Using Jupyter Notebooks With WSL 2 in DataSpell
Learn more about using Windows Subsystem for Linux with Jupyter Notebooks, including how to run Python code from Windows using Linux.
Webinar Recording: “Is Your Analysis Reproducible? 5 Ways to Make Your Work Bulletproof With Datalore”
On Thursday, 23rd June 2022, I gave a webinar about how to build reproducibility into your work using Datalore, our cloud-based data science and data analytics platform. Here’s the recording for you to watch if you missed the live stream. https://www.youtube.com/watch?v=MIctg07feIc Description Have you ever had the experience of opening up an old analysis you did in Jupyter and being completely unable to reproduce the results? Maybe you can't work out where you saved the data you used, or what version of a core dependency you had in your environment. Perhaps your Jupyter notebook is
Webinar: “Idiomatic Pandas: 5 tips for better Pandas”, With Matt Harrison and Jodie Burchell
I’ve been a long-time Pandas user, relying on it heavily since the start of my data science career. However, up until the last couple of years, I struggled with certain issues, such as not being able to work with very large DataFrames or efficiently run heavy data processing tasks. I’d also often find my Jupyter notebooks cluttered with intermediate DataFrames after applying transformations, making it harder to read the code and keep my notebook tidy. For a long time, I had thought that these issues were just endemic to Pandas and accepted there wasn’t a better way; however, there is! If yo