Plotting your Spotify data

Inspired by Nerudista's post in the Tacos de Datos website (in spanish) I asked Spotify for my data and started making some plots with them. If you want to do the same, head over to this page; once you request it, it will take a couple of days to be available. In the meantime, you can use this Google Colab, I've created a subset of my data for you to play with.

Among all the information you will receive, there will be some files named following this pattern: StreamingHistoryXX.json, and these are the ones we'll use throughout this post.

The data

The files mentioned above files contain something like this:

[
  {
    "endTime" : "2019-02-04 17:14",
    "artistName" : "MGMT",
    "trackName" : "Time to Pretend",
    "msPlayed" : 261000
  },
  {
    "endTime" : "2019-02-04 17:18",
    "artistName" : "MGMT",
"..." 

Where the values are:

• endTime: Day and time in which a song finished, in UTC format.
• artistName: Name of the artist of the song.
• trackName: Name of the song.
• msPlayed: For how long (in milliseconds) a song was played.

To load these this data into a DataFrame we'll need this tiny function:

from glob import glob
import json
import pandas as pd

def read_history():
    history = []
    for file in sorted(glob("StreamingHistory*.json")):
        with open(file) as readable:
            history.extend(json.load(readable))
    history = pd.DataFrame(history)
    history["endTime"] = pd.to_datetime(history["endTime"])
    return history

streaming_history = read_history()
streaming_history.head(5)

Which should give us something like this once we execute it:

Histogram.

I've always been a fan of the way GitHub shows developer contributions, and the data we got from Spotify seems like the perfect candidate to create something similar; however, we need to perform some transformations first.

Since we are not interested in the time each song finished we'll get rid of the temporal part of endTime:

streaming_history["date"] = streaming_history["endTime"].dt.floor('d')

Then we'll get a count of songs per day using groupby:

by_date = streaming_history.groupby("date")[["trackName"]].count()
by_date = by_date.sort_index()

For out plot, we'll need to know which day of the week each date refers to, we can use the properties week and weekday:

by_date["weekday"] = by_date.index.weekday
by_date["week"] = by_date.index.week

By the end, our dataframe should look like this:

We have almost everything we need, the next step is to get a contiuous sequence of numbers for each week. In the dataframe above the 6th week of 2016 must be the week 0, the 7th must be the week 1... I can't think of a better way to do it than a for loop:

week = 0
prev_week = by_date.iloc[0]["week"]
continuous_week = np.zeros(len(by_date)).astype(int)
sunday_dates = []
for i, (_, row) in enumerate(by_date.iterrows()):
    if row["week"] != prev_week:
        week += 1
        prev_week = row["week"]
    continuous_week[i] = week
by_date["continuous_week"] = continuous_week 
by_date.head()

Our next step is to generate a matrix with days ✕ weeks as dimensions, where each one of the entries will be the number of songs we listened to in that day and week:

songs = np.full((7, continuous_week.max()+1), np.nan)

for index, row in by_date.iterrows():
    songs[row["weekday"]][row["continuous_week"]] = row["trackName"]

Now we could just plot the matrix songs using seaborn:

fig = plt.figure(figsize=(20,5))
ax = plt.subplot()
mask = np.isnan(songs)
sns.heatmap(songs, ax = ax)

But the result is not that great:

If we want it to look better we still need some code, the first thing to do is to clean the axis labels:

min_date = streaming_history["endTime"].min()
first_monday = min_date - timedelta(min_date.weekday())
mons = [first_monday + timedelta(weeks=wk) for wk in range(continuous_week.max())]
x_labels = [calendar.month_abbr[mons[0].month]]
x_labels.extend([
    calendar.month_abbr[mons[i].month] if mons[i-1].month != mons[i].month else "" 
    for i in range(1, len(mons))])

y_labels = ["Mon", "", "Wed", "", "Fri", "", "Sun"]

The X axis labels are far more complicated than the Y ones; this is because unlike Y, the X axis is not fixed nor continuous, as such, they need to calculated based on the data. If you want a more detailed explanation, tell me in the comments or at me on twitter at @feregri_no).

After doing this we'll perform some modifications with colours and the axis:

fig = plt.figure(figsize=(20,5))
ax = plt.subplot()

ax.set_title("My year on Spotify", fontsize=20,pad=40)
ax.xaxis.tick_top()
ax.tick_params(axis='both', which='both',length=0)
ax.set_facecolor("#ebedf0") 
fig.patch.set_facecolor('white')

And finally, we can use seaborn's heatmap again, this time with a few extr arguments that I will explain later:

sns.heatmap(songs, linewidths=2, linecolor='white', square=True,
            mask=np.isnan(songs), cmap="Greens",
            vmin=0, vmax=100, cbar=False, ax=ax)

ax.set_yticklabels(y_labels, rotation=0)
ax.set_xticklabels(x_labels, ha="left")

The arguments are as follows:

• songs: our matrix with shape days ✕ weeks with the counts of songs per day,
• linewidths: the size of the spacing between each patch,
• linecolor: the colour of the spacing between each patch,
• square: this tells the function that we want to keep the aspect ratio 1:1 for each patch,
• mask: a very interesting argument, it will help us "mask" the patches for which there is no recorded value, this argument should be a boolean matrix of the same dimensions as the data being plotted, where each True means that that specific value must be masked,
• cmap: the colormap to be used, luckily for us, the value "Greens" matches with the colour palette chosen by GitHub,
• vmin: the value that should be considered as the minimum among our values,
• vmax: the value that should be considered as the maximum among our values, I'd consider 100 to be the maximum, even though my record sits as 190 in a day!
• cbar: a boolean value to indicate whether we want to show the colour bar that usually comes with the heatmap,
• ax: the axes our plot should be plotted on.

And voilà, our plot is ready:

It is up to you to modify the plot, may be by adding information about the number of songs, or show the colorbar... a great idea would be to recreate this plot in a framework such as D3.js, but that may well belong to another post. Again, feel free to head over to this Colab and contact me via twitter @io_exception.