SpotifyDataAnalytics

--For this project, I downloaded Spotify data from Kaggle.
-- Then I created a table to insert Spotify data into.
-- Finally, I performed analytics on the data using SQL. 

--Creating the table: 
CREATE TABLE BIT_DB.Spotifydata (
id integer PRIMARY KEY,
artist_name varchar NOT NULL,
track_name varchar NOT NULL,
track_id varchar NOT NULL,
popularity integer NOT NULL,
danceability decimal(4,3) NOT NULL,
energy decimal(4,3) NOT NULL,
key integer NOT NULL,
loudness decimal(5,3) NOT NULL,
mode integer NOT NULL,
speechiness decimal(5,4) NOT NULL,
acousticness decimal(6,5) NOT NULL,
instrumentalness text NOT NULL,
liveness decimal(5,4) NOT NULL,
valence decimal(4,3) NOT NULL,
tempo decimal(6,3) NOT NULL,
duration_ms integer NOT NULL,
time_signature integer NOT NULL 
)

--Then I inserted the Spotify Data .csv into the table.

--Next, I explored the data using the following SQL. 

--First, I determined who the top 10 artists were by popularity.
SELECT track_name, 
artist_name, 
popularity
FROM BIT_DB.spotifydata
ORDER BY popularity desc
LIMIT 10;

--Next, I searched for songs that artists wrote in the Major mode, which is indicated by the integer 1 on the dataset.
SELECT artist_name, 
track_name, 
mode
FROM BIT_DB.spotifydata
WHERE mode = 1;

--Then, I searched for songs who were not written in the common time signature of 4, as well as their popularity.
SELECT artist_name, 
track_name,
popularity,
time_signature
FROM BIT_DB.Spotifydata
WHERE time_signature < 4;