#Spotify music bar install#
#Spotify music bar code#
#Spotify music bar Offline#
#Spotify music bar series#

Which also opens the Jupyter web interface in a new tab in your browser. Once everything was installed, I went into a new directory created for the project and started the Jupyter notebook server with: $ jupyter notebook In this investigation, I did not really use much pandas, nor matplotlib apart from a tiny diversion.ĭon’t be discouraged by them appearing to be of little value, they shine when the data handling gets more challenging.

#Spotify music bar install#

Here’s how you’d install it all: $ pip install pandas matplotlib requests furl matplotlib - To give pandas plotting-superpowers.pandas - Makes python feel a bit like R, namely able to work with data frames and crunch data without getting too verbose about it.furl - For creating urls and requests in general with parameters for GET / POST stuff.requests - A great library for talking to web apis and fetching single web pages.Here is my choice of tools for all of the above: Jupyter is a python module, and can be installed using pip: $ pip install jupyterįor data-tastic Python fun, I usually install a few modules by default, to make sure I can do basic data crunching, plotting and http requests without much effort.

#Spotify music bar code#

In the following, code blocks starting with $ is what happens in a terminal, while everything else is Python code. It’s really convenient, if you don’t know it and like Python, check it out! So I went ahead and created a new virtual environment using virtualenv wrapper. This makes it easy to isolate dependencies, installĮXACTLY what is needed in the version which is needed and make sure that the code runs out of the box. With Python projects, I like setting up a virtual environments for each one. It’s the perfect tool for working with Python and data when you’re trying stuff out and playing with an early-stage data project. By now, jupyter notebooks are what you should use. Then I got introduced to ipython notebooks and never looked back. When tinkering with data years ago, I used to rely on running tiny Python scripts and saving intermediate results. Here’s what I usually do when starting an exploratory data project with Python. That said, it works, does what it’s supposed to do given the context and priorities of and probably looks very similar to many real-world projects, where results and time constraints are significant factors.Īlright! Let’s jump into it! The Setup Step by Step The samples are not as polished as other specimen you will find elsewhere, and might not be the best learning resource regarding best practices.

I wrote it to satisfy my own curiosity, and with one person in mind who would ever see it: yours truly. The code below was never meant to be reliable or presentable. There are further possible stuff one could do with the data, for example to handcraft an own simple artist/album recommendation script, but that’s for anonther time.

Do something with the information (aka cleanup).

Get data for each album referenced (total number of songs, how many of those are in my library).

Get data on all songs in my library (especially which album they belong to).

Obvious mis-saves, but also to see what stuff I really seem to like. Having the amazing data crunching powers of the forementioned TOKEN, I thought it’d be cool to take a look at what albums I have in my library. This leads to song playing which are not as enjoyable without the album context. You can get around that with playlists, but that’s a few more clicks.Īs I’m in the habit of giving complete albums a listen every once in a while, and forgetting about the stuff I download-saved, my music library is now a bit messy.

Doing so also “saves” them, thus adding the complete album to your music library.

#Spotify music bar Offline#

To listen to songs in offline mode, you can “download” them on a particular device. I’m not sure the library is meant for such use - it’s a bit in the way of when songs are “saved”. What I like to do, is just put all of my “saved” songs, and put the on shuffle play.

My music library on Spotify is quite big. Among others, it’s good for everything needed to analyze the heck out of your whole music library - information about songs and albums in particular. While playing around with the Spotify web API, and building a login flow in the app, it was pretty easy to get an access token for my account with all kinds of permissions for access to my data. The app is written in Python and Flask for displaying full-sized album art for the songs which are playing on a user’s Spotify account.

#Spotify music bar series#

Recently, I have been developing a web app which uses the Spotify API in part for a series of posts about developing a small real-life application and web development in general. Analyzing my Spotify Music Library With Jupyter And a Bit of PandasĪre you a Spotify user? By now, that’s the way I consume most of my music.