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What would be a way to have put rows with same time_req together and not grouped according to ErrorCode. Currently I am gettting all 0 ErrorCode report and then all Errocode 1 report like below

>>> data.groupby([data['ErrorCode'], pd.Grouper(freq='15T')])['latency'].describe().unstack().reset_index() ErrorCode Time_req count mean std \ 0 0 2017-03-08 04:30:00 1 603034.000000 NaN 1 0 2017-03-08 04:45:00 2 174720.000000 38101.741797 2 0 2017-03-08 05:00:00 2 674942.500000 786118.185810 3 0 2017-03-08 07:45:00 10 266653.200000 165867.496817 4 0 2017-03-08 08:00:00 23 208949.304348 124902.942685 5 0 2017-03-08 08:15:00 31 247282.064516 181780.519320 6 0 2017-03-08 08:30:00 35 249332.857143 340084.918015 7 0 2017-03-08 08:45:00 7 250066.000000 195051.871617 8 1 2017-03-08 04:45:00 4 227747.500000 148185.181566 9 1 2017-03-08 05:00:00 2 126633.000000 1337.846030 10 1 2017-03-08 07:45:00 10 421781.900000 464249.118555 11 1 2017-03-08 08:00:00 22 188122.272727 82110.336132 12 1 2017-03-08 08:15:00 32 294896.968750 229498.560222 13 1 2017-03-08 08:30:00 35 501679.628571 1353873.878385 14 1 2017-03-08 08:45:00 6 531606.000000 582290.903396

But I need alternate something like below

ErrorCode Time_req count 0 2017-03-08 04:30:00 1 1 NaN NaN NaN 0 2017-03-08 04:45:00 2 1 2017-03-08 04:45:00 4 AND SO ON
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1 Answer 1

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I think you need stack with unstack for add missing values:

df = data.groupby([data['ErrorCode'], pd.Grouper(freq='15T')])['latency'].describe() df = df.unstack(0).stack(dropna=False).unstack(1).reset_index() print (df) Time_req ErrorCode count mean std 0 2017-03-08 04:30:00 0 1.0 603034.000000 NaN 1 2017-03-08 04:30:00 1 NaN NaN NaN 2 2017-03-08 04:45:00 0 2.0 174720.000000 3.810174e+04 3 2017-03-08 04:45:00 1 4.0 227747.500000 1.481852e+05 4 2017-03-08 05:00:00 0 2.0 674942.500000 7.861182e+05 5 2017-03-08 05:00:00 1 2.0 126633.000000 1.337846e+03 6 2017-03-08 07:45:00 0 10.0 266653.200000 1.658675e+05 7 2017-03-08 07:45:00 1 10.0 421781.900000 4.642491e+05 8 2017-03-08 08:00:00 0 23.0 208949.304348 1.249029e+05 9 2017-03-08 08:00:00 1 22.0 188122.272727 8.211034e+04 10 2017-03-08 08:15:00 0 31.0 247282.064516 1.817805e+05 11 2017-03-08 08:15:00 1 32.0 294896.968750 2.294986e+05 12 2017-03-08 08:30:00 0 35.0 249332.857143 3.400849e+05 13 2017-03-08 08:30:00 1 35.0 501679.628571 1.353874e+06 14 2017-03-08 08:45:00 0 7.0 250066.000000 1.950519e+05 15 2017-03-08 08:45:00 1 6.0 531606.000000 5.822909e+05
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5 Comments

Can this be written to a file in "wide" way without \ and new row
I think df.to_csv('my_file.csv', index=False) works perfect.
not a csv, but what I see in my python terminal in a wide way. Thanks
I think you need pd.set_option('expand_frame_repr', False) - check here.
I have email in my profile. But please accept my solutions - To mark an answer as accepted, click on the check mark beside the answer to toggle it from greyed out to filled in. Thank you.

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