熊猫数据框中的平均连续2行

Question

我有一个数据集，如下所示

userid time           val1 val2 val3 val4
1      2010-6-1 0:15  12   16   17   11
1      2010-6-1 0:30  11.5 14   15.2 10
1      2010-6-1 0:45  12   14   15   10
1      2010-6-1 1:00  8    11   13   0
.................................
.................................
2      2010-6-1 0:15  14   16   17   11
2      2010-6-1 0:30  11   14   15.2 10
2      2010-6-1 0:45  11   14   15   10
2      2010-6-1 1:00  9    11   13   0
.................................
.................................
3 ...................................
.................................
.................................

我想获得每两行的平均值。预期结果将是

userid time           val1  val2  val3  val4
1      2010-6-1 0:30  11.75 15    16.1  10.5
1      2010-6-1 1:00  10    12.5  14    5
..............................
..............................
2      2010-6-1 0:30  12.5  15    16.1  10.5
2      2010-6-1 1:00  10    12.5  14    5
.................................
.................................
3 ...................................
.................................
.................................

目前我的方法是

data = pd.read_csv("sample_dataset.csv")
i = 0
while i < len(data) - 1:
    x = data.iloc[i:i+2].mean()
    x['time'] = data.iloc[i+1]['time']
    data.iloc[i] = x
    i+=2
for i in range(len(data)):
    if i % 2 != 1:
        del data.iloc[i]

但这是非常低效的。因此，有人可以指出我一种获得预期结果的更好方法吗？在数据集中，我有超过1000000行

Answer 1

我在用 resample

df.set_index('time').resample('30Min',closed = 'right',label ='right').mean()
Out[293]: 
                      val1  val2  val3  val4
time                                        
2010-06-01 00:30:00  11.75  15.0  16.1  10.5
2010-06-01 01:00:00  10.00  12.5  14.0   5.0

方法2

df.groupby(np.arange(len(df))//2).agg(lambda x : x.iloc[-1] if x.dtype=='datetime64[ns]' else x.mean())
Out[308]: 
                 time   val1  val2  val3  val4
0 2010-06-01 00:30:00  11.75  15.0  16.1  10.5
1 2010-06-01 01:00:00  10.00  12.5  14.0   5.0

更新解决方案

df.groupby([df.userid,np.arange(len(df))//2]).agg(lambda x : x.iloc[-1] if x.dtype=='datetime64[ns]' else x.mean()).reset_index(drop=True)