以 numpy.array()、pandas.DataFrame() 或 xarray.DataSet() 的形式展开时间序列，以将缺失的记录包含为 NaN

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，时间序列数据 ds（或 df）有 30 个随机选择的缺失记录，而没有将这些记录作为 NaN。所以数据的长度是365x5 - 30，不是365x5).

我的问题是：如何扩展 ds 和 df 以将 30 个缺失值作为 NaN（因此，长度将为 365x5）？例如，如果示例数据中缺少“2000-12-02”中的某个值，则它将看起来像：

...
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

也许你可以试试 resample 1 小时。

没有 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]

带有 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]

包含 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NaN的数量：

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

以 numpy.array()、pandas.DataFrame() 或 xarray.DataSet() 的形式展开时间序列，以将缺失的记录包含为 NaN

Expand a time series in the form of numpy.array(), pandas.DataFrame(), or xarray.DataSet() to contain the missing records as NaN

python

pandas

nan

python-xarray