Such a dataset is a Pythonista's delight! It means it not bad, and our conclusions will be valid. There are just 71 missing values in a dataset consisting of over 500k rows. If we only used it once, we'd get a sum of missing values for each column. Calling sum() twice on this gives us a total number of all the missing values in the dataset. Pandas pd.isnull()returns a dataset with booleans True and False saying if the value is missing. To be sure if we can trust this dataset, it's important to check if any values are missing. You'll see that this dataset consists of 122860 rows and 20 columns. # Loading the datsetĭf_tracks = pd.read_csv('/content/drive/MyDrive/tracks.csv') Today we'll use tracks and artists' datasets. The Spotify dataset is quite huge and there are several files containing slightly different data. Today we'll only start with the Pandas library. Who is currently most popular and what genres do they represent?įor a detailed explanation of the dataset: Ĭheck us out on Twitter for more such cool articles Follow Pre-processing the data.What is the correlation between tracks' different features?.How long should an average track last according to today's standards?.Is there a correlation between popularity and a track's traits?.Which tracks released in March 2020 gained popularity among over 80.How many tracks gained popularity over 90 out of 100.What tracks are most popular amongst Spotify users?.Yet, apart from your ingenious ability, you need to be familiar with facts. You believe you have a skill for spotting great talent and promoting them to become stars. Imagine wanting to get started in the music industry. Today, we're going to look at Spotify dataset from a perspective of a recording studio start-up.ītw if you are looking for roles in AI do check out In the past two posts within our Pandas series, we analyzed data from Chipotle restaurant and Flipkart online store.

Spotify careers data science