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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=(5,9),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','RS','SF']
album_count = len(images) + 1
current_turn = (album_count % 9) - 2
order = order[current_turn:] + order[:current_turn]
started = datetime.datetime(2024, 2, 5).date()
time_elapsed = datetime.timedelta(weeks=album_count+3)
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])
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 Album Artist 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 Album Artist JP MJ MT RL RS RW SF TL WS
38 RL Worlds Porter Robinson nan nan 7.75 nan nan 7.25 nan 7.50 nan
37 SF Dance Like Nobody's Watching Suburban Legends nan 6.00 5.00 nan nan 6.75 nan 5.00 5.75
36 MJ GALORE House of Protection nan nan 7.75 nan nan 7.50 nan 6.50 7.00
35 RW Jolene Dolly Parton nan 6.75 5.75 nan nan nan nan 7.25 6.75
34 JP Elevator Days The Backseat Lovers nan 6.25 6.25 nan nan 6.25 6.50 6.00 6.75
33 TL Seven Ways to Scream Your Name Funeral for a Friend nan 6.75 6.50 nan nan 6.75 7.25 nan 7.75
32 RS They Liked You Better When You Were Dead Fightstar nan 6.50 5.75 7.25 nan 6.00 6.25 6.50 6.00
31 MT Hideout Parcels nan 6.50 nan 6.25 nan 5.75 6.50 5.75 6.25
30 WS Belong ╪ Betray Devil Sold His Soul nan 6.75 6.25 7.75 nan 6.25 7.00 8.00 nan
29 RL Stick Up Kids Bad Rabbits nan 6.50 5.75 nan nan 6.75 6.75 5.50 5.50
28 SF Foo Fighters Foo Fighters nan 7.50 4.50 5.50 nan 6.00 nan 7.00 6.50
27 RS truANT Alien Ant Farm nan 6.50 7.00 6.00 nan 5.75 6.00 5.50 4.50
26 MJ Remain in Light Talking Heads 7.00 nan 7.00 6.25 nan 6.00 6.50 7.50 7.25
25 RW Vertigo Griff 6.50 6.50 5.50 7.50 nan nan 6.25 6.50 6.00
24 JP Bando Stone & The New World Childish Gambino nan 6.75 6.00 4.50 nan 6.25 6.00 7.75 7.75
23 TL Giants Chicane 7.00 6.00 5.50 8.00 nan 6.50 5.50 nan 7.75
22 MT Geography Tom Misch 6.75 6.50 nan 6.75 nan 6.00 6.75 6.00 7.25
21 WS Take Me Back to Eden Sleep Token nan 7.00 5.25 5.50 nan 6.00 5.25 4.50 nan
20 RL Surfing with the Alien Joe Satriani nan 6.00 7.00 nan nan 5.25 5.75 4.75 5.00
19 RS Earth vs the Wildhearts The Wildhearts 6.00 6.25 5.75 4.50 nan 4.50 6.00 4.00 4.00
18 RS The Surface Beartooth 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 nan 6.25 3.75 6.50 nan 6.00 nan 5.75 6.00
16 MJ Our Own House MisterWives 6.50 nan 5.75 8.25 nan 8.00 6.25 6.75 7.50
15 RW Throwing Copper Live 5.75 6.25 5.75 4.75 nan nan 6.50 6.00 5.00
14 JP Hollywood's Bleeding Post Malone nan 5.75 4.25 5.25 nan 6.00 4.75 5.25 4.50
13 TL Art Angels Grimes 6.00 7.25 6.00 8.50 nan 7.50 4.75 nan 7.50
12 MT Joy as an Act of Resistance IDLES 7.00 6.75 nan 5.25 nan 5.75 6.75 6.50 6.50
11 WS Radical Every Time I Die 6.25 7.00 6.75 3.00 nan 5.75 6.50 7.50 nan
10 RL ( ) Sigur Rós nan 5.75 3.00 nan nan 5.25 6.50 7.50 6.50
9 SF Hand Built by Robots Newton Faulkner 5.50 6.00 3.50 6.50 nan 5.75 nan 3.75 4.50
8 RS The Hunting Party Linkin Park 5.00 6.75 3.00 6.25 nan 6.75 6.75 6.75 3.00
7 MJ Fire Electric Six 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 Mr. Morale & the Big Steppers Kendrick Lamar nan 7.00 4.75 5.00 nan 4.75 4.25 6.75 6.00
4 TL The Colour of Spring Talk Talk 6.75 6.50 5.50 7.25 nan 6.00 7.00 nan 6.75
3 MT Volcano Jungle 8.00 6.00 nan 7.00 nan 5.75 5.75 5.75 6.75
2 WS Fever Daydream The Black Queen 4.75 6.75 3.00 4.25 nan 6.00 5.50 5.25 nan
