Uncovering Nobel Prize Trends: Gender, Country, and Age Insights with Python

Recently, Nobel laureates have sparked discussion; I performed a data analysis of historical Nobel Prize winners and share the findings.

Nobel Prize (Swedish: Nobelpriset, English: Nobel Prize) refers to the five awards established by Alfred Nobel's 1895 will, including Physics , Chemistry , Peace , Physiology or Medicine , and Literature . An additional Economic Sciences prize was created by the Swedish Central Bank in 1968. The Nobel Prizes honor outstanding contributions to society in these fields.

1. Import Data

#获取获奖信息数据
import pandas as

data_date   = pd.read_csv('nobel_prizes_by_date.csv',encoding = 'ISO-8859-1')
data_winner = pd.read_csv('nobel_prize_by_winner.csv',encoding = 'ISO-8859-1')
data_winner.head()

2. Gender Distribution Statistics

#去重后获奖者总人数

data.id.nunique()

Output: 901

def make_autopct(values):
    def my_autopct(pct):
        total = sum(values)
        val = int(round(pct*total/100.0))
        # 同时显示数值和占比的饼图
        return '{p:.2f}%  ({v:d}人)'.format(p=pct,v=val)
    return my_autopct

# 长8英寸 宽6英寸，该窗口的分辨率为80
plt.figure(figsize=(8,6), dpi=80)

labels = '男','女','组织'
explode = (0,0,0)  # 将某一块分割出来，值越大分割出的间隙越大
colors = ['yellowgreen','lightskyblue','yellow']

patches,text1,text2 = plt.pie([male,female,org],
                              explode=explode,
                              labels=labels,
                              colors=colors,
                              labeldistance = 1.1,
                              autopct = make_autopct([male,female,org]),
                              shadow = True,
                              startangle = 30,
                              pctdistance = 0.7)
plt.title('获奖者男女比例',fontsize=19)
plt.legend()
plt.show()

3. Country-wise Award Statistics

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))

#添加数据，设置饼图半径
pie.add('',[list(z) for z in zip(data_cou.head(15).index.tolist(), data_cou.head(15).values.tolist())],
        radius=['40%','65%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

4. Field-wise Award Proportion Statistics

# 长16英寸 宽8英寸，该窗口的分辨率为80
plt.figure(figsize=(16,8), dpi=80)

def make_autopct(values):
    def my_autopct(pct):
        total = sum(values)
        val = int(round(pct*total/100.0))
        return '{p:.2f}%  ({v:d}人)'.format(p=pct,v=val)
    return my_autopct

patches,l_text,p_text = plt.pie(data_cate.values,
                                 labeldistance = 1.1,
                                 labels=data_cate.index,
                                 startangle = 60,
                                 pctdistance = 0.7,
                                 autopct=make_autopct(data_cate.values))
for t in l_text:
    t.set_size(12)
for t in p_text:
    t.set_size(12)
plt.title('各领域获奖人数',fontsize=15)
plt.legend(loc=2)
plt.show()

5. Top 10 Countries per Award Category

1. Physiology or Medicine

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_med.head(10).index.tolist(), data_med.head(10).values.tolist())],
        radius=['30%','60%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

2. Physics

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_phy.head(10).index.tolist(), data_phy.head(10).values.tolist())],
        radius=['30%','60%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

3. Chemistry

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_che.head(10).index.tolist(), data_che.head(10).values.tolist())],
        radius=['30%','60%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

4. Literature

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_lit.head(10).index.tolist(), data_lit.head(10).values.tolist())],
        radius=['30%','60%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

5. Economic Sciences

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_eco.head(10).index.tolist(), data_eco.head(10).values.tolist())],
        radius=['45%','75%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

6. Peace

#实例化Pie类
pie = Pie(init_opts=opts.InitOpts(width='800px',height='550px'))
pie.add('',[list(z) for z in zip(data_pea.head(10).index.tolist(), data_pea.head(10).values.tolist())],
        radius=['30%','60%'],
        rosetype='radius')
pie.set_global_opts(title_opts=opts.TitleOpts(title=''),
                   legend_opts=opts.LegendOpts(is_show=True))
pie.render_notebook()

6. Average Age of Laureates

df.plot(kind='bar',figsize=(10,6))

plt.title('各领域获奖者获奖年龄分布',fontsize=17)
plt.ylabel('获奖年龄',fontsize=14)
plt.xlabel('获奖领域',fontsize=14)
plt.xticks(rotation=45,fontsize=14)

plt.legend(loc=9,bbox_to_anchor=(0.86,0.96))

This article is intended solely for code learning and sharing; please do not overuse web scraping.