My Discogs Collection - Analytics

For every vinyl and CD I purchased, I recorded them on Discogs with the price I paid and if it was autographed. The data has interesting information such as the value of the records, price, genre (I manually entered them), record label, and year it was released.

Table of Contents

1. Project Motivation
2. Installation
3. File Descriptions
4. Results
5. Resources
6. Licensing, Authors, and Acknowledgements

Project Motivation

I want to understand my record collection better by looking at the value, cost, and genres I am into. I also want to improve my Python skills and perhaps build a package/functions to analyze Discogs data efficiently.

Installation

Virtual environment

Add the following to your .bash_profile to source virtualenv correctly:

export WORKON_HOME=$HOME/.virtualenvs
export VIRTUALENVWRAPPER_PYTHON=/usr/local/bin/python3
export VIRTUALENVWRAPPER_VIRTUALENV=/usr/local/bin/virtualenv
source /usr/local/bin/virtualenvwrapper.sh

Create a virtual environment:

mkvirtualenv discogs --no-site-packages
workon discogs
add2virtualenv .

Python packages:

pip3 install -r requirements.txt

Jupyter notebook (if needed):

python3 -m ipykernel install --user --name discogs --display-name "discogs"

File Descriptions

*There is one notebook called discogs_analysis.ipynb that runs the exploratory data analysis.

Results

Resources

Building structured multi-plot grids

Visualizing linear relationships

Visualizing the distribution of a dataset

Plotting with categorical data

Licensing, Authors, Acknowledgements

Inspiration on EDA

Exploratory Data Analysis: Iris Flower Dataset

A Starter Pack to Exploratory Data Analysis with Python, pandas, seaborn, and scikit-learn

Future links to explore

discogs-xml2db

autoEDA-resources

Discogs Developer

Download Discogs Data

Discogs Database Search

Discogs Oauth

Feel free to use my notebook and explore my analysis!

Authors

Hasib Neaz - Initial work - hneaz