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Plus when to use Miniconda, Anaconda, conda-forge, and pip for a conda good time 😁

Python is the most popular language for data scientists. 🐍 Conda is the most common tool to create a virtual environment and manage packages for data scientists using Python.

Unfortunately, figuring out the best way to get conda on your machine and when to install packages from various channels isn’t straightforward. And it’s not easy to find the most useful commands for using conda and pip all in one place. ☹️

In this article I’m going to provide the essential conda commands and suggestions to help you avoid headaches with installation and use. 🎉

mt. st. helen’s volcano
mt. st. helen’s volcano
Sometimes Python virtual environments and packages feel like a volcano. Source: pixabay.com

Let’s get to it! 🚀

The Need

Whether working locally or on a server in the cloud, you want a virtual environment to isolate your Python version and packages so that you…


Common keyboard shortcuts for notebooks

JupyterLab is awesome. It has almost everything a data scientist could want.

  • Tabbed windows ✅
  • Split windows ✅
  • Jupyter notebooks ✅
  • Filebrowser ✅
  • Markdown file previews ✅
  • Helpful extensions ✅
  • Widget capabilities ✅
  • Edit .csv files ✅
  • Terminal windows ✅
  • Python scripts ✅
  • Export notebooks in many formats ✅
  • Helpful tutorials ✅

I’ve found it to be missing just one thing — a list of keyboard shortcuts. ☹️

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Image for post
Source: pixabay.com

With keyboard shortcuts, you can whiz around Jupyter notebooks in JupyterLab. You can save time, reduce wrist fatigue from using your mouse, and impress your friends. 🙂

Below is the missing list of common JupyterLab keyboard shortcuts from a GitHub Gist I made. …


Tips and libraries to speed up your Python code

Dealing with big data can be tricky. No one likes out of memory errors. ☹️ No one likes waiting for code to run. ⏳ No one likes leaving Python. 🐍

Don’t despair! In this article I’ll provide tips and introduce up and coming libraries to help you efficiently deal with big data. I’ll also point you toward solutions for code that won’t fit into memory. And all while staying in Python. 👍

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Let’s get to the other side of the bridge! Source: pixabay.com

Python is the most popular language for scientific and numerical computing. Pandas is the most popular for cleaning code and exploratory data analysis.

Using pandas with Python allows you to handle much more data than you could with Microsoft Excel or Google Sheets. …

About

Jeff Hale

I write about data science. Join my Data Awesome mailing list for great content: https://dataawesome.com

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