用停止符填充 pandas 系列中的NA值 [英] Fill NA Values in pandas Series with a stop
问题描述
我正在分析时间序列，根据某些条件，我可以选择事件的开始或结束的行.此时，我的系列看起来像这样(为简洁起见，我省略了一些重复的值):
I'm analyzing a time series, and based on certain criteria, I can pick out rows that are either the start or the end of the events. At this point, my series looks something like this (I've left out some repetitive values for brevity):
import numpy as np
import pandas
from pandas import Timestamp
datadict = {'event': {
Timestamp('2010-01-01 00:20:00', tz=None): 'event start',
Timestamp('2010-01-01 00:30:00', tz=None): '--',
Timestamp('2010-01-01 00:40:00', tz=None): '--',
Timestamp('2010-01-01 00:50:00', tz=None): '--',
Timestamp('2010-01-01 01:00:00', tz=None): '--',
Timestamp('2010-01-01 01:10:00', tz=None): 'event end',
Timestamp('2010-01-01 01:20:00', tz=None): '--',
Timestamp('2010-01-01 02:20:00', tz=None): '--',
Timestamp('2010-01-01 02:30:00', tz=None): 'event start',
Timestamp('2010-01-01 02:40:00', tz=None): '--',
Timestamp('2010-01-01 02:50:00', tz=None): '--',
Timestamp('2010-01-01 03:00:00', tz=None): '--',
Timestamp('2010-01-01 03:10:00', tz=None): '--',
Timestamp('2010-01-01 03:20:00', tz=None): '--',
Timestamp('2010-01-01 03:30:00', tz=None): 'event end',
}}
data = pandas.DataFrame.from_dict(datadict)
event
2010-01-01 00:20:00 event start
2010-01-01 00:30:00 --
2010-01-01 00:40:00 --
2010-01-01 00:50:00 --
2010-01-01 01:00:00 --
2010-01-01 01:10:00 event end
2010-01-01 01:20:00 --
2010-01-01 02:20:00 --
2010-01-01 02:30:00 event start
2010-01-01 02:40:00 --
2010-01-01 02:50:00 --
2010-01-01 03:00:00 --
2010-01-01 03:10:00 --
2010-01-01 03:20:00 --
2010-01-01 03:30:00 event end
这就是我想要实现的(理想的是没有for循环)
event event number
2010-01-01 00:20:00 event start 1
2010-01-01 00:30:00 -- 1
2010-01-01 00:40:00 -- 1
2010-01-01 00:50:00 -- 1
2010-01-01 01:00:00 -- 1
2010-01-01 01:10:00 event end 1
2010-01-01 01:20:00 -- NA
2010-01-01 02:20:00 -- NA
2010-01-01 02:30:00 event start 2
2010-01-01 02:40:00 -- 2
2010-01-01 02:50:00 -- 2
2010-01-01 03:00:00 -- 2
2010-01-01 03:10:00 -- 2
2010-01-01 03:20:00 -- 2
2010-01-01 03:30:00 event end 2
2010-01-01 03:40:00 -- NA
2010-01-01 03:50:00 -- NA
这就是我尝试过的
通过对数据质量的一些乐观假设，我可以像这样获得事件编号:
table = data[data.event != '--'].reset_index()
table['event number'] = 1 + np.floor(table.index / 2)
table = table.set_index('index')
event event number
index
2010-01-01 00:20:00 event start 1
2010-01-01 01:10:00 event end 1
2010-01-01 02:30:00 event start 2
2010-01-01 03:30:00 event end 2
然后我可以join将其保存到原始数据帧，并使用method='ffill'
I can then join that to my original dataframe, and fillna with the method='ffill'
data2 = data.join(table[['event number']])
data2['filled'] = data2['event number'].fillna(method='ffill')
event event number filled
2010-01-01 00:20:00 event start 1 1
2010-01-01 00:30:00 -- NaN 1
2010-01-01 00:40:00 -- NaN 1
2010-01-01 00:50:00 -- NaN 1
2010-01-01 01:00:00 -- NaN 1
2010-01-01 01:10:00 event end 1 1
2010-01-01 01:20:00 -- NaN 1 # <- d'oh
2010-01-01 02:20:00 -- NaN 1 # <- d'oh
2010-01-01 02:30:00 event start 2 2
2010-01-01 02:40:00 -- NaN 2
2010-01-01 02:50:00 -- NaN 2
2010-01-01 03:00:00 -- NaN 2
2010-01-01 03:10:00 -- NaN 2
2010-01-01 03:20:00 -- NaN 2
2010-01-01 03:30:00 event end 2 2
问题
如您所见，事件之间的时间(01:20到02:20)与事件#1相关联.
The Problem
As you can see, the time between events (01:20 through 02:20) is being associated with event #1.
总有没有跳过这些部分而不循环吗?
Is there anyway to skip over these sections without looping?
推荐答案
您可以通过查看event start的数量和event end的数量的累积总和来实现此目的:
you can achieve this by just looking at cumulative summation of number of event start and number of event end:
>>> data['event number'] = (data.event == 'event start').cumsum()
>>> data
event event number
2010-01-01 00:20:00 event start 1
2010-01-01 00:30:00 -- 1
2010-01-01 00:40:00 -- 1
2010-01-01 00:50:00 -- 1
2010-01-01 01:00:00 -- 1
2010-01-01 01:10:00 event end 1
2010-01-01 01:20:00 -- 1
2010-01-01 02:20:00 -- 1
2010-01-01 02:30:00 event start 2
2010-01-01 02:40:00 -- 2
2010-01-01 02:50:00 -- 2
2010-01-01 03:00:00 -- 2
2010-01-01 03:10:00 -- 2
2010-01-01 03:20:00 -- 2
2010-01-01 03:30:00 event end 2
现在，您只需要在没有事件时将其设置为nan即可；但是这些地方对应的行中event start的累积总和等于event end的累积总和(移动1行)
now you just need to set to nan when there is no event; but those places corresponds to rows where cumulative summation of event start is equal to cumulative summation of event end (with shifting 1 row)
>>> idx = data['event number'] == (data.event.shift(1) == 'event end').cumsum()
>>> data.loc[idx, 'event number'] = np.nan
>>> data
event event number
2010-01-01 00:20:00 event start 1
2010-01-01 00:30:00 -- 1
2010-01-01 00:40:00 -- 1
2010-01-01 00:50:00 -- 1
2010-01-01 01:00:00 -- 1
2010-01-01 01:10:00 event end 1
2010-01-01 01:20:00 -- NaN
2010-01-01 02:20:00 -- NaN
2010-01-01 02:30:00 event start 2
2010-01-01 02:40:00 -- 2
2010-01-01 02:50:00 -- 2
2010-01-01 03:00:00 -- 2
2010-01-01 03:10:00 -- 2
2010-01-01 03:20:00 -- 2
2010-01-01 03:30:00 event end 2
[15 rows x 2 columns]
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