小编Raf*_*afa的帖子

对于以下数据帧:

StationID  HoursAhead    BiasTemp  
SS0279           0          10
SS0279           1          20
KEOPS            0          0
KEOPS            1          5
BB               0          5
BB               1          5

我想得到类似的东西:

StationID  BiasTemp  
SS0279     15
KEOPS      2.5
BB         5

我知道我可以编写这样的脚本来获得所需的结果:

def transform_DF(old_df,col):
    list_stations = list(set(old_df['StationID'].values.tolist()))
    header = list(old_df.columns.values)
    header.remove(col)
    header_new = header
    new_df = pandas.DataFrame(columns = header_new)
    for i,station in enumerate(list_stations):
        general_results = old_df[(old_df['StationID'] == station)].describe()
        new_row = []
        for column in header_new:
            if column in ['StationID']: 
                new_row.append(station)
                continue
            new_row.append(general_results[column]['mean'])
        new_df.loc[i] = new_row
    return new_df

但我想知道大熊猫是否有更直接的东西.

任何建议在column=periodo_dia不丢弃任何列的情况下拆散？

原始数据框如下所示:

|   | year | month | day | periodo_dia | valor_medida | Score_recogida |
|---|------|-------|-----|-------------|--------------|----------------|
| 0 | 2015 | 4     | 18  | manana      | 25.0         | 8.166667       |
| 1 | 2015 | 4     | 18  | noche       | 47.5         | 0.000000       |
| 2 | 2015 | 4     | 18  | tarde       | 20.0         | 0.000000       |
| 3 | 2015 | 4     | 19  | manana      | 0.0          | 0.000000       |
| …