python – pandas.Series.interpolate()什么都不做.为什么？

我有一个DatetimeIndex的数据框.这是列之一：

>>> y.out_brd
2013-01-01 11:25:00     0.04464286
2013-01-01 11:30:00            NaN
2013-01-01 11:35:00            NaN
2013-01-01 11:40:00    0.005952381
2013-01-01 11:45:00     0.01785714
2013-01-01 11:50:00    0.008928571
Freq: 5T, Name: out_brd, dtype: object

当我试图在函数上使用interpolate()时,绝对没有任何变化：

>>> y.out_brd.interpolate(method='time')
2013-01-01 11:25:00     0.04464286
2013-01-01 11:30:00            NaN
2013-01-01 11:35:00            NaN
2013-01-01 11:40:00    0.005952381
2013-01-01 11:45:00     0.01785714
2013-01-01 11:50:00    0.008928571
Freq: 5T, Name: out_brd, dtype: object

如何使它工作？

更新：
生成这样一个数据帧的代码.

time_index = pd.date_range(start=datetime(2013, 1, 1, 3),
                       end=datetime(2013, 1, 2, 2, 59),
                       freq='5T')
grid_columns = [u'in_brd', u'in_alt', u'out_brd', u'out_alt']                           

df = pd.DataFrame(index=time_index, columns=grid_columns)

之后我用一些数据填充单元格.

我有dataframe field_data,其中包含有关铁路登机和下车以及车站变量的调查数据.
我也有像这样定义的interval_end函数：

interval_end = lambda index, prec_lvl: index.to_datetime() \
                        + timedelta(minutes=prec_lvl - 1,
                                    seconds=59)

代码：

for index, row in df.iterrows():
    recs = field_data[(field_data.station_name == station)
                    & (field_data.arrive_time >= index.time())
                    & (field_data.arrive_time <= interval_end(
                                        index, prec_lvl).time())]
    in_recs_num = recs[recs.orientation == u'in'][u'train_number'].count()
    out_recs_num = recs[recs.orientation == u'out'][u'train_number'].count()

    if in_recs_num:
        df.loc[index, u'in_brd'] = recs[
                recs.orientation == u'in'][u'boarding'].sum()    / \
                (in_recs_num * CAR_CAPACITY)
        df.loc[index, u'in_alt'] = recs[
                recs.orientation == u'in'][u'alighting'].sum()   / \
                (in_recs_num * CAR_CAPACITY)
    if out_recs_num:
        df.loc[index, u'out_brd'] = recs[
                recs.orientation == u'out'][u'boarding'].sum()  / \
                (out_recs_num * CAR_CAPACITY)
        df.loc[index, u'out_alt'] = recs[
                recs.orientation == u'out'][u'alighting'].sum() / \
                (out_recs_num * CAR_CAPACITY)

解决方法:

您需要将Series转换为具有float64的dtype而不是当前对象.这是一个说明差异的例子.请注意,在一般情况下,对象dtype系列的用途有限,最常见的情况是包含字符串的系列.除此之外,它们非常慢,因为它们无法利用任何数据类型信息.

In [9]: s = Series(randn(6), index=pd.date_range('2013-01-01 11:25:00', freq='5T', periods=6), dtype=object)

In [10]: s.iloc[1:3] = nan

In [11]: s
Out[11]:
2013-01-01 11:25:00   -0.69522
2013-01-01 11:30:00        NaN
2013-01-01 11:35:00        NaN
2013-01-01 11:40:00   -0.70308
2013-01-01 11:45:00    -1.5653
2013-01-01 11:50:00    0.95893
Freq: 5T, dtype: object

In [12]: s.interpolate(method='time')
Out[12]:
2013-01-01 11:25:00   -0.69522
2013-01-01 11:30:00        NaN
2013-01-01 11:35:00        NaN
2013-01-01 11:40:00   -0.70308
2013-01-01 11:45:00    -1.5653
2013-01-01 11:50:00    0.95893
Freq: 5T, dtype: object

In [13]: s.astype(float).interpolate(method='time')
Out[13]:
2013-01-01 11:25:00   -0.6952
2013-01-01 11:30:00   -0.6978
2013-01-01 11:35:00   -0.7005
2013-01-01 11:40:00   -0.7031
2013-01-01 11:45:00   -1.5653
2013-01-01 11:50:00    0.9589
Freq: 5T, dtype: float64