<p>使用<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.insert.html" rel="nofollow noreferrer">^{<cd1>}</a>with <a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.where.html" rel="nofollow noreferrer">^{<cd2>}</a>和<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.startswith.html" rel="nofollow noreferrer">^{<cd3>}</a>将不匹配的值替换为缺少的值,使用<code>ffill</code>向前填充缺少的值,然后通过<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ne.html" rel="nofollow noreferrer">^{<cd5>}</a>删除两个值相同的行,用于<a href="http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing" rel="nofollow noreferrer">^{<cd6>}</a>中的不相等:</p>
<pre><code>df.insert(0, 'country', df[0].where(df[0].str.startswith('Country')).ffill())
df = df[df['country'].ne(df[0])].reset_index(drop=True).rename(columns={0:'city'})
print (df)
country city
0 Country|India Delhi
1 Country|India Mumbai
2 Country|India Chennai
3 Country|Italy Rome
4 Country|Italy Venice
5 Country|Italy Milan
6 Country|Australia Sydney
7 Country|Australia Melbourne
8 Country|Australia Perth
</code></pre>