擅长:python、mysql、java
<p>一个潜在的解决方案是使用列表理解。你可能会得到一个速度提升使用熊猫的一些内置功能,但这将使你达到那里</p>
<pre class="lang-py prettyprint-override"><code>#!/usr/bin/env python
import numpy as np
import pandas as pd
df = pd.DataFrame({
0:["reference", "v5001 tech comp", "catenberry", "very different"],
1:["not", "phone", "other", "text"]
})
df["new_column"] = [x if (x[0].lower() == "v") & ("phone" in y.lower())
else np.nan for x,y in df.loc[:, [0,1]].values]
print(df)
</code></pre>
<p>那会产生什么</p>
<pre><code> 0 1 new_column
0 reference not NaN
1 v5001 tech comp phone v5001 tech comp
2 catenberry other NaN
3 very different text NaN
</code></pre>
<p>我所做的就是接受你的两个条件,建立一个新的列表,然后分配给你的新专栏</p>