擅长:python、mysql、java
<p>您可以将<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.agg.html" rel="nofollow noreferrer">^{<cd1>}</a>与<code>sum</code>if数字列和<code>count</code>if字符串一起使用,然后通过<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.idxmax.html" rel="nofollow noreferrer">^{<cd5>}</a>最大值获得{<cd4>},并与转换为<code>string</code>的{<cd6>}合并,如有必要,最后将{<cd8>}转换为一行<code>DataFrame</code>,使用{a3}并转置:</p>
<pre><code>f = lambda x: x.sum() if np.issubdtype(x.dtype, np.number) else x.count()
df1 = df.groupby('Company').agg(f)
print (df1)
performed Requests Request_Id Num_of_refunds
Company
A 97 66 2 5
B 113 9 3 23
D 94 7 2 8
df2 = (df1.idxmax() + ': ' + df1.max().astype(str)).to_frame().T
print (df2)
performed Requests Request_Id Num_of_refunds
0 B: 113 A: 66 B: 3 B: 23
</code></pre>