1 RL Before the Dawn Heals Us M83 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 Album Artist Genre Year AVG MED MAX MIN SUM STD
38 RL Worlds Porter Robinson Electronic 2014 7.50 7.50 7.75 7.25 22.50 0.25
37 SF Dance Like Nobody's Watching Suburban Legends Pop Rock 2006 5.70 5.75 6.75 5.00 28.50 0.74
36 MJ GALORE House of Protection Rock 2024 7.19 7.25 7.75 6.50 28.75 0.55
35 RW Jolene Dolly Parton Country 1974 6.62 6.75 7.25 5.75 26.50 0.63
34 JP Elevator Days The Backseat Lovers Rock 2018 6.33 6.25 6.75 6.00 38.00 0.26
33 TL Seven Ways to Scream Your Name Funeral for a Friend Post-hardcore 2003 7.00 6.75 7.75 6.50 35.00 0.50
32 RS They Liked You Better When You Were Dead Fightstar Rock 2005 6.32 6.25 7.25 5.75 44.25 0.49
31 MT Hideout Parcels Pop 2017 6.17 6.25 6.50 5.75 37.00 0.34
30 WS Belong ╪ Betray Devil Sold His Soul Metalcore 2014 7.00 6.88 8.00 6.25 42.00 0.74
29 RL Stick Up Kids Bad Rabbits Pop 2009 6.12 6.12 6.75 5.50 36.75 0.61
28 SF Foo Fighters Foo Fighters Rock 1995 6.17 6.25 7.50 4.50 37.00 1.08
27 RS truANT Alien Ant Farm Rock 2003 5.89 6.00 7.00 4.50 41.25 0.79
26 MJ Remain in Light Talking Heads New Wave 1980 6.79 7.00 7.50 6.00 47.50 0.55
25 RW Vertigo Griff Pop 2024 6.39 6.50 7.50 5.50 44.75 0.61
24 JP Bando Stone & The New World Childish Gambino Hip Hop 2024 6.43 6.25 7.75 4.50 45.00 1.13
23 TL Giants Chicane Dance 2010 6.61 6.50 8.00 5.50 46.25 1.02
22 MT Geography Tom Misch Alternative 2018 6.57 6.75 7.25 6.00 46.00 0.45
21 WS Take Me Back to Eden Sleep Token Metal 2023 5.58 5.38 7.00 4.50 33.50 0.85
20 RL Surfing with the Alien Joe Satriani Rock 1987 5.62 5.50 7.00 4.75 33.75 0.82
19 RS Earth vs the Wildhearts The Wildhearts Rock 1993 5.12 5.12 6.25 4.00 41.00 0.96
18 RS The Surface Beartooth Metalcore 2023 7.28 7.50 8.00 6.50 58.25 0.49
17 SF As December Falls As December Falls Rock 2019 5.71 6.00 6.50 3.75 34.25 0.99
16 MJ Our Own House MisterWives Pop Rock 2015 7.00 6.75 8.25 5.75 49.00 0.94
15 RW Throwing Copper Live Rock 1994 5.71 5.75 6.50 4.75 40.00 0.64
14 JP Hollywood's Bleeding Post Malone Hip Hop 2019 5.11 5.25 6.00 4.25 35.75 0.64
13 TL Art Angels Grimes Pop 2015 6.79 7.25 8.50 4.75 47.50 1.26
12 MT Joy as an Act of Resistance IDLES Punk 2018 6.36 6.50 7.00 5.25 44.50 0.63
11 WS Radical Every Time I Die Metalcore 2021 6.11 6.50 7.50 3.00 42.75 1.48
10 RL ( ) Sigur Rós Post Rock 2002 5.75 6.12 7.50 3.00 34.50 1.55
9 SF Hand Built by Robots Newton Faulkner Pop Rock 2007 5.07 5.50 6.50 3.50 35.50 1.16
8 RS The Hunting Party Linkin Park Rock 2014 5.53 6.50 6.75 3.00 44.25 1.67
7 MJ Fire Electric Six 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 Mr. Morale & the Big Steppers Kendrick Lamar Hip Hop 2022 5.50 5.00 7.00 4.25 38.50 1.08
4 TL The Colour of Spring Talk Talk New Wave 1986 6.54 6.75 7.25 5.50 45.75 0.60
3 MT Volcano Jungle Electronic 2023 6.43 6.00 8.00 5.75 45.00 0.86
2 WS Fever Daydream The Black Queen Electronic 2016 5.07 5.25 6.75 3.00 35.50 1.22
1 RL Before the Dawn Heals Us M83 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 4 5.84 6.88 5.69 4.75 39.31
MJ 4 6.98 8.00 7.00 5.75 43.44
MT 4 6.38 7.19 6.38 5.69 43.12
RL 5 6.16 7.10 6.25 5.10 34.80
RS 5 6.03 7.05 6.27 4.75 45.80
RW 4 6.55 7.31 6.62 5.81 40.94
SF 4 5.66 6.81 5.88 4.19 33.81
TL 4 6.74 7.88 6.81 5.56 43.62
WS 4 5.94 7.31 6.00 4.19 38.44

