<p>使用<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.duplicated.html" rel="nofollow noreferrer">^{<cd1>}</a>或<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.duplicated.html" rel="nofollow noreferrer">^{<cd2>}</a>指定列和参数<code>keep='last'</code>,然后将<code>True/False</code>到<code>1/0</code>映射的反向掩码转换为整数,或使用<a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html" rel="nofollow noreferrer">^{<cd6>}</a>:</p>
<pre><code>df['Last_dup1'] = (~df['Policy_id'].duplicated(keep='last')).astype(int)
df['Last_dup1'] = np.where(df['Policy_id'].duplicated(keep='last'), 0, 1)
</code></pre>
<p>或:</p>
<pre><code>df['Last_dup1'] = (~df.duplicated(subset=['Policy_id'], keep='last')).astype(int)
df['Last_dup1'] = np.where(df.duplicated(subset=['Policy_id'], keep='last'), 0, 1)
</code></pre>
<hr/>
<pre><code>print (df)
Id Policy_id Start_Date Last_dup Last_dup1
0 0 b123 2019/02/24 0 0
1 1 b123 2019/03/24 0 0
2 2 b123 2019/04/24 1 1
3 3 c123 2018/09/01 0 0
4 4 c123 2018/10/01 1 1
5 5 d123 2017/02/24 0 0
6 6 d123 2017/03/24 1 1
</code></pre>