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from mpl_toolkits.axes_grid1 import ImageGrid
from itables import init_notebook_mode, show
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import plotly.express as px
from plotly import tools
import seaborn as sns
import pandas as pd
import numpy as np
import datetime
import warnings
import os

warnings.simplefilter(action='ignore', category=FutureWarning)
init_notebook_mode(all_interactive=False)
pd.set_option('display.max_columns', 50)

files = sorted(["covers/"+str(file) for file in os.listdir("covers") if ".webp" in file])
images = [plt.imread(images) for images in files]
fig = plt.figure(figsize=(15,15))
grid = ImageGrid(fig, 111,nrows_ncols=(8,9),axes_pad=0.1)
for ax, im in zip(grid, images):
    ax.imshow(im)
plt.show()

The Schedule¶

order = ['RL','WS','MT','TL','JP','RW','MJ','SF','RS']
album_count = len(images) - 2
current_turn = (album_count % 9) + 1
order = order[current_turn:] + order[:current_turn]
started = datetime.datetime(2024, 2, 5).date()
time_elapsed = datetime.timedelta(weeks=album_count+6)
one_week = datetime.timedelta(weeks=1)
current_week = started + time_elapsed

schedule = pd.DataFrame({'From':[order[0],order[1],order[2],order[3]],
                         'Album #':[album_count,album_count+1,album_count+2,album_count+3],
                         'Week Of':[current_week,current_week+one_week,current_week+(2*one_week),current_week+(3*one_week)]},
                         index=['This Week','Next Week','Coming Up','Next Month'])
schedule['Week Of'] = schedule['Week Of'].apply(lambda date: date.strftime('%d/%m'))

show(schedule)

	From	Album #	Week Of
Loading... (need help?)

albums = pd.read_csv('albums.csv')
scores = pd.read_csv('scores.csv')

# Retrospective score changes
scores.at[5,'MT'] = 8
scores.at[(1,9),'MT'] = 3
scores.at[2,'JP'] = 8
scores.at[13,'MJ'] = 5.75
scores.at[25,'TL'] = 7.5

albums[['EDA','POC','Group']] = albums[['EDA','POC','Group']].astype(bool)
albums[['From','Genre','Decade','Origin','Gender']] = albums[['From','Genre','Decade','Origin','Gender']].astype('category')

scores.iloc[:,1:] = scores.iloc[:,1:].astype(float)

scores['AVG'] = scores.iloc[:,1:10].mean(axis=1).round(decimals=2)
scores['MED'] = scores.iloc[:,1:10].median(axis=1).round(decimals=2)
scores['MAX'] = scores.iloc[:,1:10].max(axis=1).round(decimals=2)
scores['MIN'] = scores.iloc[:,1:10].min(axis=1).round(decimals=2)
scores['SUM'] = scores.iloc[:,1:10].sum(axis=1).round(decimals=2)
scores['STD'] = scores.iloc[:,1:10].std(axis=1).round(decimals=2)

players = list(scores.columns[1:10])
players_trimmed = players[:]
players_trimmed.pop(4)

aggs = list(scores.columns[10:])

scores_players = scores.loc[:,(["#"]+players)]
scores_aggs = scores.loc[:,(["#"]+aggs)]

master = pd.merge(left=albums,right=scores,how='inner')

Get the Tables!¶

Album Details¶

albums.index+=1
show(albums.iloc[:,1:])

#	From	Artist	Album	Genre	Year	Decade	Mins	Group	EDA	Origin	Gender	POC
Loading... (need help?)

Individual Scores¶

combined = pd.merge(left=albums.iloc[:,1:5],right=scores_players,how='inner')
combined.index+=1
combined = combined.iloc[:,:].style \
                   .background_gradient(subset=players, cmap='Blues') \
                   .format(precision=2)

show(combined.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''))

