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- STATION DATE ELEM VALUE
- 0 US1MNCV0008 20170101 PRCP 0
- 1 US1MNCV0008 20170101 SNOW 0
- 2 US1MISW0005 20170101 PRCP 0
- 3 US1MISW0005 20170101 SNOW 0
- 4 US1MISW0005 20170101 SNWD 0
- STATION DATE ELEM VALUE ELEM VALUE ELEM VALUE
- 0 US1MNCV0008 20170101 PRCP 0 SNOW 0
- 1 US1MISW0005 20170101 PRCP 0 SNOW 0 SNWD 0
- weather.groupby(['station', as_index=False).agg(lambda x: x.tolist())
- df = (df.set_index(['STATION','DATE', df.groupby(['STATION','DATE']).cumcount()])
- .unstack()
- .sort_index(axis=1, level=1))
- df.columns = ['{}_{}'.format(i, j) for i, j in df.columns]
- df = df.reset_index()
- print (df)
- STATION DATE ELEM_0 VALUE_0 ELEM_1 VALUE_1 ELEM_2 VALUE_2
- 0 US1MISW0005 20170101 PRCP 0.0 SNOW 0.0 SNWD 0.0
- 1 US1MNCV0008 20170101 PRCP 0.0 SNOW 0.0 NaN NaN
- df = (df.groupby(['STATION','DATE'])['ELEM','VALUE']
- .apply(lambda x: pd.DataFrame(x.values, columns=x.columns))
- .unstack()
- .sort_index(axis=1, level=1))
- df.columns = ['{}_{}'.format(i, j) for i, j in df.columns]
- df = df.reset_index()
- print (df)
- STATION DATE ELEM_0 VALUE_0 ELEM_1 VALUE_1 ELEM_2 VALUE_2
- 0 US1MISW0005 20170101 PRCP 0 SNOW 0 SNWD 0
- 1 US1MNCV0008 20170101 PRCP 0 SNOW 0 NaN NaN
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