Spotify Music Data
This dataset consists of ~600 songs that were in the top songs of the year from 2010 to 2019 (as measured by Billboard). You can explore interesting song data pulled from Spotify such as the beats per minute, amount of spoken words, loudness, and energy of every song.
Not sure where to begin? Scroll to the bottom to find challenges!
I have analysed the Spotify Dataset to gain insights on the information provided by the data. I have calculated and displayed summary statistics to better understand the data and also created graphs to understand the trend of different categories in the data. The goal here was to develop an understanding of the data we are working with (organizing the raw data into information) so that we can then further decide the steps and processes to answer more complex questions and build predictive models etc. on the data. A better understanding of the fundamentals of our dataset will ensure we can extract information with maximum efficiency.
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
plt.style.use('ggplot')import pandas as pd
df = pd.read_csv("spotify_top_music.csv", index_col=0)df.head()df.info()df.describe()Data dictionary
| Variable | Explanation | |
|---|---|---|
| 0 | title | The title of the song |
| 1 | artist | The artist of the song |
| 2 | top genre | The genre of the song |
| 3 | year | The year the song was in the Billboard |
| 4 | bpm | Beats per minute: the tempo of the song |
| 5 | nrgy | The energy of the song: higher values mean more energetic (fast, loud) |
| 6 | dnce | The danceability of the song: higher values mean it's easier to dance to |
| 7 | dB | Decibel: the loudness of the song |
| 8 | live | Liveness: likeliness the song was recorded with a live audience |
| 9 | val | Valence: higher values mean a more positive sound (happy, cheerful) |
| 10 | dur | The duration of the song |
| 11 | acous | The acousticness of the song: likeliness the song is acoustic |
| 12 | spch | Speechines: higher values mean more spoken words |
| 13 | pop | Popularity: higher values mean more popular |
Source of dataset.
pop_artists = df[df['pop'] > 76][['artist','top genre','pop']]
pop_artists.sort_values(['pop'],ascending=False)cols = list(df.columns[df.dtypes == int])
colsyearly = df[cols].groupby('year').agg('mean')
yearlyplt.plot(yearly.index,yearly['bpm'], marker = 'o', mec = 'black',mfc = 'blue',c='black')
plt.title('Tempo by Year')
plt.ylabel('BPM (Beats Per Minute)')
plt.xlabel('Year')
plt.show()plt.plot(yearly.index,yearly['nrgy'], marker = 'o', mec = 'black',mfc = 'blue',c='black')
plt.title('Energy by Year')
plt.xlabel('Year')
plt.ylabel('NRGY')
plt.show()plt.plot(yearly.index,yearly['dnce'], marker = 'o', mec = 'black',mfc = 'blue',c='black')
plt.title('Danceability by Year')
plt.xlabel('Year')
plt.ylabel('DNCE')
plt.show()plt.plot(yearly.index,yearly['dB'], marker = 'o', mec = 'black',mfc = 'blue',c='black')
plt.title('Loudness by Year')
plt.xlabel('Year')
plt.ylabel('Loudness (dB)')
plt.show()