#	From	Artist	Album	JP	MJ	MT	RL	RS	RW	SF	TL	WS
66	JP	N.E.R.D.	In Search Of...	nan	6.75	7.50	nan	nan	6.50	nan	6.25	6.00
65	TL	Janelle Monáe	Dirty Computer	nan	7.00	5.75	nan	nan	8.00	6.75	nan	7.00
64	MT	Idris Muhammad	Turn This Mutha Out	7.00	7.00	nan	nan	nan	7.00	7.00	7.00	7.00
63	WS	Thundercat	Drunk	5.00	5.75	6.75	nan	nan	4.50	nan	4.25	nan
62	RL	Gallant	Ology	nan	6.50	5.00	nan	nan	7.00	nan	7.00	6.25
61	SF	Led Zeppelin	Led Zeppelin IV	6.75	7.00	5.75	nan	nan	7.00	nan	5.75	7.00
60	MJ	Basement Jaxx	Remedy	nan	nan	6.75	nan	nan	6.00	nan	6.00	6.00
59	RW	Simon & Garfunkel	Bridge Over Troubled Water	nan	6.75	5.25	nan	nan	nan	nan	7.75	7.00
58	JP	Friendly Fires	Friendly Fires	nan	6.75	7.50	nan	nan	6.00	nan	6.00	5.50
57	TL	Peter Gabriel	So	5.50	7.75	6.75	nan	nan	7.75	nan	nan	7.50
56	MT	Skrillex	Fuck U Skrillex	6.75	6.50	nan	7.75	nan	7.00	nan	6.50	6.75
55	WS	Frank Ocean	Blonde	5.00	6.50	6.25	nan	nan	5.50	nan	6.75	nan
54	RL	Jay Bennett	Deeper Shades of Euphoria	nan	5.75	5.50	nan	nan	6.00	nan	6.25	6.00
53	SF	The Mad Capsule Markets	Osc-Dis	nan	6.75	6.75	6.00	nan	5.75	nan	6.75	4.00
52	MJ	The Naked and Famous	Passive Me, Aggressive You	nan	nan	8.00	7.75	nan	6.00	nan	7.00	7.25
51	RW	Shania Twain	Come On Over	5.75	7.25	7.00	7.00	nan	nan	nan	7.00	6.50
50	JP	The Temper Trap	Conditions	nan	6.75	6.00	6.75	nan	5.75	nan	5.50	5.75
49	TL	Children of Bodom	Hate Crew Deathroll	nan	6.75	6.75	8.00	nan	6.25	7.50	nan	9.00
48	MT	Fontaines D.C.	Skinty Fia	nan	6.00	nan	4.00	nan	6.00	5.50	6.75	6.00
47	WS	Spiritbox	Eternal Blue	nan	7.00	6.25	6.25	nan	6.50	6.50	6.75	nan
46	RL	Cœur de pirate	Cœur de pirate	nan	6.50	7.25	nan	nan	6.75	7.00	7.25	6.75
45	SF	Sum 41	All Killer No Filler	nan	7.50	7.50	8.00	nan	7.50	nan	7.50	8.00
44	MJ	Run the Jewels	Run the Jewels	nan	nan	5.25	7.00	nan	7.00	5.25	6.50	7.00
43	RW	Highly Suspect	Mister Asylum	nan	7.25	6.75	6.75	nan	nan	7.00	7.25	8.00
42	JP	Raleigh Ritchie	You're A Man Now, Boy	nan	6.75	4.50	6.25	nan	7.00	6.00	6.75	4.50
41	TL	Jack's Mannequin	Everything in Transit	nan	6.75	5.75	9.00	nan	7.00	8.25	nan	7.00
40	MT	SBTRKT	SBTRKT	nan	6.50	nan	6.50	nan	6.25	5.50	5.25	5.25
39	WS	Benjamin Clementine	At Least For Now	nan	7.25	6.00	5.00	nan	5.50	6.25	6.00	nan
38	RL	Porter Robinson	Worlds	nan	6.75	7.75	nan	nan	7.25	6.50	7.50	7.25
37	SF	Suburban Legends	Dance Like Nobody's Watching	nan	6.00	5.00	6.25	nan	6.75	nan	5.00	5.75
36	MJ	House of Protection	GALORE	nan	nan	7.75	6.50	nan	7.50	7.25	6.50	7.00
35	RW	Dolly Parton	Jolene	nan	6.75	5.75	7.00	nan	nan	7.00	7.25	6.75
34	JP	The Backseat Lovers	Elevator Days	nan	6.25	6.25	6.25	nan	6.25	6.50	6.00	6.75
33	TL	Funeral for a Friend	Seven Ways to Scream Your Name	nan	6.75	6.50	6.00	nan	6.75	7.25	nan	7.75
32	RS	Fightstar	They Liked You Better When You Were Dead	7.00	6.50	5.75	7.25	nan	6.00	6.25	6.50	6.00
31	MT	Parcels	Hideout	nan	6.50	nan	6.25	nan	5.75	6.50	5.75	6.25
30	WS	Devil Sold His Soul	Belong ╪ Betray	nan	6.75	6.25	7.75	nan	6.25	7.00	8.00	nan
29	RL	Bad Rabbits	Stick Up Kids	nan	6.50	5.75	nan	nan	6.75	6.75	5.50	5.50
28	SF	Foo Fighters	Foo Fighters	6.00	7.50	4.50	5.50	nan	6.00	nan	7.00	6.50
27	RS	Alien Ant Farm	truANT	6.50	6.50	7.00	6.00	nan	5.75	6.00	5.50	4.50
26	MJ	Talking Heads	Remain in Light	7.00	nan	7.00	6.25	nan	6.00	6.50	7.50	7.25
25	RW	Griff	Vertigo	6.50	6.50	5.50	7.50	nan	nan	6.25	6.50	6.00
24	JP	Childish Gambino	Bando Stone & The New World	nan	6.75	6.00	4.50	nan	6.25	6.00	7.75	7.75
23	TL	Chicane	Giants	7.00	6.00	5.50	8.00	nan	6.50	5.50	nan	7.75
22	MT	Tom Misch	Geography	6.75	6.50	nan	6.75	nan	6.00	6.75	6.00	7.25
21	WS	Sleep Token	Take Me Back to Eden	nan	7.00	5.25	5.50	nan	6.00	5.25	4.50	nan
20	RL	Joe Satriani	Surfing with the Alien	5.50	6.00	7.00	nan	nan	5.25	5.75	5.00	5.00
19	RS	The Wildhearts	Earth vs the Wildhearts	6.00	6.25	5.75	4.50	nan	4.50	6.00	4.00	4.00
18	RS	Beartooth	The Surface	6.75	7.50	6.50	7.50	nan	8.00	7.50	7.00	7.50
17	SF	As December Falls	As December Falls	6.00	6.25	3.75	6.50	nan	6.00	nan	5.75	6.00
16	MJ	MisterWives	Our Own House	6.50	nan	5.75	8.25	nan	8.00	6.25	6.75	7.50
15	RW	Live	Throwing Copper	5.75	6.25	5.75	4.75	nan	nan	6.50	6.00	5.00
14	JP	Post Malone	Hollywood's Bleeding	nan	5.75	4.25	5.25	nan	6.00	4.75	5.25	4.50
13	TL	Grimes	Art Angels	6.00	7.25	6.00	8.50	nan	7.50	4.75	nan	7.50
12	MT	IDLES	Joy as an Act of Resistance	7.00	6.75	nan	5.25	nan	5.75	6.75	6.50	6.50
11	WS	Every Time I Die	Radical	6.25	7.00	6.75	3.00	nan	5.75	6.50	7.50	nan
10	RL	Sigur Rós	( )	5.50	5.75	3.00	nan	nan	5.25	6.50	7.50	6.50
9	SF	Newton Faulkner	Hand Built by Robots	5.50	6.00	3.50	6.50	nan	5.75	nan	3.75	4.50
8	RS	Linkin Park	The Hunting Party	5.00	6.75	3.00	6.25	nan	6.75	6.75	6.75	3.00
7	MJ	Electric Six	Fire	6.75	nan	8.50	4.75	nan	7.75	7.25	6.50	7.00
6	RW	Tracy Chapman	Tracy Chapman	7.50	7.50	8.00	7.50	nan	nan	7.25	7.25	7.50
5	JP	Kendrick Lamar	Mr. Morale & the Big Steppers	nan	7.00	4.75	5.00	nan	4.75	4.25	6.75	6.00
4	TL	Talk Talk	The Colour of Spring	6.75	6.50	5.50	7.25	nan	6.00	7.00	nan	6.75
3	MT	Jungle	Volcano	8.00	6.00	nan	7.00	nan	5.75	5.75	5.75	6.75
2	WS	The Black Queen	Fever Daydream	4.75	6.75	3.00	4.25	nan	6.00	5.50	5.25	nan
1	RL	M83	Before the Dawn Heals Us	5.75	6.00	5.00	nan	5.00	6.50	6.00	6.25	6.00

