Python for Scientists – Disaster Recovery

Disasters happen: coffee spills on a laptop, electronics are dropped, hard disks fail or become corrupt. The combination of Conda environments and version control provides a near-bullet-proof disaster mitigation strategy. We will also discuss disaster recovery for when disasters happen. Topics covered in this post: Disaster MitigationDisaster Recovery Click here for a list of related... Continue Reading →

The Best IDEs for your Python Projects

What program do you use for writing code? A simple text editor? Or maybe one of many integrated development environments (IDEs)? There are TONS of options! So many that you can waste at least an entire day (or more) researching the best one. A web search will turn up many websites with lists of the... Continue Reading →

Python for Scientists – Environments for multiple Operating Systems

Do you want a Conda environment that can be shared between different operating systems? This can come in handy if your project does not contain OS-specific Python packages. Examples where sharing an environment across multiple Operating Systems might be handy are workshops or tutorials. Read on to learn a new way to create and share environments!

Python for Scientists – Version Control

Do you have 15+ versions of your manuscript/thesis/report? Maybe they have names such as report.doc, report_pd_edits.doc, report_final.doc, report_final_v3.doc, etc.? At some point there are so many files you have to filter by modification date to know which one you were working on last. Or maybe you accidentally saved over a document and lost a week... Continue Reading →

Python for Scientists – Environments

Why make environments? Why can't I just install everything into the root, or base, python environment and go happily on my way? Because sooner or later this is going to happen.

You find a new feature in the latest release for a library that finally allows you to simplify your code. This is great! You quickly type the command to update the package in your base environment. The installation is going fine until you get a message that installation failed and Conda will try to roll back changes. The roll back seems to work fine and you decide to live without the new feature. Then you try to run your script again and it fails due to a library conflict. What!? My script worked just a few minutes ago! After searching online for an hour for a fix, you decide to delete Miniconda3 and reinstall everything. You try to remember all of the libraries that were used, but other scripts from different projects rely on different libraries. Oh no. You're in for a terrible, horrible, no good, very bad day(s).

Python for Scientists – Installation for Linux

If used properly, Anaconda can take most of the annoyances out of managing Python libraries and virtual environments. Even if corruption occurs (yes, it will happen), this series will equip you with means to replicate your working environments. Before we get to disaster recovery, we need a solid base installation. Remember, this post covers installation... Continue Reading →

Python for Scientists – Installation for Windows

If used properly, Anaconda can take most of the annoyances out of managing Python libraries and virtual environments. Even if corruption occurs (yes, it will happen), this series will equip you with means to replicate your working environments. Before we get to disaster recovery, we need a solid base installation. Remember, this post covers installation... Continue Reading →

Python for Scientists – Introduction

I'm going out on a limb here and guessing you're either new to programming, or you're self taught and looking for ways to improve. New graduate students are typically met with the cold reality that they need to quickly learn how to program to keep up with their course work and/or research. They search for... Continue Reading →

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