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How to Install pip on Windows

How to Install pip on Windows
Awais Khan
Technical writer
Python Windows
15.01.2025
Reading time: 6 min

pip is a utility that turns Python package installation and management into a straightforward task. From Python beginners to coding wizards, having this utility on your Windows computer is a true game-changer. It effortlessly facilitates the setup of crucial frameworks and libraries for your development needs. Automating package management with pip frees up your time and reduces the complications linked to manual installations.

Follow this guide to become proficient in configuring pip and overseeing your Python packages seamlessly.

pip Setup Process for Windows

Here are the guidelines to set up pip on a Windows machine.

Step 1: Confirm Installation

Verify Python is operational on your device before starting the pip setup. To carry out this operation, run command prompt and apply:

python --version

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If Python's not present on your system, download it from the official site.

Step 2: Download get-pip.py

Python's standard installation package automatically includes pip. However, in case of accidental removal, grab the get-pip.py script. 

You have a couple of options: either visit the pip.py webpage, or use the curl command for a quick install:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

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Note: Installing Python again to get pip is also an option. However, it can sometimes lead to conflicts with other dependencies or settings. Your existing Python setup stays unchanged with this script.

Step 3: Run get-pip.py

Move to the script’s location through the command prompt and apply:

python get-pip.py

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This will smoothly install pip on your device.

Step 4: Confirm pip Installation

Validate the installation by executing:

pip --version

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Applying this command ensures pip is installed on the system.

Step 5: Add pip to System PATH

If the command doesn't execute properly, update your system PATH with these instructions to incorporate pip:

  • Access Properties by right-clicking on My Computer or This PC from the drop-down menu.

  • Opt for Advanced system settings.

  • Select Environment Variables.

  • Head over to System Variables, spot the Path variable, and choose Edit.

  • Insert the Python Scripts directory into your system PATH, for example, C:\Python39\Scripts.

Alternative Ways for pip Installation on Windows

Let's discuss a few other ways to effortlessly get pip running on Windows.

Via Built-in ensurepip Module

From Python 3.4 onward, there's an awesome built-in module named ensurepip. With this tool, pip installation is simplified, eliminating the need for the get-pip.py script.

Step 1: Run ensurepip

Input the command below to set up pip:

python -m ensurepip --default-pip

Step 2: Verify pip Installation

Check pip version through:

pip --version

Python Installer Approach for pip Installation

Ensure the pip checkbox is marked during the Python setup. Here's how:

Step 1: Download Installer

Fire up your favorite browser, go to the official Python website, and acquire the most recent installation file.

Step 2: Launch the Installer

Launch the installer you've downloaded and remember to pick the Add Python to PATH option while setting up.

Step 3: Install pip

While progressing through the setup, don't forget to enable the Install pip option.

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Step 4: Validate pip is Installed

When the setup wraps up, check pip installation via:

pip --version

Adjusting pip Version: Upgrade or Downgrade

pip can be adjusted to suit your requirements by upgrading or downgrading. Here's how:

Upgrading pip

To give pip a fresh upgrade, execute:

python -m pip install --upgrade pip

Downgrading pip

To roll back pip, apply:

python -m pip install pip==<version>

Enter the desired version number to install instead of <version> (e.g., 21.0).

Resolving pip Installation Issues: Essential Commands

Let's discover common pip installation issues and their fixes:

Issue 1: "pip" is not recognized as an internal or external command

Solution: This implies the pip path isn't set in your system PATH. Simply follow the instructions in "Step 5" to fix this.

Issue 2: Permission Denied

Solution: Elevate your command prompt privileges by right-clicking the Command Prompt icon and choosing Run as administrator. Afterward, rerun the commands.

Issue 3: Missing Dependencies

Solution: Sometimes, you'll run into trouble because of missing dependencies. To correct this, manually install the essential dependencies with pip. For example:

pip install package_name

Swap out package_name for the appropriate dependency.

Utilizing Virtual Environments

Employing virtual environments keeps dependencies distinct and avoids any conflicts. Here's how to utilize a virtual environment with pip:

Creating a Virtual Environment

python -m venv env_name

Replace env_name with your desired environment name.

Initiating Your Virtual Environment

env_name\Scripts\activate

Standard pip Commands

To explore pip's usage, check these essential commands:

Installing a Package

pip install package_name

Modify package_name to accurately reflect the package you're aiming to install.

Uninstalling a Package

pip uninstall package_name

Showing Installed Packages

pip list

Showing Package Information

pip show package_name

Optimal Strategies for Package Management

  • Employ virtual environments to handle dependencies efficiently in multiple projects.

  • Regularly inspect and upgrade your packages to keep everything running smoothly.

