<p>您可以<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html" rel="nofollow noreferrer">^{<cd1>}</a><code>card_id</code>并使用<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.transform.html" rel="nofollow noreferrer">^{<cd3>}</a>和lambda表达式<code>sum</code>使用<code>status</code>的次数等于<code>Y</code>的<code>num_approved</code>或<code>N</code>的<code>num_of_denied</code>的<a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.eq.html" rel="nofollow noreferrer">^{<cd10>}</a>:</p>
<pre><code>df['num_approved'] = df.groupby('card_id').status.transform(
lambda x: x.eq('Y').sum())
df['num_of_denied'] = df.groupby('card_id').status.transform(
lambda x: x.eq('N').sum())
trn_id card_id status num_approved num_of_denied
0 1 c1 Y 1 0
1 2 c2 Y 1 1
2 3 c2 N 1 1
3 4 c3 Y 2 0
4 5 c3 Y 2 0
</code></pre>