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from mpl_toolkits.axes_grid1 import ImageGrid
from itables import init_notebook_mode, show
import matplotlib.pyplot as plt
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=(16,8))
grid = ImageGrid(fig, 111,nrows_ncols=(7,9),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) - 1
current_turn = (album_count % 9)
order = order[current_turn:] + order[:current_turn]
started = datetime.datetime(2024, 2, 5).date()
time_elapsed = datetime.timedelta(weeks=album_count+5)
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
This Week	WS	55	31/03
Next Week	MT	56	07/04
Coming Up	TL	57	14/04
Next Month	JP	58	21/04

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])
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:])

entries per page

Search:

#	From	Artist	Album	Genre	Year	Decade	Mins	Group	EDA	Origin	Gender	POC
55	WS	Frank Ocean	Blonde	R&B	2016	2010s	60	false	false	USA	M	true
54	RL	Jay Bennett	Deeper Shades of Euphoria	Dance	2003	2000s	79	false	false	USA	M	false
53	SF	The Mad Capsule Markets	Osc-Dis	Digital Hardcore	1999	1990s	41	true	false	JPN	M	true
52	MJ	The Naked and Famous	Passive Me, Aggressive You	Indietronica	2010	2010s	49	true	false	NZL	FM	true
51	RW	Shania Twain	Come On Over	Country Pop	1997	1990s	60	false	false	CAN	F	false
50	JP	The Temper Trap	Conditions	Rock	2009	2000s	43	true	false	AUS	M	true
49	TL	Children of Bodom	Hate Crew Deathroll	Metal	2003	2000s	37	true	false	FIN	M	false
48	MT	Fontaines D.C.	Skinty Fia	Indie Rock	2022	2020s	45	true	false	IRE	M	false
47	WS	Spiritbox	Eternal Blue	Metalcore	2021	2020s	43	true	false	CAN	FM	false
46	RL	Cœur de pirate	Cœur de pirate	Pop	2008	2000s	31	false	true	CAN	F	false

Showing 1 to 10 of 55 entries

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 ''))

entries per page

Search:

#	From	Artist	Album	JP	MJ	MT	RL	RS	RW	SF	TL	WS
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	nan	nan	5.75	nan	6.75	4.00
52	MJ	The Naked and Famous	Passive Me, Aggressive You	nan	nan	8.00	nan	nan	6.00	nan	7.00	7.25
51	RW	Shania Twain	Come On Over	5.75	7.25	7.00	nan	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	nan	nan	9.00
48	MT	Fontaines D.C.	Skinty Fia	nan	6.00	nan	4.00	nan	6.00	nan	6.75	6.00
47	WS	Spiritbox	Eternal Blue	nan	7.00	6.25	6.25	nan	6.50	nan	6.75	nan
46	RL	Cœur de pirate	Cœur de pirate	nan	6.50	7.25	nan	nan	6.75	nan	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

Showing 1 to 10 of 54 entries

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 ''))

entries per page

Search:

#	From	Artist	Album	Genre	Year	AVG	MED	MAX	MIN	SUM	STD
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	6.75	6.75	4.00	30.00	1.20
52	MJ	The Naked and Famous	Passive Me, Aggressive You	Indietronica	2010	7.06	7.12	8.00	6.00	28.25	0.83
51	RW	Shania Twain	Come On Over	Country Pop	1997	6.70	7.00	7.25	5.75	33.50	0.60
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	7.35	6.75	9.00	6.25	36.75	1.13
48	MT	Fontaines D.C.	Skinty Fia	Indie Rock	2022	5.75	6.00	6.75	4.00	28.75	1.03
47	WS	Spiritbox	Eternal Blue	Metalcore	2021	6.55	6.50	7.00	6.25	32.75	0.33
46	RL	Cœur de pirate	Cœur de pirate	Pop	2008	6.90	6.75	7.25	6.50	34.50	0.34
45	SF	Sum 41	All Killer No Filler	Pop Punk	2001	7.67	7.50	8.00	7.50	46.00	0.26

Showing 1 to 10 of 54 entries

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	6	5.90	6.88	5.81	4.83	40.29
MJ	6	6.90	7.83	7.00	5.71	41.42
MT	6	6.19	7.00	6.23	5.33	39.42
RL	7	6.18	7.00	6.20	5.29	38.54
RS	5	6.06	7.05	6.30	4.75	48.50
RW	6	6.70	7.42	6.79	5.96	42.38
SF	6	6.07	7.00	6.27	4.71	38.25
TL	6	6.90	8.25	6.83	5.62	43.50
WS	6	6.05	7.25	6.08	4.67	37.08

