I will describe how I did this in another blog post, but for now, I want to show how easy it is to connect to this database within DataSpell and then view the table contents.īy clicking on the “Database” tab to the right of the IDE, and then clicking on “New”, I was able to easily create a connection to my database just by specifying the database name, user and password (JetBrains has instructions on how to connect to a range of databases here). I then loaded my data into a table in this database using pandas, sqlalchemy and psycopg2. I really like DataGrip, so the introduction of some of the features of DataGrip into DataSpell is a very welcome addition for me.įor this project, I created a local PostgreSQL database in a Docker container.
I wanted to first show a really cool feature of DataSpell, which is its ability to support connections to databases. Obviously, the first step is to access our data. In this analysis, we’ll be reading in the data, doing some simple feature engineering, checking the model accuracy using cross-validation, and exploring a decision tree visualisation package called pybaobab which makes interpreting decision trees easier. To explore what DataSpell can do, I’ll be creating a simple decision tree model using the Heart Failure Prediction DataSet, which was kindly uploaded to Kaggle by Federico Soriano Palacios. I should note that DataSpell also supports R natively, but in this post I’ll be focusing on it’s capabilities with a Python project. In addition, there are a number of other nice features that make doing a data science project that much nicer, which I’ll show in this blog post.
Pycharm vs jupyterlab code#
This IDE aims to take those features from P圜harm that we all know and love, such as smart code completion and dependency management, while at the same time making notebooks first-class citizens. I was therefore very excited to hear that JetBrains has released a new IDE called DataSpell specifically designed to support data science work. This is particularly due to the fact that the flow of a data science project often iterates between the research and development phases.
However, moving between two different IDEs creates a lot of friction when doing data science work, as it involves task switching and introduces needless distractions. Over the years, I’ve tried working with just notebooks, experimented with Spyder and Rodeo, and have settled on a combination of JupyterLab for my research workflow and P圜harm when I want to write code intended for production. When I was primarily working with R, RStudio was a very nice environment to work with, but when I moved to working in Python I hadn’t been able to find anything close. During my years of working as a data scientist, I’ve tried quite a number of IDEs.
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