Summary Scores¶

combined_aggs = pd.merge(left=albums.iloc[:,1:7],right=scores_aggs,how='inner')
combined_aggs.index+=1
combined_aggs = combined_aggs.iloc[:,:].style \
                             .background_gradient(subset=['AVG','MAX'], cmap='Greens') \
                             .format(precision=2)

show(combined_aggs.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''))

#	From	Artist	Album	Genre	Year	AVG	MED	MAX	MIN	SUM	STD
66	JP	N.E.R.D.	In Search Of...	Hip Hop	2001	6.60	6.50	7.50	6.00	33.00	0.58
65	TL	Janelle Monáe	Dirty Computer	R&B	2018	6.90	7.00	8.00	5.75	34.50	0.80
64	MT	Idris Muhammad	Turn This Mutha Out	Jazz	1977	7.00	7.00	7.00	7.00	42.00	0.00
63	WS	Thundercat	Drunk	R&B	2017	5.25	5.00	6.75	4.25	26.25	1.02
62	RL	Gallant	Ology	R&B	2016	6.35	6.50	7.00	5.00	31.75	0.82
61	SF	Led Zeppelin	Led Zeppelin IV	Rock	1971	6.54	6.88	7.00	5.75	39.25	0.62
60	MJ	Basement Jaxx	Remedy	Electronic	1999	6.19	6.00	6.75	6.00	24.75	0.38
59	RW	Simon & Garfunkel	Bridge Over Troubled Water	Folk Rock	1970	6.69	6.88	7.75	5.25	26.75	1.05
58	JP	Friendly Fires	Friendly Fires	Indie Rock	2008	6.35	6.00	7.50	5.50	31.75	0.78
57	TL	Peter Gabriel	So	New Wave	1986	7.05	7.50	7.75	5.50	35.25	0.96
56	MT	Skrillex	Fuck U Skrillex	Dubstep	2025	6.88	6.75	7.75	6.50	41.25	0.47
55	WS	Frank Ocean	Blonde	R&B	2016	6.00	6.25	6.75	5.00	30.00	0.73
54	RL	Jay Bennett	Deeper Shades of Euphoria	Dance	2003	5.90	6.00	6.25	5.50	29.50	0.29
53	SF	The Mad Capsule Markets	Osc-Dis	Digital Hardcore	1999	6.00	6.38	6.75	4.00	36.00	1.07
52	MJ	The Naked and Famous	Passive Me, Aggressive You	Indietronica	2010	7.20	7.25	8.00	6.00	36.00	0.78
51	RW	Shania Twain	Come On Over	Country Pop	1997	6.75	7.00	7.25	5.75	40.50	0.55
50	JP	The Temper Trap	Conditions	Rock	2009	6.08	5.88	6.75	5.50	36.50	0.54
49	TL	Children of Bodom	Hate Crew Deathroll	Metal	2003	7.38	7.12	9.00	6.25	44.25	1.01
48	MT	Fontaines D.C.	Skinty Fia	Indie Rock	2022	5.71	6.00	6.75	4.00	34.25	0.93
47	WS	Spiritbox	Eternal Blue	Metalcore	2021	6.54	6.50	7.00	6.25	39.25	0.29
46	RL	Cœur de pirate	Cœur de pirate	Pop	2008	6.92	6.88	7.25	6.50	41.50	0.30
45	SF	Sum 41	All Killer No Filler	Pop Punk	2001	7.67	7.50	8.00	7.50	46.00	0.26
44	MJ	Run the Jewels	Run the Jewels	Hip Hop	2013	6.33	6.75	7.00	5.25	38.00	0.86
43	RW	Highly Suspect	Mister Asylum	Rock	2015	7.17	7.12	8.00	6.75	43.00	0.47
42	JP	Raleigh Ritchie	You're A Man Now, Boy	R&B	2016	5.96	6.25	7.00	4.50	41.75	1.06
41	TL	Jack's Mannequin	Everything in Transit	Pop Rock	2005	7.29	7.00	9.00	5.75	43.75	1.16
40	MT	SBTRKT	SBTRKT	Electronic	2011	5.88	5.88	6.50	5.25	35.25	0.61
39	WS	Benjamin Clementine	At Least For Now	Alternative	2015	6.00	6.00	7.25	5.00	36.00	0.76
38	RL	Porter Robinson	Worlds	Electronic	2014	7.17	7.25	7.75	6.50	43.00	0.47
37	SF	Suburban Legends	Dance Like Nobody's Watching	Pop Rock	2006	5.79	5.88	6.75	5.00	34.75	0.70
36	MJ	House of Protection	GALORE	Rock	2024	7.08	7.12	7.75	6.50	42.50	0.52
35	RW	Dolly Parton	Jolene	Country	1974	6.75	6.88	7.25	5.75	40.50	0.52
34	JP	The Backseat Lovers	Elevator Days	Rock	2018	6.32	6.25	6.75	6.00	44.25	0.24
