<p>这是你的熊猫数据框:</p>
<pre><code>import pandas as pd
data = [
['Germany', 1990, 'Aquatics', 'HAJOS, Alfred', 'silver'],
['Germany', 1990, 'Aquatics', 'IRSCHMANN, Otto', 'silver'],
['Germany', 1990, 'Aquatics', 'DRIVAS, Dimitrios', 'silver'],
['US', 2008, 'Athletics', 'MALOKINIS, Ioannis', 'silver'],
['US', 2008, 'Athletics', 'HAJOS, Alfred', 'silver'],
['US', 2009, 'Athletics', 'CHASAPIS, Spiridon', 'gold'],
['France', 2010, 'Athletics', 'CHOROPHAS, Efstathios', 'gold'],
['France', 2010, 'golf', 'HAJOS, Alfred', 'silver'],
['France', 2011, 'golf', 'ANDREOU, Joannis', 'silver']
]
df = pd.DataFrame(data, columns = ['country', 'edition', 'sports', 'Athletes', 'Medals'])
print(df)
country edition sports Athletes Medals
0 Germany 1990 Aquatics HAJOS, Alfred silver
1 Germany 1990 Aquatics IRSCHMANN, Otto silver
2 Germany 1990 Aquatics DRIVAS, Dimitrios silver
3 US 2008 Athletics MALOKINIS, Ioannis silver
4 US 2008 Athletics HAJOS, Alfred silver
5 US 2009 Athletics CHASAPIS, Spiridon gold
6 France 2010 Athletics CHOROPHAS, Efstathios gold
7 France 2010 golf HAJOS, Alfred silver
8 France 2011 golf ANDREOU, Joannis silver
</code></pre>
<p>现在,您只需过滤银牌,然后<code>groupby</code>版(注意<code>'Edition'</code>将抛出<code>KeyError</code>而不是<code>'edition'</code>),最后得到计数:</p>
<pre><code>df[df.Medals == 'silver'].groupby('edition').count()['Medals'].idxmax()
>>> 1990
</code></pre>