问题描述
我有一个时间序列，其中缺少一些条目，如下所示:
I have a times series with some missing entries, that looks like this:
date value
---------------
2000 5
2001 10
2003 8
2004 72
2005 12
2007 13
我想为previous_value"创建一个列.但我只希望它显示连续几年的值.所以我希望它看起来像这样:
I would like to do create a column for the "previous_value". But I only want it to show values for consecutive years. So I want it to look like this:
date value previous_value
-------------------------------
2000 5 nan
2001 10 5
2003 8 nan
2004 72 8
2005 12 72
2007 13 nan
但是，仅将 Pandas shift 函数直接应用于列 'value' 会为 'time' = 2003 提供 'previous_value' = 10，而对于 'time' = 2007 将提供 'previous_value' = 12.
However just applying pandas shift function directly to the column 'value' would give 'previous_value' = 10 for 'time' = 2003, and 'previous_value' = 12 for 'time' = 2007.
在熊猫中处理这个问题的最优雅的方法是什么?(我不确定它是否像设置 'freq' 属性一样简单).
What's the most elegant way to deal with this in pandas? (I'm not sure if it's as easy as setting the 'freq' attribute).
推荐答案
In [588]: df = pd.DataFrame({ 'date':[2000,2001,2003,2004,2005,2007],
'value':[5,10,8,72,12,13] })
In [589]: df['previous_value'] = df.value.shift()[ df.date == df.date.shift() + 1 ]
In [590]: df
Out[590]:
date value previous_value
0 2000 5 NaN
1 2001 10 5
2 2003 8 NaN
3 2004 72 8
4 2005 12 72
5 2007 13 NaN
另请参阅此处使用 resample() 的时间序列方法:对不均匀间隔的数据使用 shift()
Also see here for a time series approach using resample(): Using shift() with unevenly spaced data
这篇关于 pandas 移动时间序列缺失值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！