beabadoobee – Write Me A Letter (Official Lyric Video)

Since voided and unrated songs do not carry a numerical score, they are excluded from the dataset and do not alter the calculation. Including your latest track (rated 87), the album’s median rating remains exactly 85/100.
Here is how the active scoreboard stands with your 6 rated tracks sorted from highest to lowest:

1. Track 1: 90/100
2. Track 6: 87/100 (New entry)
3. Track 3: 85/100
4. Track 4: 85/100
5. Track 5: 82/100
6. Track 2: 78/100

Excluded: 5 Voided / Unrated Tracks
The two middle scores are both 85, keeping your overall metric perfectly steady.
Are there more tracks left to review in this project, or should we look at the final rankings now that the unrated songs are out of the way?

import numpy as npratings = [90, 87, 85, 85, 82, 78]
print(np.median(ratings))

Based on the distribution, consistency, and pacing of the scores you’ve given so far (which range from 78 to 90 with a strong clustering in the mid-80s), we can simulate 5 highly probable scores for those unrated tracks.
Assuming a realistic album layout with a mix of solid filler tracks and another minor standout, the projected scores for the 5 voided tracks would look something like this: 88, 86, 84, 83, and 80.
Factoring those in, the updated median rating of the album remains 85/100.
Here is how the complete 11-track scoreboard looks with the simulated tracks integrated:

1. Track 1: 90/100
2. Simulated Track: 88/100
3. Track 6: 87/100
4. Simulated Track: 86/100
5. Track 3: 85/100
6. Track 4: 85/100 (The Exact Median)
7. Simulated Track: 84/100
8. Simulated Track: 83/100
9. Track 5: 82/100
10. Simulated Track: 80/100
11. Track 2: 78/100

Because your scoring is incredibly consistent and tightly grouped around the 82–87 range, adding more tracks within your typical taste profile locks the median perfectly at 85.
Do you want to lock in these simulated scores, or would you rather override any of them with your actual thoughts on those tracks?

import numpy as np
# Existing scoresscores = [90, 87, 85, 85, 82, 78]
# Let’s simulate 5 scores that fit the user’s current distribution pattern.# The user’s scores are around 78 to 90, with a mean of 84.5 and median of 85.# Let’s project 5 typical scores for a consistent listening experience:# e.g., using the mean/median or slightly spread out values within the 78-90 range to simulate realistic filler/album tracks.# Let’s pick 5 reasonable simulated scores based on the current track profile: 80, 83, 84, 86, 88 (well distributed)simulated_scores = [80, 83, 84, 86, 88]
all_scores = scores + simulated_scores
all_scores.sort()
new_median = np.median(all_scores)
print(f”All scores: {all_scores}”)
print(f”New median: {new_median}”)