以 numpy.array()、pandas.DataFrame() 或 xarray.DataSet() 的形式展開時間序列以包含作為 NaN 的缺失記錄

Question

import numpy as np
import pandas as pd
import xarray as xr

validIdx = np.ones(365*5, dtype= bool)
validIdx[np.random.randint(low=0, high=365*5, size=30)] = False
time = pd.date_range("2000-01-01", freq="H", periods=365 * 5)[validIdx]
data = np.arange(365 * 5)[validIdx]
ds = xr.Dataset({"foo": ("time", data), "time": time})
df = ds.to_dataframe()

在上面的示例中，時間序列數據ds （或df ）有 30 條隨機選擇的缺失記錄，而沒有那些作為 NaN 的記錄。 因此，數據的長度是 365x5 - 30，而不是 365x5)。

我的問題是：如何擴展ds和df以將 30 個缺失值作為 NaN（因此，長度將為 365x5）？ 例如，如果示例數據中遺漏了“2000-12-02”中的值，則它將如下所示：

...
2000-12-01  value 1
2000-12-03  value 2
...

，而我想要的是：

...
2000-12-01  value 1
2000-12-02  NaN
2000-12-03  value 2
...

Answer 1

也許您可以嘗試用 1 小時resample 。

沒有 NaN 的df （就在df = ds.to_dataframe()之后）：

>>> df
                      foo
time
2000-01-01 00:00:00     0
2000-01-01 01:00:00     1
2000-01-01 02:00:00     2
2000-01-01 03:00:00     3
2000-01-01 04:00:00     4
...                   ...
2000-03-16 20:00:00  1820
2000-03-16 21:00:00  1821
2000-03-16 22:00:00  1822
2000-03-16 23:00:00  1823
2000-03-17 00:00:00  1824

[1795 rows x 1 columns]

帶有 NaN 的df ( df_1h )：

>>> df_1h = df.resample('1H').mean()
>>> df_1h
                        foo
time
2000-01-01 00:00:00     0.0
2000-01-01 01:00:00     1.0
2000-01-01 02:00:00     2.0
2000-01-01 03:00:00     3.0
2000-01-01 04:00:00     4.0
...                     ...
2000-03-16 20:00:00  1820.0
2000-03-16 21:00:00  1821.0
2000-03-16 22:00:00  1822.0
2000-03-16 23:00:00  1823.0
2000-03-17 00:00:00  1824.0

[1825 rows x 1 columns]

帶有 NaN 的行：

>>> df_1h[df_1h['foo'].isna()]
                     foo
time
2000-01-02 10:00:00  NaN
2000-01-04 07:00:00  NaN
2000-01-05 06:00:00  NaN
2000-01-09 02:00:00  NaN
2000-01-13 15:00:00  NaN
2000-01-16 16:00:00  NaN
2000-01-18 21:00:00  NaN
2000-01-21 22:00:00  NaN
2000-01-23 19:00:00  NaN
2000-01-24 01:00:00  NaN
2000-01-24 19:00:00  NaN
2000-01-27 12:00:00  NaN
2000-01-27 16:00:00  NaN
2000-01-29 06:00:00  NaN
2000-02-02 01:00:00  NaN
2000-02-06 13:00:00  NaN
2000-02-09 11:00:00  NaN
2000-02-15 12:00:00  NaN
2000-02-15 15:00:00  NaN
2000-02-21 04:00:00  NaN
2000-02-28 05:00:00  NaN
2000-02-28 06:00:00  NaN
2000-03-01 15:00:00  NaN
2000-03-02 18:00:00  NaN
2000-03-04 18:00:00  NaN
2000-03-05 20:00:00  NaN
2000-03-12 08:00:00  NaN
2000-03-13 20:00:00  NaN
2000-03-16 01:00:00  NaN

df_1h中的 NaN 數量：

>>> df_1h.isnull().sum()
foo    30
dtype: int64