将 pandas datetimeindex延长1个周期 [英] extend a pandas datetimeindex by 1 period

问题描述

考虑DateTimeIndex dates

dates = pd.date_range('2016-01-29', periods=4, freq='BM')
dates

DatetimeIndex(['2016-01-29', '2016-02-29', '2016-03-31', '2016-04-29'],
              dtype='datetime64[ns]', freq='BM')

我想以附加到对象的频率将索引延长一个周期.

I want to extend the index by one period at the frequency attached to the object.

我希望

pd.date_range('2016-01-29', periods=5, freq='BM')

DatetimeIndex(['2016-01-29', '2016-02-29', '2016-03-31', '2016-04-29',
               '2016-05-31'],
              dtype='datetime64[ns]', freq='BM')

---

我尝试过

---

I've tried

dates.append(dates[[-1]] + pd.offsets.BusinessMonthEnd())

但是

• 未普遍使用dates
的频率
• 我收到性能警告

• Not generalized to use frequency of dates
• I get a performance warning

PerformanceWarning:非向量化的DateOffset应用于Series或DatetimeIndex

PerformanceWarning: Non-vectorized DateOffset being applied to Series or DatetimeIndex

推荐答案

DatetimeIndex中的时间戳已经知道它们描述的是工作月份结束，因此您只需添加1:

The timestamps in your DatetimeIndex already know that they are describing business month ends, so you can simply add 1:

import pandas as pd
dates = pd.date_range('2016-01-29', periods=4, freq='BM')

print(repr(dates[-1]))
# => Timestamp('2016-04-29 00:00:00', offset='BM')

print(repr(dates[-1] + 1))
# => Timestamp('2016-05-31 00:00:00', offset='BM')

您可以使用.union将后者添加到索引中:

You can add the latter to your index using .union:

dates = dates.union([dates[-1] + 1])
print(dates)
# => DatetimeIndex(['2016-01-29', '2016-02-29', '2016-03-31', '2016-04-29',
#                   '2016-05-31'],
#                  dtype='datetime64[ns]', freq='BM')

与.append相比，它保留了偏移量的知识.

Compared to .append, this retains knowledge of the offset.

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