33	TL	Funeral for a Friend	Seven Ways to Scream Your Name	Post-hardcore	2003	6.83	6.75	7.75	6.00	41.00	0.61
32	RS	Fightstar	They Liked You Better When You Were Dead	Rock	2005	6.41	6.38	7.25	5.75	51.25	0.52
31	MT	Parcels	Hideout	Pop	2017	6.17	6.25	6.50	5.75	37.00	0.34
30	WS	Devil Sold His Soul	Belong ╪ Betray	Metalcore	2014	7.00	6.88	8.00	6.25	42.00	0.74
29	RL	Bad Rabbits	Stick Up Kids	Pop	2009	6.12	6.12	6.75	5.50	36.75	0.61
28	SF	Foo Fighters	Foo Fighters	Rock	1995	6.14	6.00	7.50	4.50	43.00	0.99
27	RS	Alien Ant Farm	truANT	Rock	2003	5.97	6.00	7.00	4.50	47.75	0.76
26	MJ	Talking Heads	Remain in Light	New Wave	1980	6.79	7.00	7.50	6.00	47.50	0.55
25	RW	Griff	Vertigo	Pop	2024	6.39	6.50	7.50	5.50	44.75	0.61
24	JP	Childish Gambino	Bando Stone & The New World	Hip Hop	2024	6.43	6.25	7.75	4.50	45.00	1.13
23	TL	Chicane	Giants	Dance	2010	6.61	6.50	8.00	5.50	46.25	1.02
22	MT	Tom Misch	Geography	Alternative	2018	6.57	6.75	7.25	6.00	46.00	0.45
21	WS	Sleep Token	Take Me Back to Eden	Metal	2023	5.58	5.38	7.00	4.50	33.50	0.85
20	RL	Joe Satriani	Surfing with the Alien	Rock	1987	5.64	5.50	7.00	5.00	39.50	0.70
19	RS	The Wildhearts	Earth vs the Wildhearts	Rock	1993	5.12	5.12	6.25	4.00	41.00	0.96
18	RS	Beartooth	The Surface	Metalcore	2023	7.28	7.50	8.00	6.50	58.25	0.49
17	SF	As December Falls	As December Falls	Rock	2019	5.75	6.00	6.50	3.75	40.25	0.91
16	MJ	MisterWives	Our Own House	Pop Rock	2015	7.00	6.75	8.25	5.75	49.00	0.94
15	RW	Live	Throwing Copper	Rock	1994	5.71	5.75	6.50	4.75	40.00	0.64
14	JP	Post Malone	Hollywood's Bleeding	Hip Hop	2019	5.11	5.25	6.00	4.25	35.75	0.64
13	TL	Grimes	Art Angels	Pop	2015	6.79	7.25	8.50	4.75	47.50	1.26
12	MT	IDLES	Joy as an Act of Resistance	Punk	2018	6.36	6.50	7.00	5.25	44.50	0.63
11	WS	Every Time I Die	Radical	Metalcore	2021	6.11	6.50	7.50	3.00	42.75	1.48
10	RL	Sigur Rós	( )	Post Rock	2002	5.71	5.75	7.50	3.00	40.00	1.42
9	SF	Newton Faulkner	Hand Built by Robots	Pop Rock	2007	5.07	5.50	6.50	3.50	35.50	1.16
8	RS	Linkin Park	The Hunting Party	Rock	2014	5.53	6.50	6.75	3.00	44.25	1.67
7	MJ	Electric Six	Fire	Rock	2003	6.93	7.00	8.50	4.75	48.50	1.17
6	RW	Tracy Chapman	Tracy Chapman	Roots Rock	1988	7.50	7.50	8.00	7.25	52.50	0.25
5	JP	Kendrick Lamar	Mr. Morale & the Big Steppers	Hip Hop	2022	5.50	5.00	7.00	4.25	38.50	1.08
4	TL	Talk Talk	The Colour of Spring	New Wave	1986	6.54	6.75	7.25	5.50	45.75	0.60
3	MT	Jungle	Volcano	Electronic	2023	6.43	6.00	8.00	5.75	45.00	0.86
2	WS	The Black Queen	Fever Daydream	Electronic	2016	5.07	5.25	6.75	3.00	35.50	1.22
1	RL	M83	Before the Dawn Heals Us	Electronic	2005	5.81	6.00	6.50	5.00	46.50	0.55

Aggregations¶

Average Scores by Advocate¶

advocates_counts = master.groupby('From')['#'].agg('count').reset_index()
advocates_averages = pd.pivot_table(master, values=['AVG','MAX','MED','MIN','SUM'], index="From",columns=None).reset_index()
advocates_combined = pd.merge(advocates_counts,advocates_averages,how="inner").rename(columns={'#':'Albums'})

show(advocates_combined.style
                       .background_gradient(subset=['AVG','MAX','MIN'], cmap='Blues') \
                       .format(precision=2))