  • Prepare requirements files to ease the management of dependencies in your projects.

Securing pip Installation

Ensuring the protection of packages handled by pip is critical. Here are some tips to keep your environment secure:

  • Maintain project isolation to avoid conflicts and secure installations.

  • Check the trustworthiness and verification of package sources before installing. Always refer to official repositories and examine reviews if they are available.

  • Consistently update pip and your packages to stay protected with the latest security patches and improvements.

  • Periodically review your dependencies for known vulnerabilities. Tools such as pip-audit can assist in identifying and resolving security concerns.

  • Adhere to secure coding standards and steer clear of deprecated or insecure packages.

Integrating pip with IDEs

pip can be effortlessly embedded into various Integrated Development Environments (IDEs), significantly boosting your development efficiency:

  • VS Code: Utilize the built-in terminal for direct pip command and package management within the editor.

  • PyCharm: Streamline package management by setting up pip configurations via the project interpreter. This simplifies the process of installing and managing packages customized to your project's specific needs.

  • Jupyter Notebook: Employ magic commands in the notebook interface for direct package installation. This provides a smooth and integrated experience for managing dependencies while you work on your interactive notebooks. 

Conclusion

Windows offers several methods to set up pip, catering to different preferences and requirements. No matter if you select the .py script, use Python's built-in ensurepip module, or enable pip during the initial setup, these approaches will make sure pip is properly configured on your system. This all-in-one guide empowers you to handle and install Python packages with ease.

Don't forget, keeping pip updated is essential for ensuring the security and efficiency of your Python setup. Routinely check for updates and keep pip upgraded.

In addition, on our application platform you can find Python apps, such as Celery, Django, FastAPI and Flask.

Python Windows
15.01.2025
Reading time: 6 min

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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. 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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. 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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. 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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.
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How to Get the Length of a List in Python

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17 July 2025 · 3 min to read
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Understanding the main() Function in Python

