<h3>在外部合并应该可以得到所有的结果:</h3>
<ul>
<li><a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html" rel="nofollow noreferrer">^{<cd1>}</a></li>
<li><code>outer</code>:使用来自两个帧的键的并集,类似于SQL完全外部连接;按字典顺序对键排序。你知道吗</li>
<li>根据您的评论,您需要所有日期,而不仅仅是<code>Expected Output</code>中显示的日期</li>
<li>如果要按<code>date</code>排序,请添加<code>parameter</code>、<code>sort=True</code></li>
</ul>
<pre class="lang-py prettyprint-override"><code>df_exp = pd.merge(df_so2, df_met, on='Date_Time', how='outer')
X_x POC Datum Date_Time Date_GMT Sample.Measurement MDL X_y air_temp_set_1 dew_point_temperature_set_1
1.0 2.0 WGS84 2015-01-01 3:00 01/01/2015 09:00 2.3 0.2 3 35.6 35.6
2.0 2.0 WGS84 2015-01-01 4:00 01/01/2015 10:00 2.5 0.2 4 33.8 33.8
3.0 2.0 WGS84 2015-01-01 5:00 01/01/2015 11:00 2.1 0.2 5 33.2 33.2
4.0 2.0 WGS84 2015-01-01 6:00 01/01/2015 12:00 2.3 0.2 6 33.8 33.8
5.0 2.0 WGS84 2015-01-01 7:00 01/01/2015 13:00 1.1 0.2 7 33.8 33.8
NaN NaN NaN 2015-01-01 1:00 NaN NaN NaN 1 35.6 35.6
NaN NaN NaN 2015-01-01 2:00 NaN NaN NaN 2 35.6 35.6
</code></pre>
<h3>没有来自<code>df_met</code>:</h3>的列
<pre class="lang-py prettyprint-override"><code>df_exp.drop(columns=['X_y', 'air_temp_set_1', 'dew_point_temperature_set_1'], inplace=True)
df_exp.rename(columns={'X_x': 'X'}, inplace=True)
X POC Datum Date_Time Date_GMT Sample.Measurement MDL
1.0 2.0 WGS84 2015-01-01 3:00 01/01/2015 09:00 2.3 0.2
2.0 2.0 WGS84 2015-01-01 4:00 01/01/2015 10:00 2.5 0.2
3.0 2.0 WGS84 2015-01-01 5:00 01/01/2015 11:00 2.1 0.2
4.0 2.0 WGS84 2015-01-01 6:00 01/01/2015 12:00 2.3 0.2
5.0 2.0 WGS84 2015-01-01 7:00 01/01/2015 13:00 1.1 0.2
NaN NaN NaN 2015-01-01 1:00 NaN NaN NaN
NaN NaN NaN 2015-01-01 2:00 NaN NaN NaN
</code></pre>