如何计算在Pandas中另一列上分组的平均值

Question

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.

Answer 1

你可以groupby继续StationID然后mean()继续BiasTemp.要输出Dataframe,请使用as_index=False

In [4]: df.groupby('StationID', as_index=False)['BiasTemp'].mean()
Out[4]:
  StationID  BiasTemp
0        BB       5.0
1     KEOPS       2.5
2    SS0279      15.0

没有as_index=False,它返回一个Series代替

In [5]: df.groupby('StationID')['BiasTemp'].mean()
Out[5]:
StationID
BB            5.0
KEOPS         2.5
SS0279       15.0
Name: BiasTemp, dtype: float64

groupby在这个pydata 教程中阅读更多相关内容.

Answer 2

这groupby是为了什么:

In [117]:
df.groupby('StationID')['BiasTemp'].mean()

Out[117]:
StationID
BB         5.0
KEOPS      2.5
SS0279    15.0
Name: BiasTemp, dtype: float64

在这里,我们通过'StationID'列进行分组,然后我们访问'BiasTemp'列并调用mean它

有关此功能的文档中有一节.