In any complex program, it’s crucial to organize the code properly: define a starting point and separate its logical components. In Python, modules can be executed on their own or imported into other modules, so a well‑designed program must detect the execution context and adjust its behavior accordingly.  Separating run‑time code from import‑time code prevents premature execution, and having a single entry point makes it easier to configure launch parameters, pass command‑line arguments, and set up tests. When all important logic is gathered in one place, adding automated tests and rolling out new features becomes much more convenient.  For exactly these reasons it is common in Python to create a dedicated function that is called only when the script is run directly. Thanks to it, the code stays clean, modular, and controllable. That function, usually named main(), is the focus of this article. All examples were executed with Python 3.10.12 on a Hostman cloud server running Ubuntu 22.04. Each script was placed in a separate .py file (e.g., script.py) and started with: python script.py The scripts are written so they can be run just as easily in any online Python compiler for quick demonstrations. What Is the main() Function in Python The simplest Python code might look like: print("Hello, world!")  # direct execution Or a script might execute statements in sequence at file level: print("Hello, world!")       # action #1 print("How are you, world?") # action #2 print("Good‑bye, world...")  # action #3 That trivial arrangement works only for the simplest scripts. As a program grows, the logic quickly becomes tangled and demands re‑organization: # function containing the program’s main logic (entry point) def main():     print("Hello, world!") # launch the main logic if __name__ == "__main__":     main()                    # call the function with the main logic With more actions the code might look like: def main(): print("Hello, world!") print("How are you, world?") print("Good‑bye, world...") if __name__ == "__main__": main() This implementation has several important aspects, discussed below. The main() Function The core program logic lives inside a separate function. Although the name can be anything, developers usually choose main, mirroring C, C++, Java, and other languages.  Both helper code and the main logic are encapsulated: nothing sits “naked” at file scope. # greeting helper def greet(name): print(f"Hello, {name}!") # program logic def main(): name = input("Enter your name: ") greet(name) # launch the program if __name__ == "__main__": main() Thus main() acts as the entry point just as in many other languages. The if __name__ == "__main__" Check Before calling main() comes the somewhat odd construct if __name__ == "__main__":.  Its purpose is to split running from importing logic: If the script runs directly, the code inside the if block executes. If the script is imported, the block is skipped. Inside that block, you can put any code—not only the main() call: if __name__ == "__main__":     print("Any code can live here, not only main()") __name__ is one of Python’s built‑in “dunder” (double‑underscore) variables, often called magic or special. All dunder objects are defined and used internally by Python, but regular users can read them too. Depending on the context, __name__ holds: "__main__" when the module runs as a standalone script. The module’s own name when it is imported elsewhere. This lets a module discover its execution context. Advantages of Using  main() Organization Helper functions and classes, as well as the main function, are wrapped separately, making them easy to find and read. Global code is minimal—only initialization stays at file scope: def process_data(data): return [d * 2 for d in data] def main(): raw = [1, 2, 3, 4] result = process_data(raw) print("Result:", result) if __name__ == "__main__": main() A consistent style means no data manipulation happens at the file level. Even in a large script you can quickly locate the start of execution and any auxiliary sections. Isolation When code is written directly at the module level, every temporary variable, file handle, or connection lives in the global namespace, which can be painful for debugging and testing. Importing such a module pollutes the importer’s globals: # executes immediately on import values = [2, 4, 6] doubles = [] for v in values: doubles.append(v * 2) print("Doubled values:", doubles) With main() everything is local; when the function returns, its variables vanish: def double_list(items): return [x * 2 for x in items] # create a new list with doubled elements def main(): values = [2, 4, 6] result = double_list(values) print("Doubled values:", result) if __name__ == "__main__": main() That’s invaluable for unit testing, where you might run specific functions (including  main()) without triggering the whole program. Safety Without the __name__ check, top‑level code runs even on import—usually undesirable and potentially harmful. some.py: print("This code will execute even on import!") def useful_function(): return 42 main.py: import some print("The logic of the imported module executed itself...") Console: This code will execute even on import! The logic of the imported module executed itself... The safer some.py: def useful_function():     return 42 def main():     print("This code will not run on import") main() plus the __name__ check guard against accidental execution. Inside main() you can also verify user permissions or environment variables. How to Write main() in Python Remember: main() is not a language construct, just a regular function promoted to “entry point.” To ensure it runs only when the script starts directly: Tools – define helper functions with business logic. Logic – assemble them inside main() in the desired order. Check – add the if __name__ == "__main__" guard.  This template yields structured, import‑safe, test‑friendly code—excellent practice for any sizable Python project. Example Python Program Using main() # import the standard counter from collections import Counter # runs no matter how the program starts print("The text‑analysis program is active") # text‑analysis helper def analyze_text(text): words = text.split() # split text into words total = len(words) # total word count unique = len(set(words)) # unique word count avg_len = sum(len(w) for w in words) / total if total else 0 freq = Counter(words) # build frequency counter top3 = freq.most_common(3) # top three words return { 'total': total, 'unique': unique, 'avg_len': avg_len, 'top3': top3 } # program’s main logic def main(): print("Enter text (multiple lines). Press Enter on an empty line to finish:") lines = [] while True: line = input() if not line: break lines.append(line) text = ' '.join(lines) stats = analyze_text(text) print(f"\nTotal number of words: {stats['total']}") print(f"Unique words: {stats['unique']}") print(f"Average word length: {stats['avg_len']:.2f}") print("Top‑3 most frequent words:") for word, count in stats['top3']: print(f" {word!r}: {count} time(s)") # launch program if __name__ == "__main__": main() Running the script prints a prompt: Enter text (multiple lines). Press Enter on an empty line to finish: Input first line: Star cruiser Orion glided silently through the darkness of intergalactic space. Second line: Signals of unknown life‑forms flashed on the onboard sensors where the nebula glowed with a phosphorescent light. Third line: The cruiser checked the sensors, then the cruiser activated the defense system, and the cruiser returned to its course. Console output: The text‑analysis program is active Total number of words: 47 Unique words: 37 Average word length: 5.68 Top‑3 most frequent words: 'the': 7 time(s) 'cruiser': 4 time(s) 'of': 2 time(s) If you import this program (file program.py) elsewhere: import program         # importing program.py Only the code outside main() runs: The text‑analysis program is active So, a moderately complex text‑analysis utility achieves clear logic separation and context detection. When to Use main() and When Not To Use  main() (almost always appropriate) when: Medium/large scripts – significant code with non‑trivial logic, multiple functions/classes. Libraries or CLI utilities – you want parts of the module importable without side effects. Autotests – you need to test pure logic without extra boilerplate. You can skip main() when: Tiny one‑off scripts – trivial logic for a quick data tweak. Educational snippets – short examples illustrating a few syntax features. In short, if your Python program is a standalone utility or app with multiple processing stages, command‑line arguments, and external resources—introduce  main(). If it’s a small throw‑away script, omitting main() keeps things concise. Conclusion The  main() function in Python serves two critical purposes: Isolates the program’s core logic from the global namespace. Separates standalone‑execution logic from import logic. Thus, a Python file evolves from a straightforward script of sequential actions into a fully‑fledged program with an entry point, encapsulated logic, and the ability to detect its runtime environment.
14 July 2025 · 8 min to read

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