獲取數據幀中不同子集的最大值和總和值。同時繪製每個子集

Question

我有一個數據幀，如圖「事件」：

   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 

Q1）如何獲得MODFLOW（CFS）ObsFlow的ObsFlow（CFS）爲每個事件&也總和（最大值cfs）列& ObsVol（f3）列將每個事件轉換爲新的數據幀？

所需的輸出格式：

   DateTime  Peak ModFlow(cfs)  Peak ObsFlow(cfs) Total ModVol(f3) Total ObsVol(f3) 
Event 
Event 1  8/15/2016 15:35   -     -    -     - 
Event 2  8/15/2016 10:05   -     -    -     - 
Event 3  8/15/2016 10:00   -     -    -     - 
Event 4  8/15/2016 9:55   -     -    -     - 
Event 5  8/15/2016 4:10   -     -    -     -  

而且我怎樣才能畫出「事件」數據幀，這樣我得到每個事件獨立的情節？

Answer 1

我想你需要aggregate通過first，max和sum：

df1 = df.groupby(level=0) 
     .agg({'DateTime':'first', 
       'ModFlow(cfs)':'max', 
       'ObsFlow(cfs)':'max', 
       'ModVol(f3)':'sum', 
       'ObsVol(f3)':'sum'}) 

#set order of columns 
df1 = df1.reindex(columns=['DateTime','ModFlow(cfs)','ObsFlow(cfs)', 
          'ModVol(f3)','ObsVol(f3)']) 

df1.columns = ['DateTime','Peak ModFlow(cfs)','Peak ObsFlow(cfs)', 
       'Total ModVol(f3)','Total ObsVol(f3)'] 
print (df1) 
       DateTime Peak ModFlow(cfs) Peak ObsFlow(cfs) \ 
Event                
Event 1 8/15/2016 15:35   0.565016   32.174440 
Event 10 6/23/2016 4:35   0.879257   0.752537 
Event 11 6/10/2016 8:05   0.871517   0.662112 
Event 12 4/26/2016 22:00   3.278638   4.090476 

      Total ModVol(f3) Total ObsVol(f3) 
Event           
Event 1   0.001883   0.352908 
Event 10   0.008617   0.009945 
Event 11   0.010480   0.008791 
Event 12   0.057580   0.057173 

然後，如果需要DataFrame.plot.bar：

df1.plot.bar() 

對於第一DataFrame：

df.groupby(level=0).apply(lambda x: x.plot.bar())