<p>请这样做:</p>
<pre><code>import numpy as np
df1 = pd.read_csv('PersonDate.csv')
df2 = pd.read_csv('useriddate.csv')
df1['datetime'] = pd.to_datetime(df1['datetime'])
df2['datetime'] = pd.to_datetime(df2['datetime'])
df3 = df1.merge(df2.groupby('userid',as_index=False).agg({'datetime' : np.min}), on='userid')
df3[df3["datetime_x"]>=df3["datetime_y"]]
</code></pre>
<p>输出:</p>
<pre><code> userid datetime_x Score datetime_y
5 AB-4243 2016-02-06 76.00 2016-02-06
6 AB-4243 2016-02-07 84.00 2016-02-06
7 AB-4243 2016-02-08 84.00 2016-02-06
8 AB-4243 2016-02-09 81.00 2016-02-06
9 AB-4243 2016-02-10 79.00 2016-02-06
10 NP-7585 2016-02-01 22.00 2016-02-01
11 NP-7585 2016-02-02 23.50 2016-02-01
12 NP-7585 2016-02-03 30.15 2016-02-01
13 NP-7585 2016-02-04 30.15 2016-02-01
14 NP-7585 2016-02-05 30.15 2016-02-01
15 NP-7585 2016-02-06 30.15 2016-02-01
16 NP-7585 2016-02-07 0.00 2016-02-01
17 NP-7585 2016-02-08 0.00 2016-02-01
18 NP-7585 2016-02-09 22.50 2016-02-01
19 NP-7585 2016-02-10 45.67 2016-02-01
29 VX-4376 2016-02-10 33.13 2016-02-10
</code></pre>