The legendary city-builder returns. Lead the creation of one of the greatest civilizations the world has ever seen in this 4K remake with modernized UI. Build monuments, manage your population through hundred of hours of gameplay and explore 4,000 years of history through more than 50 missions.
Stop Writing Messy Code! A Helpful Guide to Pylint
4/5 – (1 vote)
As a Python developer, you know how important it is to write high-quality, error-free code. But let’s face it – sometimes it’s hard to catch every little mistake, especially when working on large projects or collaborating with a team.
Pylint is like a trusty sidekick for your code, helping you spot errors, enforce good coding habits, and keep your code consistent and clean. It’s like having a second set of eyes to catch the little things that you might have missed.
But Pylint isn’t just for the big leagues – it’s a tool that can benefit developers of all levels.
In this article, we’ll dive into Pylint and cover everything you need to know to get started – from installation and configuration, to using Pylint in your favorite code editor, to tackling common errors and warnings that Pylint can help you catch.
So whether you’re a seasoned pro or a Python newbie, let’s dive in and see what Pylint can do for your code!
Installing and Configuring Pylint
Installing Pylint is a straightforward process, and there are several ways to do it.
One of the most popular methods is to use pip, Python’s package manager. To install Pylint using pip, simply open your command prompt or terminal and type:
pip install pylint
If you’re using a different package manager, you can consult their documentation for specific installation instructions.
Once you have Pylint installed, it’s important to configure it to match your project’s needs. Pylint has many configuration options that can be customized to fit your preferences and coding style. For example, you might want to change the maximum line length or enable/disable specific checks based on your project’s requirements.
To configure Pylint, you can create a configuration file in your project’s root directory. The default configuration file name is .pylintrc, but you can also specify a different file name or path if needed. The configuration file is written in INI format, and it contains various sections and options that control Pylint’s behavior.
This file sets the maximum line length to 120 characters and disables two specific checks related to missing docstrings and invalid variable names. You can customize the file to match your project’s requirements and coding style.
Keep in mind that Pylint also provides many command-line options that can override or supplement the configuration file settings. You can run pylint --help to see a list of available options.
With these steps, you should be able to install and configure Pylint to help you keep your code in top shape. In the next section, we’ll explore how to use Pylint within popular code editors like VSCode and PyCharm.
Pylint in Code Editors
When it comes to writing code, most developers prefer to use a code editor that can provide real-time feedback and make the coding process easier. Fortunately, Pylint can be integrated with many popular code editors, making it easier to use and providing real-time feedback.
Let’s take a look at how to set up Pylint in two of the most popular code editors, VSCode and PyCharm.
Open VSCode and install the Python extension if you haven’t already.
Open a Python project in VSCode.
Press Ctrl + Shift + P to open the Command Palette and type “Python: Select Linter”. Choose “Pylint” from the list.
You may be prompted to install Pylint if you haven’t already. If prompted, select “Install”.
You should now see Pylint output in the VSCode “Problems” panel. Pylint will automatically check your code as you type, and will show any errors or warnings in real-time.
Setting up Pylint in PyCharm
Open your Python project in PyCharm.
Go to Preferences > Tools > External Tools.
Click the + button to add a new external tool. Fill in the fields as follows:
Scroll down to the “Python” section and make sure that “Pylint” is checked.
Click OK to save your settings.
You should now see Pylint output in the PyCharm “Inspection Results” panel. Pylint will check your code as you type or run your project, and will show any errors or warnings in real-time.
By using Pylint in your code editor, you can quickly spot and fix issues in your code, making it easier to maintain high code quality. With Pylint checking your code in real-time, you can focus on writing great code without worrying about common mistakes. In the next section, we’ll compare Pylint with another popular Python linter, Flake8.
Pylint vs. Flake8
While Pylint is a powerful tool for analyzing Python code, it’s not the only linter out there. Another popular linter is Flake8, which, like Pylint, can help you identify errors, enforce coding standards, and keep your code consistent.
But what are the differences between these two tools, and which one should you use?
Comparing Pylint and Flake8
Pylint and Flake8 have several similarities, but they also have some key differences. Here are some of the most important differences to consider:
Scope: Pylint is a more comprehensive tool that can check for a wide range of issues, including potential bugs, coding style, and design patterns. Flake8, on the other hand, focuses primarily on coding style issues and is more lightweight.
Configuration: Pylint has many configuration options that can be customized to fit your coding style and preferences. Flake8, on the other hand, has fewer configuration options but is generally easier to set up and use out of the box.
Performance: Pylint can be slower than Flake8, especially on large projects. This is because Pylint analyzes code more thoroughly and performs more complex checks than Flake8.
Output: Pylint provides more detailed output than Flake8, including error codes, severity levels, and more. Flake8, on the other hand, provides simpler, more straightforward output.
Which One Should You Use?