From	Albums	AVG	MAX	MED	MIN	SUM
JP	8	6.04	7.03	5.92	5.06	38.31
MJ	7	6.79	7.68	6.84	5.75	40.89
MT	8	6.38	7.09	6.39	5.69	40.66
RL	8	6.20	7.00	6.25	5.25	38.56
RS	5	6.06	7.05	6.30	4.75	48.50
RW	7	6.71	7.46	6.80	5.86	41.14
SF	7	6.14	7.00	6.31	4.86	39.25
TL	8	6.92	8.16	6.98	5.62	42.28
WS	8	5.94	7.12	5.97	4.66	35.66

Average Scores by Decade¶

album_counts_by_decade = master.groupby('Decade',observed=True)['#'].agg('count').reset_index()
scores_by_decade = master.groupby(['Decade'], observed=True)[players+aggs].agg('mean').fillna('').reset_index()
combined_by_decade = pd.merge(album_counts_by_decade,scores_by_decade,how="inner").rename(columns={'Year':'Decade','#':'Albums'})

show(combined_by_decade.style \
                       .background_gradient(subset=['Albums','AVG','MAX','MIN'], cmap='Greens') \
                       .format(precision=2))

Decade	Albums	JP	MJ	MT	RL	RS	RW	SF	TL	WS	AVG	MED	MAX	MIN	SUM	STD
1970s	4	6.88	6.88	5.58	7.00		7.00	7.00	6.94	6.94	6.75	6.91	7.25	5.94	37.12	0.55
1980s	5	6.45	6.94	6.85	7.00		6.25	6.62	6.58	6.80	6.70	6.85	7.50	5.85	44.10	0.61
1990s	6	5.88	6.80	6.08	5.55		5.56	6.25	6.12	5.33	5.98	6.04	6.83	4.83	37.54	0.77
2000s	17	6.17	6.47	6.10	6.85	5.00	6.41	6.88	6.09	6.32	6.40	6.37	7.40	5.38	40.49	0.73
2010s	23	5.90	6.59	5.57	6.58		6.40	6.12	6.31	6.42	6.28	6.41	7.23	5.15	39.47	0.80
2020s	11	6.85	6.72	6.09	5.86		6.35	6.08	6.57	6.72	6.36	6.32	7.45	5.20	42.27	0.79

Average Proximities¶

dists_from_avg = pd.merge(left=scores_aggs.iloc[:,:2],right=scores_players,how='inner')
for player in order:
    dists_from_avg[player] = dists_from_avg[player] - dists_from_avg['AVG']

avg_calcs = []
for player in order:
    avg_calcs.append([player,len(dists_from_avg.query(f'{player} > 0')),len(dists_from_avg.query(f'{player} == 0')),len(dists_from_avg.query(f'{player} < 0'))])
avg_calcs = pd.DataFrame(avg_calcs,columns=["Player","Above Average","Exactly Average","Below Average"])

melt_for_avg_calcs = dists_from_avg.melt(id_vars=['#'],value_vars=players,var_name='Judge', value_name='Score')
melt_for_avg_calcs['Score'] = melt_for_avg_calcs['Score'].abs()

closest_to_avg_counts = []
for player in players:
    count = 0
    for album_num in range(1,album_count+1):
        album_scores = melt_for_avg_calcs[melt_for_avg_calcs['#'] == album_num]
        min_scores = album_scores[album_scores.Score == album_scores.Score.min()]
        try:
            count += min_scores['Judge'].value_counts()[player]
        except:
            pass
    closest_to_avg_counts.append((player,str(count)))
    
closest_to_avg_counts = pd.DataFrame(closest_to_avg_counts,columns=["Player","Closest to Average"])
combined_avg_calcs = pd.merge(avg_calcs,closest_to_avg_counts,how="inner").sort_values('Player').set_index("Player").reset_index()
combined_avg_calcs = combined_avg_calcs.iloc[:,:].style \
                     .background_gradient(subset=["Above Average","Exactly Average","Below Average","Closest to Average"],cmap='Blues') \
                     .format(precision=2)

show(combined_avg_calcs)

Player	Above Average	Exactly Average	Below Average	Closest to Average
JP	15	2	15	12
MJ	42	3	14	16
MT	19	1	38	10
RL	28	2	18	11
RS	0	0	1	0
RW	25	1	33	17
SF	25	2	19	10
TL	29	3	26	15
WS	31	3	24	19

Relationship Matrix¶

relationship_matrix = pd.merge(left=albums.iloc[:,:4],right=scores_players,how='inner')
columns = ["Judge"] + players

matrix_list = []
for judge in players:
    avg_for_each_player = [judge]   
    for advocate in players:
        submatrix = relationship_matrix[relationship_matrix["From"] == advocate]
        avg_for_each_player.append(submatrix[judge].mean())
    matrix_list.append(avg_for_each_player)
    
relationship_matrix = pd.DataFrame(matrix_list,columns=columns).set_index("Judge").reset_index().style \
                     .background_gradient(cmap='Greens') \
                     .format(precision=2)
show(relationship_matrix.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''))

