擅长:python、mysql、java
<p>您可以将<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.groupby.GroupBy.transform.html" rel="nofollow">^{<cd1>}</a>与<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html" rel="nofollow">^{<cd2>}</a>一起使用,因为<code>agg</code>或{<cd4>}聚合输出:</p>
<pre><code>print (df['Send_Amount'].astype(str).str[0].astype(int))
0 3
1 1
2 1
3 1
4 6
5 1
6 7
7 1
8 1
9 5
10 4
11 8
12 9
13 1
Name: Send_Amount, dtype: int32
print (df.groupby('Send_Agent')['Send_Amount'].transform(lambda x: x.astype(str).str[0])
.astype(int))
0 3
1 1
2 1
3 1
4 6
5 1
6 7
7 1
8 1
9 5
10 4
11 8
12 9
13 1
Name: Send_Amount, dtype: int32
</code></pre>
<p>如果数字大于<code>9</code>,请使用<code>str[:2]</code>:</p>
^{pr2}$
<p><a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html#transformation" rel="nofollow">Transformation</a>。在</p>