Because of this, do not forget to use sudo. Running installations with apt on Ubuntu and Debian requires root permissions. Python 3Īt first, we need to install a set of packages on the operating system level to enable the usage of Python 3. The commands shown here have been tested on a Ubuntu 18.04 Vagrant box, but work on any other Ubuntu system as well. An article from served as a guideline to this setup process. On the other hand, you really need to know which packages you need to install on Ubuntu and for your local Python 3 installation. One immediate observation I made was a much quicker download and installation process – it felt faster by magnitudes. I took the easiest route on the map and installed all my packages with the Python 3 Package Manager pip, more specifically pip3. However, after trying very hard for several hours, I chose to take another approach. In fact Anaconda has the ability to not only install Python packages, but also non-Python packages, which are required for many Python packages to work properly. I found that it has rarely been true that a widely used software was buggy, when i had my issues with it – more often the bug was sitting in front of my computer. I’ll be honest: Probably I’m simply to stupid to use Anaconda. Trying to update it as documented gave me error messages for which my intensive online research didn’t deliver any results to solve the problem. conda warned me about being out of date (although I installed the latest version available from the Anaconda website).I had hoped that Anaconda took automatically care of this matter □ Importing pandas in a notebook gave me complaints about old versions of numpy.Python virtual environments are still some kind of a book of seven seals for me. Packages I like to use in notebooks (such as plotly for data visualization) ended up in other virtual environments than the actual jupyter package, although I never specified a particular environment name.However, I couldn’t get JupyterLab installed with Anaconda this time and wasted several hours scratching my head. In the past, I had good experiences installing Jupyter Notebook using Anaconda. As recommended in the official docs, I went with Anaconda to install the various required Python packages, as I didn’t want to drown in a hot lava sea down in the dependency hell. Until that point it was all fun and games. Tell me, where does it hurt?įinally, I wanted to make the switch and tried to install JupyterLab 1.0.4 based on a Vagrant box ( ubuntu/bionic64). Although the Notebooks app works great, they built a new and more powerful and extensible front end: JupyterLab. Project Jupyter created an indispensable tool for every Data Scientist that finally made exploring and visualizing data a fun thing to do: Jupyter Notebooks.
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