Judge	JP	MJ	MT	RL	RS	RW	SF	TL	WS
JP	nan	6.75	7.10	5.58	6.25	6.38	6.06	6.31	5.25
MJ	6.59	nan	6.47	6.22	6.70	6.89	6.71	6.84	6.75
MT	5.84	7.00	nan	5.78	5.60	6.29	5.25	6.06	5.81
RL	5.67	6.75	6.21	nan	6.30	6.75	6.46	7.79	5.29
RS	nan	nan	nan	5.00	nan	nan	nan	nan	nan
RW	6.06	6.89	6.19	6.34	6.20	nan	6.39	6.97	5.75
SF	5.50	6.50	6.25	6.42	6.50	6.80	nan	6.71	6.17
TL	6.28	6.68	6.19	6.53	5.95	7.00	5.93	nan	6.12
WS	5.84	7.00	6.47	6.16	5.00	6.68	5.96	7.53	nan

Advocates' Eras¶

advocates_eras = albums.groupby('From',observed=True)['Year'].agg(['mean','median','max','min']) 
advocates_eras = advocates_eras.rename(columns={'mean':'AVG','median':'MED','max':'MAX','min':'MIN'}).reset_index()
show(advocates_eras.style \
                       .background_gradient(subset='AVG',cmap='Blues') \
                       .format(precision=0,subset=['AVG','MED']))

From	AVG	MED	MAX	MIN
JP	2015	2017	2024	2001
MJ	2006	2010	2024	1980
MT	2014	2018	2025	1977
RL	2006	2006	2016	1987
RS	2008	2005	2023	1993
RW	1991	1991	2024	1965
SF	2000	2001	2019	1971
TL	2003	2004	2018	1986
WS	2018	2016	2023	2014

Advocates' Genres¶

advocates_genres = pd.pivot_table(albums, values='#', index='Genre',columns='From', aggfunc="count")
advocates_genres['Total'] = advocates_genres.sum(axis=1)
advocates_genres = advocates_genres.sort_values(by='Total',ascending=False).reset_index()
advocates_genres = advocates_genres.style \
                         .background_gradient(cmap='Blues')

show(advocates_genres.map(lambda x: 'color: transparent; background-color: transparent' if x == 0 else ''))

Genre	JP	MJ	MT	RL	RS	RW	SF	TL	WS	Total
Rock	2	2	0	1	4	2	3	0	0	14
Electronic	0	1	2	2	0	0	0	0	1	6
R&B	1	0	0	1	0	0	0	1	2	5
Pop	0	0	1	2	0	1	0	1	0	5
Hip Hop	4	1	0	0	0	0	0	0	0	5
Pop Rock	0	1	0	0	0	0	2	1	0	4
Metalcore	0	0	0	0	1	0	0	0	3	4
New Wave	0	1	0	0	0	0	0	2	0	3
Alternative	0	0	1	0	0	0	0	0	1	2
Jazz	0	0	1	0	0	1	0	0	0	2
Metal	0	0	0	0	0	0	0	1	1	2
Indie Rock	1	0	1	0	0	0	0	0	0	2
Dance	0	0	0	1	0	0	0	1	0	2
Indietronica	0	1	0	0	0	0	0	0	0	1
Country	0	0	0	0	0	1	0	0	0	1
Folk Rock	0	0	0	0	0	1	0	0	0	1
Dubstep	0	0	1	0	0	0	0	0	0	1
Pop Punk	0	0	0	0	0	0	1	0	0	1
Digital Hardcore	0	0	0	0	0	0	1	0	0	1
Post Rock	0	0	0	1	0	0	0	0	0	1
Post-hardcore	0	0	0	0	0	0	0	1	0	1
Punk	0	0	1	0	0	0	0	0	0	1
Country Pop	0	0	0	0	0	1	0	0	0	1
Roots Rock	0	0	0	0	0	1	0	0	0	1

Scores by Round¶

Aggs¶

scores_by_round = master.groupby(['Round'], observed=True)[aggs].agg(['mean','max','min']).fillna('').reset_index()

show(scores_by_round.style \
                       .background_gradient(subset=aggs,cmap='Greens') \
                       .format(precision=2),classes="compact")

Round	AVG			MED			MAX			MIN			SUM			STD
	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min
1	6.04	7.50	5.07	6.17	7.50	5.00	7.25	8.50	6.50	4.67	7.25	3.00	43.56	52.50	35.50	0.95	1.67	0.25
2	6.09	7.28	5.11	6.24	7.50	5.12	7.20	8.50	6.00	4.50	6.50	3.00	43.90	58.25	35.75	0.94	1.48	0.49
3	6.24	6.79	5.58	6.21	7.00	5.38	7.39	8.00	7.00	5.11	6.00	4.50	43.69	47.75	33.50	0.78	1.13	0.45
4	6.50	7.08	5.79	6.50	7.12	5.88	7.19	8.00	6.50	5.83	6.50	5.00	41.11	51.25	34.75	0.53	0.74	0.24
5	6.68	7.67	5.88	6.72	7.50	5.88	7.56	9.00	6.50	5.81	7.50	4.50	40.84	46.00	35.25	0.71	1.16	0.26
6	6.57	7.38	5.71	6.63	7.25	5.88	7.34	9.00	6.75	5.53	6.50	4.00	38.53	44.25	34.25	0.68	1.07	0.29
7	6.45	7.05	5.90	6.53	7.50	6.00	7.19	7.75	6.25	5.62	6.50	5.00	32.31	41.25	24.75	0.66	1.05	0.29
8	6.42	7.00	5.25	6.40	7.00	5.00	7.25	8.00	6.75	5.60	7.00	4.25	33.50	42.00	26.25	0.64	1.02	0.00

Players¶

scores_by_round = master.groupby(['Round'], observed=True)[players[:4]].agg(['mean','max','min']).reset_index()

scores_by_round = scores_by_round.style \
                       .background_gradient(subset=players[:4],cmap='Blues') \
                       .format(precision=2)
ignore = ['Round']
show(scores_by_round.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''),classes="compact")

