The PyCharm Blog

The Python IDE for Professional Developers

Tutorials

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

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

In his new tutorial, Mukul Mantosh gives very detailed instructions on how to develop a Django Application using AWS. If you have never used AWS to run your code, with this new tutorial you can give it a try and build your confidence. This tutorial is divided into distinct parts, so if you are only interested in a specific topic you can skip all the others. It also contains text in addition to the video content, so you can use it in any circumstances and in the most comfortable way for you. READ THE TUTORIAL Let’s take a short look at what is in the tutorial. Mukul explains the Django

Lets-Plot interactive visualizations in PyCharm and Datalore

The Lets-Plot library is an open-sourced interactive plotting library developed by JetBrains for Python and Kotlin. Its architecture was inspired by the ggplot library for the R language, and is built with layered graphic principles in mind. But what sets Lets-Plot apart from the well-known Matplotlib and Seaborn Python libraries? With Lets-Plot you can produce interactive visualizations, and do it with just a few lines of code. The Lets-Plot library can be easily configured in SciView in PyCharm Professional edition, and comes already pre-installed in Datalore, the online Jupyter notebo

PyCharm started its life with Django support and our new release adds some useful improvements for Django coding. Let's take a look at support for custom Django tags in PyCharm Professional 2020.1. But First, One More Thing Maybe it's bragging, but we need to show one existing feature related to this topic. When you're in some general Python code and you need something imported, PyCharm will give you visual cue -- a warning -- as advanced notice: To fix it, Alt-Enter can do it for you. No need to stop what you're doing, go to the top, type in the import, then find your way back: But what abo

Hi there! Have you tried Jupyter Notebooks integration in PyCharm 2019.2? Not yet? Then let me show you what it looks like! In this blog post, we're going to explore some data using PyCharm and its Jupyter Notebook integration. First, of course, we'll need said data. Whenever I need a new dataset to play with, I typically head to Kaggle where I'm sure to find something interesting to toy with. This time a dataset called "Pizza Restaurants and the Pizza They Sell" caught my attention. Who doesn't love pizza? Let's analyze these pizza restaurants and try to learn a thing or two from it

React is obviously super-hot, TypeScript has recently become super-hot, and the combination is attracting increasing interest. Combined with Jest for test-driven development (TDD)…great developer experience. Want to see what our IDE has to offer for fullstack Python projects which use React frontends? We’re proud to release a long, LONG “under construction” tutorial series: React, TypeScript, and TDD. Videos, code, write-ups with screenshots, organized into 12 parts. And along the way, a formal unveiling of the PyCharm Guide, a compendium of resources for becoming a PyCharm badass. The Tutoria

Last week we published a blog post on the new pytest fixture support in PyCharm 2018.2. This feature was a big part of the 2018.2 work and proved to be a hit. But it wasn’t the only notable pytest work: 2018.2 also delivered support for behavior testing with pytest-bdd, a project that provides behavior testing with pytest as the test framework. Our What’s New in 2018.2 video gave a quick glimpse of this support. Let’s rewrite the tutorial from last week, showing the pytest-bdd support instead of simply working with fixtures. Behavior-driven-development (BDD) support is a part of PyCharm Profes

Python has long had a culture of testing and pytest has emerged as the clear favorite for testing frameworks. PyCharm has long had very good “visual testing” features, including good support for pytest. One place we were weak: pytest “fixtures”, a wonderful feature that streamlines test setup. PyCharm 2018.2 put a lot of work and emphasis towards making pytest fixtures a pleasure to work with, as shown in the What’s New video. This tutorial walks you through the pytest fixture support added to PyCharm 2018.2. Except for “visual coverage”, PyCharm Community and Professional Editions share all t

Python 3.7 is set to be released this summer, let’s have a sneak peek at some of the new features! If you’d like to play along at home with PyCharm, make sure you get PyCharm 2018.1 (or later if you’re reading this from the future). There are many new things in Python 3.7: various character set improvements, postponed evaluation of annotations, and more. One of the most exciting new features is support for the dataclass decorator. What is a Data Class? Most Python developers will have written many classes which looks like: class MyClass: def __init__(self, var_a, var_b): self.var_

You can use code cells to divide a Python script into chunks that you can individually execute, maintaining the state between them. This means you can re-run only the part of the script you’re developing right now, without having to wait for reloading your data. Code cells were added to PyCharm 2018.1 Professional Edition’s scientific mode. To try this out, let’s have a look at the raw data from the Python Developer Survey 2017 that was jointly conducted by JetBrains and the Python Software Foundation. To start, let’s create a scientific project. After opening PyCharm Professional Editi

The PyCharm Blog

Tutorials

How to Debug a Jupyter Notebook in PyCharm

Flask tutorial: Create a Flask application in PyCharm

How to use Materialized Views in Django

New Tutorial: Develop a Django App Using AWS

Interactive Visualizations in PyCharm and Datalore

Django Custom Tags in PyCharm Professional 2020.1

Jupyter, PyCharm and Pizza

Deep-dive tutorial series on React, TypeScript, and TDD

PyCharm and pytest-bdd

PyCharm 2018.2 and pytest Fixtures

Python 3.7: Introducing Data Classes

PyCharm Scientific Mode with Code Cells