TidyTuesday dataset of 2025-10-28

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

prizes = pd.read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-10-28/prizes.csv')

prizes

prizes['prize_genre']=prizes['prize_genre'].str.capitalize()

prizes.groupby(['prize_genre','highest_degree']).count()['prize_name'].sort_values(ascending=False)

prize_genre highest_degree
Fiction Bachelors 147
Masters 144
unknown 75
Non-fiction Doctorate 59
Fiction Doctorate 52
Crime unknown 46
Poetry Bachelors 43
Non-fiction unknown 33
Bachelors 30
Crime Bachelors 30
Poetry unknown 25
Fiction none 24
Poetry Masters 23
Doctorate 19
No/any/multi genre Bachelors 17
Non-fiction Masters 15
Children's unknown 14
Crime Masters 12
Sff unknown 12
Biography unknown 11
Crime none 10
Children's Bachelors 10
Poetry none 9
Biography Bachelors 9
Crime Doctorate 9
Sff Masters 7
No/any/multi genre unknown 7
Biography Doctorate 7
Sff Bachelors 6
Doctorate 5
Drama unknown 4
Bachelors 4
No/any/multi genre Doctorate 4
Non-fiction none 3
Children's Masters 3
Sff none 3
Fiction Postgraduate 3
Children's Doctorate 2
Biography Masters 2
No/any/multi genre Masters 2
Fiction Juris Doctor 2
Biography Diploma 1
Children's Postgraduate 1
Biography Postgraduate 1
Poetry MD 1
Postgraduate 1
Biography Juris Doctor 1
Non-fiction Juris Doctor 1
Children's none 1
No/any/multi genre none 1
Fiction Certificate of Education 1
Name: prize_name, dtype: int64

prizes.groupby(['degree_field_category']).count()['prize_name'].sort_values(ascending=False)

degree_field_category
Language and Literature 272
unknown 265
Writing 114
History and Cultural Studies 72
none 51
Arts 40
Maths and Sciences 34
Multiple 30
Politics and Economics 27
Philosophy and Theology 24
Law 17
Medicine and Social Work 5
Education 1
Name: prize_name, dtype: int64

df = prizes[(~prizes["degree_field_category"].isin(['unknown','none'])) & (prizes["highest_degree"].isin(['Bachelors','Masters', 'Doctorate']))]
df

#fix,ax = plt.subplots(figsize=(10,5))
sns.catplot(data=df, \
            x='prize_year', y='prize_genre', hue='gender', dodge=True, kind='strip', \
            col='highest_degree', col_wrap=2, aspect=2, legend=False, jitter=True, size=10)

pivot_table = df.pivot_table(
    index='prize_genre',
    columns='highest_degree',
    values='prize_name',
    aggfunc='count',
    fill_value=0
)

plt.figure(figsize=(8, 5))
sns.heatmap(pivot_table, annot=True, fmt='d', cmap='YlGnBu', linewidths=0.5)
#plt.title('Tile Plot of Prizes by Degree Field and Degree Level')
#plt.xlabel('Highest Degree')
#plt.ylabel('Degree Field Category')
plt.tight_layout()
plt.show()

prizes['degree'] = prizes['highest_degree'].apply(lambda x: x if x in ['Bachelors', 'Masters', 'Doctorate'] else 'Others')
prizes['gender_new'] = prizes['gender'].apply(lambda x: x if x in ['man', 'woman'] else 'NB')

df1= (prizes.groupby(['prize_genre', 'degree', 'gender_new']).count()['prize_id'].reset_index())
df1['degree_gender'] = df1['degree']+'_'+df1['gender_new']
df1

reshaped = df1.pivot_table(
    index='prize_genre',
    columns='degree_gender',
    values='prize_id',
    aggfunc='sum',  # or 'count' if you want number of entries
    fill_value=0
).reset_index()
reshaped = reshaped.set_index('prize_genre')

ind = prizes.groupby(['prize_genre']).count()['prize_name'].sort_values(ascending=False).index
reshaped.columns

Index(['Bachelors_NB', 'Bachelors_man', 'Bachelors_woman', 'Doctorate_NB',
'Doctorate_man', 'Doctorate_woman', 'Masters_NB', 'Masters_man',
'Masters_woman', 'Others_NB', 'Others_man', 'Others_woman'],
dtype='object', name='degree_gender')

reshaped1 = reshaped[['Bachelors_man', 'Bachelors_woman', 'Masters_man', 'Masters_woman',
       'Doctorate_man', 'Doctorate_woman', 'Others_man', 'Others_woman']]
reshaped1 = reshaped1.reindex(ind)
reshaped1.index = reshaped1.index.str.replace('Sff', 'Sci-Fi and Fantasy')

bg_color = '#DBD4FF'
fg_color = '#723480'
fg_color2 = '#FFFFE3'

lw=5
cmap = sns.color_palette("Wistia", as_cmap=True)
cmap.set_under('#FFFFE3')  

fig1,ax1 = plt.subplots()
sns.heatmap(reshaped1, annot=True, fmt='d', cmap=cmap, cbar=False, \
            linewidths=lw, ax=ax1,vmin=0.1, annot_kws={"fontfamily": "monospace"}, \
            cbar_kws={'shrink': 0.6, 'aspect': 30, 'pad': 0.02})
fig1.set_facecolor(bg_color)
ax1.set_facecolor(bg_color)
ax1.patch.set_facecolor(bg_color)

ax_top = ax1.twiny()

# Match the position and scale of the bottom axis
ax_top.set_xlim(ax1.get_xlim())

ax_top.set_xticks(ax1.get_xticks())
ax_top.set_xticklabels(['M', 'W']*4, fontsize=9)

for i in range(0, len(reshaped1.columns)+1):  
    if(i % 2 == 0):
        ax1.vlines(i, *ax1.get_ylim(), colors=bg_color, linewidth=lw)
    else:
        ax1.vlines(i, *ax1.get_ylim(), colors=fg_color2, linewidth=lw)
for i in range(0, len(reshaped1.index)+1):  
    ax1.hlines(i, *ax1.get_xlim(), colors=bg_color, linewidth=lw)

xticklabels = ax1.get_xticklabels()
new_labels = [label._text.split('_')[0] if i % 2 == 0 else '' for i, label in enumerate(xticklabels)]

# Apply the new labels
ax1.set_xticklabels(new_labels, rotation=0, ha='left', fontsize=10)
ax1.tick_params(axis='both', which='both', length=0)
ax_top.tick_params(axis='both', which='both', length=0, pad=0)
ax1.set_xlabel('')
ax1.set_ylabel('')
#colorbar = ax1.collections[0].colorbar
#colorbar.ax.tick_params(labelsize=10)
for label in ax1.get_xticklabels()+ax_top.get_xticklabels():
    label.set_fontfamily('monospace')
    label.set_color(fg_color)
for label in ax1.get_yticklabels():
    label.set_fontfamily('monospace')
    label.set_color(fg_color)
sns.despine(left=True,bottom=True)    
#plt.tight_layout()
plt.title("Genre-wise distribution of the British literary prizes \nbased on the highest degree and gender of the authors.", fontfamily='Serif', fontsize=16, color=fg_color, loc='left', x=-0.3, y=1.07)
plt.savefig('British_prize.png', dpi=300, bbox_inches='tight')
plt.show()