基于pandas中的索引值将一个数据帧分成多个数据帧问题的回答

基于pandas中的索引值将一个数据帧分成多个数据帧

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我有一个df，如下所示： <pre><code> DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) ObsVol(f3) Event Event 1 8/15/2016 15:35 11.85926 0 0.039530867 0 Event 1 8/15/2016 10:05 30.05923 0 0.100197433 0 Event 1 8/15/2016 10:00 31.10118 0 0.1036706 0 Event 1 8/15/2016 9:55 32.17444 0 0.107248133 0 Event 1 8/15/2016 4:10 0.6783166 0.5650155 0.002261055 0.001883385 Event 10 6/23/2016 4:35 0.5573569 0.4814242 0.001857856 0.001604747 Event 10 6/23/2016 4:40 0.5536903 0.3544892 0.001845634 0.001181631 Event 10 6/23/2016 4:45 0.5502114 0.368421 0.001834038 0.00122807 Event 10 6/23/2016 4:50 0.5698021 0.501548 0.00189934 0.001671827 Event 10 6/23/2016 4:55 0.7525368 0.879257 0.002508456 0.002930857 Event 11 6/10/2016 8:05 0.6593155 0.6145511 0.002197718 0.002048504 Event 11 6/10/2016 8:10 0.6621117 0.8405573 0.002207039 0.002801858 Event 11 6/10/2016 8:15 0.6578091 0.8173375 0.002192697 0.002724458 Event 11 6/10/2016 8:20 0.6581948 0.871517 0.002193983 0.002905057 Event 12 4/26/2016 22:00 2.307288 2.588235 0.00769096 0.00862745 Event 12 4/26/2016 22:05 2.366998 3.091331 0.007889993 0.010304437 Event 12 4/26/2016 22:10 2.494073 3.278638 0.008313577 0.010928793 Event 12 4/26/2016 22:15 2.746868 3.083591 0.009156227 0.010278637 Event 12 4/26/2016 22:20 3.146326 2.877709 0.010487753 0.009592363 Event 12 4/26/2016 22:30 4.090476 2.354489 0.01363492 0.007848297 </code></pre> 类似于47号事件 我想为每个事件有一个单独的数据帧。预期产出如下： 事件1_df： ^{pr2}$ 事件2_df： <pre><code> DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) Obs Vol(f3) Event 8/15/2016 15:35 11.85926 0 0.039530867 0 8/15/2016 10:05 30.05923 0 0.100197433 0 Event 2 8/15/2016 10:00 31.10118 0 0.1036706 0 8/15/2016 9:55 32.17444 0 0.107248133 0 8/15/2016 4:10 0.6783166 0.5650155 0.002261055 0.001883385 </code></pre> 其他事件也一样 我怎么可能做到呢？在

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因为，<code>Event</code>是索引，所以可以使用<code>loc</code>来提取内容并将它们放入<code>dict</code>。在 <pre><code>In [482]: {x: df.loc[x] for x in df.index.unique()} Out[482]: {'Event1': DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) ObsVol(f3) Event Event1 8/15/2016 11.859260 0.000000 0.039531 0.000000 Event1 8/15/2016 30.059230 0.000000 0.100197 0.000000 Event1 8/15/2016 31.101180 0.000000 0.103671 0.000000 Event1 8/15/2016 32.174440 0.000000 0.107248 0.000000 Event1 8/15/2016 0.678317 0.565016 0.002261 0.001883, 'Event10': DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) ObsVol(f3) Event Event10 6/23/2016 0.557357 0.481424 0.001858 0.001605 Event10 6/23/2016 0.553690 0.354489 0.001846 0.001182 Event10 6/23/2016 0.550211 0.368421 0.001834 0.001228 Event10 6/23/2016 0.569802 0.501548 0.001899 0.001672 Event10 6/23/2016 0.752537 0.879257 0.002508 0.002931, 'Event11': DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) ObsVol(f3) Event Event11 6/10/2016 0.659315 0.614551 0.002198 0.002049 Event11 6/10/2016 0.662112 0.840557 0.002207 0.002802 Event11 6/10/2016 0.657809 0.817338 0.002193 0.002724 Event11 6/10/2016 0.658195 0.871517 0.002194 0.002905, 'Event12': DateTime ModFlow(cfs) ObsFlow(cfs) ModVol(f3) ObsVol(f3) Event Event12 4/26/2016 2.307288 2.588235 0.007691 0.008627 Event12 4/26/2016 2.366998 3.091331 0.007890 0.010304 Event12 4/26/2016 2.494073 3.278638 0.008314 0.010929 Event12 4/26/2016 2.746868 3.083591 0.009156 0.010279 Event12 4/26/2016 3.146326 2.877709 0.010488 0.009592 Event12 4/26/2016 4.090476 2.354489 0.013635 0.007848} </code></pre> 详细信息： ^{pr2}$

基于pandas中的索引值将一个数据帧分成多个数据帧

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