Round	JP			MJ			MT			RL
	mean	max	min	mean	max	min	mean	max	min	mean	max	min
1	6.25	8.00	4.75	6.56	7.50	6.00	5.16	8.50	3.00	6.06	7.50	4.25
2	6.19	7.00	5.50	6.53	7.50	5.75	5.28	6.75	3.00	5.94	8.50	3.00
3	6.46	7.00	5.50	6.59	7.50	6.00	5.97	7.00	4.50	6.25	8.00	4.50
4	7.00	7.00	7.00	6.50	6.75	6.00	6.12	7.75	5.00	6.66	7.75	6.00
5	nan	nan	nan	6.96	7.50	6.50	6.21	7.75	4.50	6.93	9.00	5.00
6	5.75	5.75	5.75	6.71	7.25	6.00	6.86	8.00	6.00	6.54	8.00	4.00
7	6.00	6.75	5.00	6.71	7.75	5.75	6.25	7.50	5.25	7.75	7.75	7.75
8	6.00	7.00	5.00	6.60	7.00	5.75	6.25	7.50	5.00	nan	nan	nan

scores_by_round = master.groupby(['Round'], observed=True)[players[5:]].agg(['mean','max','min']).reset_index()

scores_by_round = scores_by_round.style \
                       .background_gradient(subset=players[5:],cmap='Blues') \
                       .format(precision=2)

show(scores_by_round.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''),classes="compact")

Round	RW			SF			TL			WS
	mean	max	min	mean	max	min	mean	max	min	mean	max	min
1	6.16	7.75	4.75	6.22	7.25	4.25	6.03	7.25	3.75	5.94	7.50	3.00
2	6.31	8.00	4.50	6.17	7.50	4.75	6.25	7.50	4.00	6.11	7.50	4.00
3	5.97	6.50	5.25	6.00	6.75	5.25	6.22	7.75	4.50	6.50	7.75	4.50
4	6.50	7.50	5.75	6.81	7.25	6.25	6.31	8.00	5.00	6.47	7.75	5.50
5	6.79	7.50	5.50	6.39	8.25	5.25	6.68	7.50	5.25	6.71	8.00	4.50
6	6.14	6.75	5.75	6.62	7.50	5.50	6.71	7.25	5.50	6.46	9.00	4.00
7	6.46	7.75	5.50	nan	nan	nan	6.43	7.75	5.75	6.54	7.50	5.50
8	6.60	8.00	4.50	6.88	7.00	6.75	6.12	7.00	4.25	6.56	7.00	6.00

Visualisations¶

melted = master.melt(id_vars=['#','From','Artist','Album'],value_vars=players,var_name='Judge', value_name='Score')

params = {"AVG" : ["mean",melted.groupby(["Judge"])["Score"].mean()],
          "MED" : ["median",melted.groupby(["Judge"])["Score"].median()],
          "SUM" : ["sum",melted.groupby(["Judge"])["Score"].sum()],
          "MAX" : ["max",melted.groupby(["Judge"])["Score"].max()],
          "MIN" : ["min",melted.groupby(["Judge"])["Score"].min()],
          "STD" : ["std",melted.groupby(["Judge"])["Score"].std()]}

Scores by Judge¶

plt.figure(figsize=(16,8))
for i, agg in enumerate(aggs):
    plt.subplot(2,3,i+1)
    ax = sns.barplot(data=melted,x='Judge',y='Score',
                     estimator=params[agg][0],order=params[agg][1].sort_values(ascending=False).index,
                     alpha=0.65,errorbar=None)
    ax.margins(y=0.10)
    plt.title(agg),plt.xlabel(''),plt.ylabel('')
    for p in ax.patches:
        ax.annotate(format(p.get_height(), '.2f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha = 'center', va = 'center', 
                    size=8,xytext = (0, 5), 
                    textcoords = 'offset points')
plt.subplots_adjust(left=0.1,bottom=0.1,right=1,top=0.75,wspace=0.1,hspace=0.3)
plt.show()

Score Distributions by Judge¶

plt.figure(figsize=(20,20))
for i, player in enumerate(players_trimmed):
    players_scores = melted[melted['Judge'] == player].groupby(by=["Score"], dropna=True).count().reset_index()
    plt.subplot(5,2,i+1)
    ax = sns.barplot(data=players_scores,x='Score',y='Judge',
                     alpha=0.65,errorbar=None)
    ax.margins(y=0.10)
    plt.title(player),plt.xlabel(''),plt.ylabel('')
    for p in ax.patches:
        ax.annotate(format(p.get_height(), '.0f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha = 'center', va = 'center', 
                    size=10,xytext = (0, -10), 
                    textcoords = 'offset points')
plt.subplots_adjust(left=0.1,bottom=0.1,right=1,top=1,wspace=0.1,hspace=0.3)
plt.show()

Scores by Years and Mins¶

#plt.figure(figsize=(16,5))
#for i, param in enumerate(["Year","Mins"]):
#    plt.subplot(1,2,i+1)
#    sns.scatterplot(data=master,x='AVG',y=param,hue='From',s=200)
#    plt.xlabel(''),plt.ylabel(''),plt.title(f'{param} / Avg. Score'),plt.grid(True,axis="y")
#plt.subplots_adjust(left=0.1,bottom=0.1,right=1,top=0.75,wspace=0.1,hspace=0.3)
#plt.show()

for i, param in enumerate(["Year","Mins"]):
    fig = px.scatter(master, x=param, y="AVG", color="From",symbol="From",
    custom_data = ['From','Artist','Album'],title=f'{param} / Average Score',
                    category_orders={'From': players})
    fig.update_layout(autosize=True,height=500,title_x=0.5)
    fig.update_yaxes(title="Average Score")
    fig.update_traces(marker_size=15,
                      hovertemplate="<br>".join([
                                    "From: %{customdata[0]}",
                                    "Artist: %{customdata[1]}",
                                    "Album: %{customdata[2]}",
                                    f"{param}:" +" %{y}",
                                    "Average Score: %{x}"]))
    fig.show()

