SHOW:
|
|
- or go back to the newest paste.
| 1 | import pandas, numpy, StringIO | |
| 2 | ||
| 3 | txt = '''ID,RUN_START_DATE,PUSHUP_START_DATE,SITUP_START_DATE,PULLUP_START_DATE | |
| 4 | 1,2013-01-24,2013-01-02,,2013-02-03 | |
| 5 | 2,2013-01-30,2013-01-21,2013-01-13,2013-01-06 | |
| 6 | 3,2013-01-29,2013-01-28,2013-01-01,2013-01-29 | |
| 7 | 4,2013-02-16,2013-02-12,2013-01-04,2013-02-11 | |
| 8 | 5,2013-01-06,2013-02-07,2013-02-25,2013-02-12 | |
| 9 | 6,2013-01-26,2013-01-28,2013-02-12,2013-01-10 | |
| 10 | 7,2013-01-26,,2013-01-12,2013-01-30 | |
| 11 | 8,2013-01-03,2013-01-24,2013-01-19,2013-01-02 | |
| 12 | 9,2013-01-22,2013-01-13,2013-02-03, | |
| 13 | 10,2013-02-06,2013-01-16,2013-02-07,2013-01-11''' | |
| 14 | df = pandas.read_csv(StringIO.StringIO(txt)) | |
| 15 | Bigdata_date_tofix = [c for c in df.columns if 'DATE' in c] | |
| 16 | df[Bigdata_date_tofix] = df[Bigdata_date_tofix].apply(pandas.to_datetime) | |
| 17 | - | as_indx = df[['RUN_START_DATE', 'PUSHUP_START_DATE', 'SITUP_START_DATE', 'PULLUP_START_DATE']].apply(numpy.argsort, axis=1) |
| 17 | + | as_indx = df[['RUN_START_DATE', 'PUSHUP_START_DATE', 'SITUP_START_DATE', 'PULLUP_START_DATE']].apply(numpy.argsort, axis=1) |
| 18 | ||
| 19 | ||
| 20 | ||
| 21 | ############# RESULT ##################### | |
| 22 | ||
| 23 | ''' | |
| 24 | ||
| 25 | RUN_START_DATE PUSHUP_START_DATE SITUP_START_DATE \ | |
| 26 | 0 1970-01-01 00:00:00 1970-01-01 00:00:00 2262-04-10 00:12:43.145224 | |
| 27 | 1 3 2 1 | |
| 28 | 2 2 1 0 | |
| 29 | 3 2 3 1 | |
| 30 | 4 0 1 3 | |
| 31 | 5 3 0 1 | |
| 32 | 6 1970-01-01 00:00:00 2262-04-10 00:12:43.145224 1970-01-01 00:00:00 | |
| 33 | 7 3 0 2 | |
| 34 | 8 1970-01-01 00:00:00 1970-01-01 00:00:00 1970-01-01 00:00:00 | |
| 35 | 9 3 1 0 | |
| 36 | ||
| 37 | PULLUP_START_DATE | |
| 38 | 0 1970-01-01 00:00:00 | |
| 39 | 1 0 | |
| 40 | 2 3 | |
| 41 | 3 0 | |
| 42 | 4 2 | |
| 43 | 5 2 | |
| 44 | 6 1970-01-01 00:00:00 | |
| 45 | 7 1 | |
| 46 | 8 2262-04-10 00:12:43.145224 | |
| 47 | 9 2 | |
| 48 | ||
| 49 | ''' |
