2022: Week 26 - Making Spotify Data Spotless

Challenge By: Algirdas Grajauskas

It's always great when a Data Schooler in training comes up with an idea for a Preppin' Data Challenge! Al is part of DS31 and has been in training for 3 months now. For this challenge we're going to clean up and analyse his Spotify data. 

Input

Unclean Spotify data – from Spotify 

Requirements

  • Input the data
  • Create a new field which would break down milliseconds into seconds and minutes
    • e.g. 208,168 turned into minutes would be 3.47min
  • Extract the year from the timestamp field
  • Rank the artists by total minutes played overall
  • For each year, find the ranking of the artists by total minutes played
  • Reshape the data so we can compare how artist position changes year to year
  • Filter to the overall top 100 artists
  • Output the data

Output


  • 10 fields
    • Overall Rank
    • Artist Name
    • 2015
    • 2016
    • 2017
    • 2018
    • 2019
    • 2020
    • 2021
    • 2022
  • 100 rows (101 including headers)
You can download the full output here. 

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

Popular posts from this blog

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will use dif
Read more

2023: Week 2 - International Bank Account Numbers

Image
Challenge By: Jenny Martin For week 2 of our beginner month, Data Source Bank has a requirement to construct International Bank Account Numbers (IBANs), even for Transactions taking place in the UK. We have all the information in separate fields, we just need to put it altogether in the following order: Inputs A list of the transactions, with information about the receiving bank account  A lookup table for the SWIFT Bank Codes  Requirements Input the data In the Transactions table, there is a Sort Code field which contains dashes. We need to remove these so just have a 6 digit string ( hint ) Use the SWIFT Bank Code lookup table to bring in additional information about the SWIFT code and Check Digits of the receiving bank account ( hint ) Add a field for the Country Code ( hint ) Hint: all these transactions take place in the UK so the Country Code should be GB Create the IBAN as above ( hint ) Hint: watch out for trying to combine sting fields with numeric fields - check data types Re
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in
Read more