.

  3. To install Python, open Windows PowerShell and run the following commands:

For the 64-bit version:

nuget.exe install python -ExcludeVersion -OutputDirectory

For the 32-bit version:

nuget.exe install pythonx86 -ExcludeVersion -OutputDirectory

Embeddable Package

The embeddable package provides a minimal Python environment. It is distributed as a ZIP file and is intended to integrate Python into larger applications. The embeddable package is not designed for direct user access.

You will have a fully isolated Python environment when extracted from the archive. It will be isolated from the user’s operating system, including environment variables (like PATH), the system registry, and any installed packages. The standard Python library is included in the embeddable package as compiled and optimized files. However, this version does not include a package manager (pip) or documentation.

You can download the embeddable package from the python.org website:

  1. Go to the Downloads section.

  2. Clicking on Download will download the regular version, so click the hyperlink for Python for Windows instead.

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  1. Click Latest Python 3 Release - Python x.x.x.

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  1. Scroll down to the Files section and choose one of the embeddable versions (for 64-bit or 32-bit systems).

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Conclusion

This guide has covered various methods for installing Python on Windows 10. For more useful Python resources, you can explore our tutorials. If you want to build a web service using Python, you can rent a cloud server at competitive prices with Hostman.

Python Windows
18.10.2024
Reading time: 5 min

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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. 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16 April 2025 · 6 min to read
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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
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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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