Decision Framework: If you’re working on a large project and want a more comprehensive analysis of your code, Pylint might be the better choice. If you’re looking for a simpler, more lightweight tool that focuses on coding style issues, Flake8 might be a better fit.
In many cases, you can use both Pylint and Flake8 together to get the best of both worlds.
For example, you can use Pylint to perform a comprehensive analysis of your code and use Flake8 to focus on coding style issues. You can also use both tools in your code editor to get real-time feedback as you type.
In the next section, we’ll dive into some common errors and warnings that Pylint can help you catch in your code.
Common Pylint Errors and Warnings
As you use Pylint to analyze your Python code, you may encounter various errors and warnings that highlight potential issues or inconsistencies in your code. In this section, we’ll address some of the most common issues that Pylint may raise, and provide guidance on how to fix them and improve your code quality.
“Line too long” error
One of the most common errors that Pylint may raise is the “line too long” error, which occurs when a line of code exceeds the maximum line length specified in your Pylint configuration. By default, this limit is 80 characters, but you can change it to fit your preferences.
To fix this issue, you can break the line into multiple lines using line continuation characters, such as \ or ().
“Too many branches” and “too many statements” warnings
Another set of common warnings that Pylint may raise are the “too many branches” and “too many statements” warnings. These warnings are raised when a function or method has too many conditional branches or too many statements, respectively. They’re a sign that your code might be too complex and difficult to maintain.
To address these warnings, you can refactor your code to make it more modular and easier to understand.
For example, you can break down a long function into smaller functions, or use a switch statement instead of multiple if/else statements.
Here’s an example:
# Before
def complex_function(): if condition1: # do something elif condition2: # do something else elif condition3: # do something even different else: # do something completely different # After
def simpler_function(): if condition1: do_something() elif condition2: do_something_else() elif condition3: do_something_different() else: do_something_completely_different() def do_something(): # do something def do_something_else(): # do something else def do_something_different(): # do something even different def do_something_completely_different(): # do something completely different
By breaking down complex code into smaller, more manageable pieces, you can make your code easier to understand and maintain.
By addressing these common errors and warnings that Pylint may raise, you can improve the quality and readability of your code, making it easier to maintain and scale.
In the next section, we’ll wrap up the article and summarize the benefits of using Pylint in your Python projects.
Conclusion
In this article, we’ve explored Pylint, a powerful tool for analyzing Python code and improving code quality.
We started by discussing how to install and configure Pylint, highlighting the importance of customizing Pylint to match your project’s needs.
We then dove into how to use Pylint in popular code editors like VSCode and PyCharm, providing step-by-step instructions for setup and highlighting the benefits of using Pylint for real-time feedback.
Next, we compared Pylint with another popular linter, Flake8, and discussed the strengths and weaknesses of each tool.
Finally, we addressed some common errors and warnings that Pylint may raise, providing guidance and code examples on how to fix these issues and improve your code quality.
Pylint is a valuable tool that can help you maintain high code quality and avoid common mistakes. By using Pylint to analyze your code, you can catch errors and warnings before they become bigger issues, making it easier to maintain and scale your projects. With real-time feedback and customizable settings, Pylint is a great asset for developers of all levels and experience.
Whether you’re a seasoned Python developer or just starting out, we hope this article has provided you with valuable insights and practical tips on how to use Pylint effectively.
We are welcoming everyone to join our discord[discord.gg]. We are more active there in finding giveaways, small or large, and there are daily raffles you can participate.
Ten Dates is the sequel to the interactive rom-com, Five Dates, taking place in the post-pandemic world.
Misha, a millennial from London in search of that elusive in-person connection, tricks her best friend Ryan into going to a speed dating event with her. Each with their own five potential matches, Misha and Ryan must pluck up the courage and turn on the charm to date wildly different personalities.
Throughout the game, your choices and interactions will either strengthen or weaken your relationship with your date. Amidst a branching, multi-directional chain of conversation topics and deep-dive questions, Misha and Ryan are faced with ice-breaker games, awkward scenarios and unexpected truths. Will either find love?
Streamer Flips Sexist Joke Back At Harassers, Speedruns Making Sandwiches
Call of Duty streamer SteffyEvans often hears misogynistic jokes telling her to "get back in the kitchen" or "make me a sandwich." Instead of ignoring them, she decided to take it as a challenge, rushing to the kitchen to speedrun making a sandwich each time someone told her to--and getting back in time to still win the game.
The stream was recapped in a video on TikTok, showing another player telling her to "go make me a sandwich" almost as soon as she had loaded into a game. While the initial sandwich-making cost her team the round, she came back in the next one, and even won a future round after ducking off to make another two sandwiches.
Speaking to Kotaku, she explained that the sandwich-making challenge often cost her a round or two, but more often the comments were made after she had won. She also explained that she not just made the sandwiches, but packed them in bags with a side of chips and bottles of water so the full meals could be later donated to the homeless.
