<p>将<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.cat.html" rel="nofollow noreferrer">^{<cd1>}</a>与<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.strip.html" rel="nofollow noreferrer">^{<cd2>}</a>一起使用:</p>
<pre><code>df['new'] = df['Drug'].str.cat(df['Product(Predicted)'], na_rep='', sep='+').str.strip('+')
</code></pre>
<p>或在合并前将缺少的值替换为空字符串:</p>
<pre><code>df['new'] = (df['Drug'].fillna('') + '+' + df['Product(Predicted)']).str.strip('+')
</code></pre>
<hr/>
<pre><code>print (df)
Drug Product(Predicted) new
0 NaN acetaldehyde acetaldehyde
1 NaN nicotine nicotine
2 NaN resin resin
3 NaN rosa rosa
4 NaN eliglustat eliglustat
5 NaN valpromide valpromide
6 NaN clostridium clostridium
7 NaN ambenonium ambenonium
8 solution valpromide solution+valpromide
9 solution valpromide solution+valpromide
</code></pre>
<p>如果要删除<code>drug</code>列中带有<code>NaN</code>的行:</p>
<pre><code>df1 = df.dropna(subset=['Drug'])
df1['new'] = df1['Drug'] + '+' + df1['Product(Predicted)']
print (df)
Drug Product(Predicted) new
8 solution valpromide solution+valpromide
9 solution valpromide solution+valpromide
</code></pre>