<p>可以在连接之前添加“性别”列。你知道吗</p>
<p>我们使用<a href="https://pandas.pydata.org/pandas-docs/stable/categorical.html" rel="nofollow noreferrer">Categorical Data</a>和<code>groupby</code>来计算笛卡尔积。这也会带来性能上的好处。你知道吗</p>
<pre><code>df = pd.concat([df_Male.assign(gender='Male'),
df_Female.assign(gender='Female'),
df_Transgender.assign(gender='Transgender')])
for col in ['gender', 'continent']:
df[col] = df[col].astype('category')
res = df.groupby(['gender', 'continent']).first().fillna(0).astype(int)
print(res)
avg_count_country avg_age
gender continent
Female Africa 60 12
America 0 0
Asia 50 7
Europe 70 0
Male Africa 65 10
America 0 0
Asia 55 5
Europe 75 8
Transgender Africa 40 11
America 80 10
Asia 30 6
Europe 0 0
</code></pre>