擅长:python、mysql、java
<p>使用<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.dt.floor.html" rel="nofollow noreferrer">^{<cd1>}</a>表示删除秒,<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.head.html" rel="nofollow noreferrer">^{<cd2>}</a>表示每个组的第一个值:</p>
<pre><code>#if necessary convert to DatetimeIndex
df.index = pd.to_datetime(df.index)
df1 = df.groupby(df.index.floor('T')).head(1)
print (df1)
Name
2019-07-29 08:07:12.299705088 Olaf
2019-07-29 08:09:41.507259904 Anna
2019-07-29 08:13:02.310900992 Hans
</code></pre>
<p>如果需要随机行,请使用带有<a href="http://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html" rel="nofollow noreferrer">^{<cd3>}</a>的lambda函数:</p>
<pre><code>df2 = df.groupby(df.index.floor('T'), group_keys=False).apply(lambda x: x.sample(1))
print (df2)
Name
2019-07-29 08:07:12.299705088 Olaf
2019-07-29 08:09:41.507259904 Anna
2019-07-29 08:13:02.310900992 Hans
</code></pre>