Plotly and seaborn are great options to visualize data in your notebook, as well as for interactive plots to be used in presentations. I personally prefer the HDF format (fueled by the tables package), as it is very tolerant about the data types in your data frames and as it is also amazingly quick when loading Gigabytes of data. While pandas is the most-used library for data analysis in Python, fastparquet and pyarrow are packages that will allow you to persist your raw or processed data to disk into compressed formats which can be reloaded into memory very fast.
These will be installed using the now available pip3 command: pip3 install Working with Notebooks in JupyterLab doesn’t make any sense without some prominent libraries from the Data Science community. Popular packages for Data Science purposes As not all flavors of Ubuntu out there are always fully equipped with all kinds of tools, I added curl to the list of packages to be installed. 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.
You can use Pip to upgrade the pip package by issuing the command below: pip install -upgrade pip To install Pip for Python version 3, issue the command below to install pip with its dependencies: sudo apt install python3-pip Issue the command below to install pip with its dependencies for Python2: sudo apt install python-pipĪfter installing Pip in the above step, issue the command to verify our installation by checking the version installed: pip -version
In cases where the project you are working on is only compatible with Python version 2, you can use the following steps to install Pip. Issue the command below: sudo apt-get update & sudo apt-get upgrade It is recommended that you update the system to the latest packages before beginning any major installations. Once you have signed up, log into your Cloudwafer Client Area and deploy your Cloudwafer cloud server. Take a moment to create an account after which you can quickly deploy your cloud servers. If you have not already registered with Cloudwafer, you should begin by getting signed up. In this guide, we will install the latest version of Pip on Debian 9. You can use pip to install packages from the Python Package Index and other indexes.