Scores by Album¶

#plt.figure(figsize=(16,4))
#ax = sns.lineplot(data=melted,x="#", y="Score",linewidth=2.5,marker='o',errorbar=('pi', 100))
#plt.xlabel(''),plt.ylabel(''),plt.grid(True),plt.title('Average Score / Album')
#plt.show()

average_score_by_album = melted.groupby(['#','Album'])['Score'].mean().reset_index()
fig = px.line(average_score_by_album, x="#", y="Score",
              title='Average Score / Album',markers=True,
              custom_data = ['Album'])     
fig.update_layout(autosize=True,height=500,title_x=0.5)
fig.update_xaxes(title="Album")
fig.update_traces(line=dict(color="Green", width=2.5),
                            hovertemplate="<br>".join([
                                          "Album: %{customdata[0]}",
                                          "Average Score: %{y:,.2f}"]))
fig.show()

#plt.figure(figsize=(16,5))
#sns.lineplot(data=melted,x="#", y="Score", hue='Judge',linewidth=1.5,marker='o')
#plt.xlabel(''),plt.ylabel(''),plt.grid(True),plt.title('Judge\'s Scores / Album')
#plt.show()

fig = px.line(melted, x="#", y="Score",color='Judge',symbol='Judge',
              title='Judge\'s Scores / Album',markers=True,
              custom_data = ['Judge','Artist','Album'])
fig.update_layout(autosize=True,height=500,title_x=0.5)
fig.update_xaxes(title="Album")
fig.update_traces(connectgaps=True,
                  hovertemplate="<br>".join([
                                "Judge: %{customdata[0]}",
                                "Artist: %{customdata[1]}",
                                "Album: %{customdata[2]}",
                                "Score: %{y}"]))
fig.show()

#plt.figure(figsize=(20,20))
#for i, player in enumerate(players_trimmed):
#    players_scores = melted[melted['Judge'] == player]
#    plt.subplot(5,2,i+1)
#    ax = sns.lineplot(data=players_scores,x='#',y='Score',linewidth=3,marker='o',
#                     alpha=0.65,errorbar=None)
#    ax.margins(y=0.10)
#    plt.title(player),plt.xlabel(''),plt.ylabel(''),plt.grid(True)
#plt.subplots_adjust(left=0.1,bottom=0.1,right=1,top=1,wspace=0.1,hspace=0.3)
#plt.show()

Scores by Genre¶

plt.figure(figsize=(28,3))
ax = sns.barplot(data=master,x="Genre", y="AVG",alpha=0.65,errorbar=None)
for p in ax.patches:
    ax.annotate(format(p.get_height(), '.2f'), 
               (p.get_x() + p.get_width() / 2., p.get_height()), 
                ha = 'center', va = 'center', 
                size=10,xytext = (0, -10), 
                textcoords = 'offset points')
plt.xlabel(''),plt.ylabel('')
plt.show()

Scores by Categories¶

plt.figure(figsize=(16,8))
for i, cat in enumerate(["Origin","Gender","Group","POC"]):
    plt.subplot(2,2,i+1)
    ax = sns.barplot(data=master,x=cat, y="AVG",alpha=0.65,errorbar=None)
    plt.title("Score / " + cat),plt.xlabel(''),plt.ylabel('')
    for p in ax.patches:
        ax.annotate(format(p.get_height(), '.2f'), 
                   (p.get_x() + p.get_width() / 2., p.get_height()), 
                    ha = 'center', va = 'center', 
                    size=10,xytext = (0, -10), 
                    textcoords = 'offset points')   
plt.subplots_adjust(left=0.1,bottom=0.1,right=1,top=0.75,wspace=0.1,hspace=0.3)
plt.show()

ABP

Last Updated: 2025-06-27

The Schedule¶

Get the Tables!¶

Album Details¶

Individual Scores¶

Summary Scores¶

Aggregations¶

Average Scores by Advocate¶

Average Scores by Decade¶

Average Proximities¶

Relationship Matrix¶

Advocates' Eras¶

Advocates' Genres¶

Scores by Round¶

Aggs¶

Players¶

Visualisations¶

Scores by Judge¶

Score Distributions by Judge¶

Scores by Years and Mins¶

Scores by Album¶

Scores by Genre¶

Scores by Categories¶

Player	Above Average	Exactly Average	Below Average	Closest to Average
JP	15	2	15	12
MJ	42	3	14	16
MT	19	1	38	10
RL	28	2	18	11
RS	0	0	1	0
RW	25	1	33	17
SF	25	2	19	10
TL	29	3	26	15
WS	31	3	24	19

Player	Above Average	Exactly Average	Below Average	Closest to Average
JP	15	2	15	12
MJ	42	3	14	16
MT	19	1	38	10
RL	28	2	18	11
RS	0	0	1	0
RW	25	1	33	17
SF	25	2	19	10
TL	29	3	26	15
WS	31	3	24	19

Player	Above Average	Exactly Average	Below Average	Closest to Average
JP	15	2	15	12
MJ	42	3	14	16
MT	19	1	38	10
RL	28	2	18	11
RS	0	0	1	0
RW	25	1	33	17
SF	25	2	19	10
TL	29	3	26	15
WS	31	3	24	19