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	1		6.75	5.75	7.00			7.00	7.25	6.75	6.75	6.88	7.25	5.75	40.50	0.52
1980s	4	6.69	6.67	6.88	7.00		5.75	6.62	6.58	6.62	6.62	6.69	7.44	5.94	46.31	0.53
1990s	5	5.88	6.80	5.95	4.92		5.42	6.25	6.15	5.20	5.93	6.12	6.85	4.60	37.50	0.88
2000s	15	6.17	6.43	5.92	6.85	5.00	6.43	6.78	6.08	6.40	6.39	6.35	7.38	5.33	40.60	0.75
2010s	19	6.12	6.62	5.47	6.51		6.43	6.14	6.37	6.39	6.32	6.46	7.25	5.18	40.64	0.79
2020s	10	6.88	6.75	6.09	5.67		6.28	6.09	6.58	6.71	6.31	6.28	7.42	5.08	41.17	0.84

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")
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)

	Above Average	Exactly Average	Below Average	Closest to Average
Player
JP	14	1	11	9
MJ	33	2	13	15
MT	14	1	33	8
RL	25	1	18	9
RS	0	0	1	0
RW	20	0	28	15
SF	23	1	15	5
TL	25	2	21	12
WS	27	2	19	16

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").style \
                     .background_gradient(cmap='Greens') \
                     .format(precision=2)
show(relationship_matrix.map(lambda x: 'color: transparent; background-color: transparent' if pd.isnull(x) else ''))

	JP	MJ	MT	RL	RS	RW	SF	TL	WS
Judge
JP	nan	6.75	7.25	5.58	6.25	6.38	5.83	6.58	5.50
MJ	6.54	nan	6.38	6.18	6.70	6.92	6.67	6.67	6.96
MT	5.29	7.04	nan	5.89	5.60	6.46	5.17	6.00	5.58
RL	5.67	6.55	5.96	nan	6.30	6.70	6.55	7.79	5.29
RS	nan	nan	nan	5.00	nan	nan	nan	nan	nan
RW	6.00	7.04	5.92	6.25	6.20	nan	6.29	6.67	6.00
SF	5.50	6.81	6.25	6.30	6.50	6.80	nan	6.55	6.10
TL	6.33	6.79	6.00	6.46	5.95	6.88	5.96	nan	6.33
WS	5.88	7.17	6.33	6.14	5.00	6.62	5.79	7.62	nan

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(cmap='Blues') \
                       .format(precision=2),classes="compact")

	AVG			MED			MAX			MIN			SUM			STD
	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min
Round
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	6.71	7.67	5.88	6.75	7.50	5.88	7.56	9.00	6.50	5.81	7.50	4.50	40.19	46.00	32.75	0.69	1.16	0.26
6	6.55	7.35	5.75	6.59	7.12	5.88	7.34	9.00	6.75	5.53	6.50	4.00	32.62	36.75	28.25	0.75	1.20	0.33
7	5.90	5.90	5.90	6.00	6.00	6.00	6.25	6.25	6.25	5.50	5.50	5.50	29.50	29.50	29.50	0.29	0.29	0.29

Players

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

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

	JP			MJ			MT			RL
	mean	max	min	mean	max	min	mean	max	min	mean	max	min
Round
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				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	6.25	8.00	4.00
7				5.75	5.75	5.75	5.50	5.50	5.50

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

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

	RW			SF			TL			WS
	mean	max	min	mean	max	min	mean	max	min	mean	max	min
Round
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	6.58	8.25	5.50	6.68	7.50	5.25	6.71	8.00	4.50
6	6.14	6.75	5.75				6.71	7.25	5.50	6.46	9.00	4.00
7	6.00	6.00	6.00				6.25	6.25	6.25	6.00	6.00	6.00

Visualisations

melted = scores.melt(id_vars=['#'],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()

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()

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()

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()

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: 2025-03-29

The Schedule

Get the Tables!

Album Details

Individual Scores

Summary Scores

Aggregations

Average Scores by Advocate

Average Scores by Decade

Average Proximities

Relationship Matrix

Scores by Round

Aggs

Players

Visualisations

Scores by Judge

Scores by Years and Mins

Scores by Album

Scores by Genre

Scores by Categories