How-To's

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.

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