Python

Python has a rich ecosystem of quality, mature tooling: linters, formatters, type checkers, etc. Each of these has decent performance, but what if the tooling was fast? Like, really fast – as in, instantaneous? This is the argument posed by Charlie Marsh when he introduced Ruff: a linter with the engine written in Rust. The performance numbers were incredible from the start, as was its reception by the Python community. Ruff is developing quickly – not just by filling in the details, but expanding beyond just linting. PyCharm is hosting Charlie for a special February 14th webinar. We

Sooner or later in your data science journey, you’ll hit a point where you need to get data from a database. However, making the leap from reading a locally-stored CSV file into pandas to connecting to and querying databases can be a daunting task. In the first of a series of blog posts, we’ll explore how to read data stored in a MySQL database into pandas, and look at some nice PyCharm features that make this task easier. Viewing the database contents In this tutorial, we’re going to read some data about airline delays and cancellations from a MySQL database into a pandas DataFrame.

If you’re involved in machine learning at all, you can’t have missed the plethora of groundbreaking models that have come out in past months. Two of the most hyped models are Whisper, OpenAI’s state-of-the-art speech recognition model, and Stable Diffusion, Stability AI’s groundbreaking image generation algorithm. In our upcoming webinar, Alaeddine Abdessalem, Software Developer at Jina AI, will show us how we can use both of these models to create an end-to-end multimodal application, capable of generating artwork from audio. Whisper allows extremely accurate voice-to-text transcription in

Making mistakes in your code is a pain, and debugging in Jupyter notebooks can get messy. While Jupyter helpfully displays the full Python traceback, which highlights the lines that have failed, working out exactly what caused the issue in your code can be tricky. I usually end up pulling my code apart in new cells in the same notebook, which creates a huge mess to clean up after discovering the problem. In this blog post, we’ll go over how you can instead use PyCharm’s debugger to track down problems with your code much more efficiently and cleanly. Our example problem We’ll work wi

This year saw the premier of the first ever PyCon Portugal! With DjangoCon Europe being hosted in Porto this year, the organizers took the opportunity to piggyback off of this conference and set up a PyCon in this beautiful country. I was lucky enough to be selected as one of the speakers, and it was great to be able to contribute to the success of the day in my own small way. Arrival I flew into Porto following Big Data LDN with an easy, direct flight from London. Facing equally long lines to grab a taxi or buy a tram ticket, I opted for the tram, which dropped me off just 15 minutes’ w

Join the webinar by Christopher Trudeau to get started with Django Ninja.

Read an interview with Adam Symington, author of the PythonMaps project, concerning geospatial data visualization and the Python tools used in it.

In this Flask tutorial you will learn how to create a simple Flask application in a few minutes. By providing simple Flask web app example we will demonstrate how to get started with a Flask project and run your Flask app in PyCharm.

Repeatedly running queries that span multiple tables can be burdensome on your database. In this blog post, we’re going to talk about materialized views, how and why they help us cut down on query costs, and how to integrate them into your Django application. Read on to learn more or check out this video overview. What is a materialized view Materialized views are usually used to increase the performance of query retrieval, as they are cached, precomputed datasets derived from queries. Generally, you can benefit most from using a materialized view if the query is run frequently and the d

The JetBrains full line code completion plugin for Python is now available as a public beta. We would like to talk about some of the technologies and algorithms used to create the plugin and share statistics about Python programming that we’ve collected in the process. What is “full line code completion?” You are probably already familiar with code completion, the kind that suggests the next word the user is typing. If you are not, we have covered it in a series of articles (one, two, three, four). Full line code completion extends the service by suggesting larger fragments of code.

For those developers, whose daily work benefits from the Django framework. As you probably know, the Django framework is built and maintained by community members. This might give you the impression that Django is effortlessly built by itself, but the truth is that the organization needs strong management and efficient communication. Making major Django releases every 8 months and bug fixes every month is no small task! That’s why the Django Fellowship program exists and why Django Software Foundation needs support from the community – both technical and financial. Moreover, the Django So