JavaOne is nearly upon us and we’ve been working hard to fill out the list of final activities. Here’s a high-level Run of Show to give you a sense for what you can expect from the first JavaOne in 5 years. Note that the JavaOne conference is co-located with CloudWorld, so I will mix in a few notable things like the CloudWorld keynotes and the Steve Miller Band. Rock on!
import os, openai
from flask import Flask, redirect, render_template, request, url_for app = Flask(__name__)
openai.api_key = os.getenv("OPENAI_API_KEY") @app.route("/", methods=("GET", "POST"))
def index(): if request.method == "POST": category = request.form["category"] number = request.form["number"] response = openai.Completion.create( model="text-davinci-003", prompt=generate_prompt(number, category), temperature=0.5, max_tokens=60, top_p=1, frequency_penalty=0, presence_penalty=0 ) return redirect(url_for("index", result=response.choices[0].text)) result = request.args.get("result") return render_template("index.html", result=result) def generate_prompt(number, category): return """Recommend the best {} {} movies to watch:""".format(number, category.capitalize() ) if __name__ == '__main__': app.run(host='127.0.0.1', port=5050, debug=True)
The imports I use are the following:
os – to work with the functionality dependent on the operating system
openai – to get the best of OpenAI artificial intelligence
flask – to have a nice-looking frontend framework for Python
Some Flask basics.
@app.route("/", methods=("GET", "POST"))
The first line there is implementing the main route (and the only one in this app). You can think of it as the main URL, whether it is localhost/ or www.mysite.com/.
The following function index() is taking information from the HTML form (see index.html) and preparing the data to be displayed back by the index.html site.
Here’s what happens the moment you hit the “Generate titles” button on your site:
index() function takes the input being “number” and “category” from the form,
feeds it into the generate_prompt() function,
which crafts and passes back a question to be asked,
which then – via the API – is sent to OpenAI to get a “response”,
that is then passed as “result” onto index.html to render on the screen.
Let’s also have a look at the index.html file.
<!DOCTYPE html>
<head> <title>OpenAI Movies</title> <link rel="shortcut icon" href="{{ url_for('static', filename='movie.png') }}" /> <link rel="stylesheet" href="{{ url_for('static', filename='main.css') }}" />
</head> <body> <img src="{{ url_for('static', filename='movie.png') }}" class="icon" /> <h3>Recommend the top movies</h3> <form action="/" method="post"> <input type="text" name="number" placeholder="How many proposals, e.g. 1,3,10" required /> <input type="text" name="category" placeholder="Enter the category e.g. thriller, comedy" required /> <input type="submit" value="Generate titles" /> </form> {% if result %} <div class="result"> <pre>{{ result | safe }}</pre> </div> {% endif %}
</body>
I will not go over the HTML tags as these should be familiar to you, and if not, you can get yourself up to speed with that using other web sources.
Initially, the file will feel like an ordinary HTML/CSS site, but after a while, you will notice a strange animal. It is placed here at the bottom of the file.
{% if result %} <div class="result"> {{ result }} </div> {% endif %}
This is where Python co-exists with HTML and allows the “result” that we generate in our “Python backend” to be passed over to the “Flask frontend”. If it exists, the flask engine will render the website with the results at the bottom.
Run the App Locally
Running an app is simple. I just run the index.py file.
With the “host” and “port” attributes specified in the index.py file, Flask will run a local web server with the site.
This is how it looks in my Visual Studio Code:
And this is the browser view:
Vercel deployment
Alright, the app is built and works fine on my local machine.
Now – let’s get it shipped to the world!
First, I put the whole project into my personal GitHub repo. I am using a public one just so that you and other readers can use it. Yet, if you follow my steps here, I would suggest a private one to you.
Warning: The risk with public repo is that it exposes your OpenAI secret key to the world! That would be identified anyways, and your key would rotate, but why bother?
Now, I log in to the Vercel dashboard and click on “Add New…” and select “Project”.
Selecting GitHub as Git provider.
Selecting the repository and importing it.
Arrived at the “You’re almost done.” page. There is no need to alter any of the default parameters there except adding one important variable. In environment variables, I add my own unique OPENAI_API_KEY.
Making sure this is indeed saved properly.
This is it. Hitting “Deploy” and watching the wheels spin.
Vercel builds it for me and assigns some public domains to the app.
Once I arrive at the final page, I open up the app, test it again and if all works ok, share with the family & friends & Finxter readers!
I’m sure many employers and clients would love to hire coders that can use the latest technological disruptions to build advanced applications (that are also easy to develop).
Recommended: 
Try It Yourself: 
Warning: The risk with public repo is that it exposes your OpenAI secret key to the world! That would be identified anyways, and your key would rotate, but why bother?