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 7.25 6.75 6.62 6.75 7.25 5.75 26.50 0.63
1980s 4 7.08 6.67 6.88 7.00 5.75 6.62 6.50 6.62 6.61 6.69 7.44 5.88 44.88 0.55
1990s 3 5.88 6.67 5.33 4.92 5.25 6.25 5.67 5.17 5.67 5.71 6.75 4.42 39.33 0.89
2000s 9 6.00 6.25 5.56 6.12 5.00 6.36 6.57 5.81 5.94 6.07 6.17 7.17 4.83 38.97 0.84
2010s 13 6.14 6.50 5.15 6.64 6.46 6.09 6.32 6.30 6.29 6.43 7.23 5.12 40.19 0.80
2020s 8 6.88 6.82 6.07 5.71 6.29 5.93 6.53 6.83 6.36 6.30 7.56 5.06 42.06 0.88

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 11 1 7 8
MJ 24 2 7 10
MT 10 0 24 4
RL 16 1 11 8
RS 0 0 1 0
RW 14 0 20 6
SF 18 1 12 6
TL 17 1 16 7
WS 21 1 11 10

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 5.75 5.92 6.58 5.50 6.58 5.50
MJ 6.44 nan 6.44 6.06 6.70 6.75 6.44 6.62 6.88
MT 5.31 7.25 nan 5.70 5.60 6.25 4.19 5.88 5.31
RL 4.92 6.42 6.31 nan 6.30 6.58 6.17 7.92 5.12
RS nan nan nan 5.00 nan nan nan nan nan
RW 5.81 7.31 5.81 6.20 6.20 nan 6.12 6.69 6.00
SF 5.38 6.67 6.44 6.25 6.50 6.67 nan 6.12 6.06
TL 6.44 6.81 6.00 6.30 5.95 6.75 5.38 nan 6.31
WS 6.25 7.19 6.69 5.75 5.00 6.31 5.69 7.44 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 6.27 7.50 5.12 7.20 8.50 6.00 4.50 6.50 3.00 42.75 58.25 34.25 0.96 1.55 0.49
3 6.23 6.79 5.58 6.24 7.00 5.38 7.39 8.00 7.00 5.08 6.00 4.50 41.67 47.50 33.50 0.81 1.13 0.45
4 6.60 7.50 5.70 6.58 7.50 5.75 7.25 8.00 6.50 6.03 7.25 5.00 33.92 44.25 22.50 0.51 0.74 0.25

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 6.32 7.00 5.75 6.53 7.50 5.75 5.28 6.75 3.00 5.94 8.50 3.00
3 6.81 7.00 6.50 6.59 7.50 6.00 5.97 7.00 4.50 6.25 8.00 4.50
4 6.50 6.75 6.00 6.31 7.75 5.00 7.08 7.75 6.25 
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 6.19 7.75 4.50 6.50 7.75 4.50
4 6.58 7.50 5.75 6.71 7.25 6.25 6.44 8.00 5.00 6.47 7.75 5.50

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 Round

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 / Round')
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 / Round')
plt.show()

Scores by Genre

plt.figure(figsize=(16,3))
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()