Separating a dataframe into multiple dataframes based on the index value in pandas

Question

I have a df as shown below:

             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

Similarly upto Event 47

I want to have a seperate dataframe for each event. Expected output would look like:

Event1_df:

            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 1 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

Event2_df:

           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

Similarly for other events

How could i possibly do it?

Zero · Accepted Answer · 2017-02-07 13:44:03Z

5

Since, Event is the index, you could use loc to extract conents and place them in dict.

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}

Details:

In [489]: df.index.unique()
Out[489]: array(['Event1', 'Event10', 'Event11', 'Event12'], dtype=object)

In [487]: df_split = {x: df.loc[x]  for x in df.index.unique()}

In [488]: df_split['Event1']
Out[488]:
         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

edited Feb 7, 2017 at 13:44

answered Feb 7, 2017 at 13:42

Zero

77.4k22 gold badges154 silver badges154 bronze badges

Sign up to request clarification or add additional context in comments.

4 Comments

nish Over a year ago

Thank you.. can i get this into a tabular form?

Zero Over a year ago

dataframe is a tabular form, did you try out the solution?

nish Over a year ago

how can i export this and save it in .csv?

Zero Over a year ago

Hint: Search for pd.to_csv

James · Accepted Answer · 2017-02-07 13:47:47Z

1

You won't be able to assign them to variable names without hard-coding in those names. You can, however, create a dictionary that stores a key to each dataframe. In this case a dictionary comprehension:

> event_dict = {event: df.loc[event] for event in df.index.unique()}

> event_dict['Event1']
# returns 
          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

answered Feb 7, 2017 at 13:47

James

37k4 gold badges54 silver badges79 bronze badges

1 Comment

nish Over a year ago

Thank you. I have another question. If i have to obtain maximum value of ModFlow(cfs) and ObsFlow(cfs) of each event and also, sum of ModVol(f3) and ObsVol(f3) for each event into a another dataframe, is that possible?

Collectives™ on Stack Overflow

Separating a dataframe into multiple dataframes based on the index value in pandas

2 Answers 2

4 Comments

1 Comment

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

2 Answers 2

4 Comments

1 Comment

Your Answer

Sign up or log in

Post as a guest

Related