以天为单位获得熊猫日期时间指数系列差异的最佳方法

Question

假设我有一系列的pandas.tseries.index.DatetimeIndex，基本上是2016年的工作日。有没有简单/优雅的方法来找出系列天数的顺序差异？就像 .diff（） 对整数或浮点数据帧列所做的那样。

import pandas as pd
import numpy as np
ds = pd.date_range("2016-01-01","2016-12-31",freq='B')
# I was hoping for something like this:
ds.diff().days
# this gives me what I want, but it is ugly and unintuitive
np.diff(ds) / 86400000000000

我也考虑过np.diff(ds.date)但它给了我一个 datetime.timedelta 的 ndarray ，我不知道如何将其转换为没有 for 循环的整数数组/系列。

Answer 1

试试这个：

In [154]: ds.to_series().diff()
Out[154]:
2016-01-01      NaT
2016-01-04   3 days
2016-01-05   1 days
2016-01-06   1 days
2016-01-07   1 days
2016-01-08   1 days
2016-01-11   3 days
2016-01-12   1 days
2016-01-13   1 days
2016-01-14   1 days
2016-01-15   1 days
2016-01-18   3 days
2016-01-19   1 days
2016-01-20   1 days
2016-01-21   1 days
2016-01-22   1 days
2016-01-25   3 days
2016-01-26   1 days
2016-01-27   1 days
2016-01-28   1 days
2016-01-29   1 days
2016-02-01   3 days
2016-02-02   1 days
2016-02-03   1 days
2016-02-04   1 days
              ...

Answer 2

我认为您需要添加Index.to_series，然后Series.dt.days是否需要数字输出：

print (ds.to_series().diff().dt.days)

2016-01-01    NaN
2016-01-04    3.0
2016-01-05    1.0
2016-01-06    1.0
2016-01-07    1.0
2016-01-08    1.0
2016-01-11    3.0
2016-01-12    1.0
2016-01-13    1.0
2016-01-14    1.0
2016-01-15    1.0
2016-01-18    3.0
2016-01-19    1.0
2016-01-20    1.0
2016-01-21    1.0
2016-01-22    1.0
2016-01-25    3.0
2016-01-26    1.0
2016-01-27    1.0
2016-01-28    1.0
2016-01-29    1.0
2016-02-01    3.0
2016-02-02    1.0
2016-02-03    1.0
2016-02-04    1.0
2016-02-05    1.0
2016-02-08    3.0
2016-02-09    1.0
2016-02-10    1.0
2016-02-11    1.0
2016-11-21    3.0
2016-11-22    1.0
2016-11-23    1.0
2016-11-24    1.0
2016-11-25    1.0
2016-11-28    3.0
2016-11-29    1.0
2016-11-30    1.0
2016-12-01    1.0
2016-12-02    1.0
2016-12-05    3.0
2016-12-06    1.0
2016-12-07    1.0
2016-12-08    1.0
2016-12-09    1.0
2016-12-12    3.0
2016-12-13    1.0
2016-12-14    1.0
2016-12-15    1.0
2016-12-16    1.0
2016-12-19    3.0
2016-12-20    1.0
2016-12-21    1.0
2016-12-22    1.0
2016-12-23    1.0
2016-12-26    3.0
2016-12-27    1.0
2016-12-28    1.0
2016-12-29    1.0
2016-12-30    1.0
Freq: B, dtype: float64