pandas 填充熊猫中缺失的索引
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/20392265/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
fill missing indices in pandas
提问by qua
I have data like follows:
我有如下数据：
import pandas as pd
from datetime import datetime
x = pd.Series([1, 2, 4], [datetime(2013,11,1), datetime(2013,11, 2), datetime(2013, 11, 4)])
The missing index at November 3rd corresponds to a zero value, and I want it to look like this:
11 月 3 日缺失的索引对应于零值，我希望它看起来像这样：
y = pd.Series([1,2,0,4], pd.date_range('2013-11-01', periods = 4))
What's the best way to convert x to y? I've tried
将 x 转换为 y 的最佳方法是什么？我试过了
y = pd.Series(x, pd.date_range('2013-11-1', periods = 4)).fillna(0)
This throws an index error sometimes which I can't interpret (Index length did not match values, even though index and data have the same length. Is there a better way to do this?
这有时会引发我无法解释的索引错误（索引长度与值不匹配，即使索引和数据具有相同的长度。有没有更好的方法来做到这一点？
回答by Roman Pekar
You can use pandas.Series.resample()for this:
您可以pandas.Series.resample()为此使用：
>>> x.resample('D').fillna(0)
2013-11-01 1
2013-11-02 2
2013-11-03 0
2013-11-04 4
There's fill_methodparameter in the resample()function, but I don't know if it's possible to use it to replace NaNduring resampling. But looks like you can use howmethod to take care of it, like:
函数里有fill_method参数，resample()不知道NaN重采样时能不能用它来替换。但是看起来您可以使用how方法来处理它，例如：
>>> x.resample('D', how=lambda x: x.mean() if len(x) > 0 else 0)
2013-11-01 1
2013-11-02 2
2013-11-03 0
2013-11-04 4
Don't know which method is preferred one. Please also take a look at @AndyHayden's answer - probably reindex()with fill_value=0would be most efficien way to do this, but you have to make your own tests.
不知道哪种方法是首选。也请看一看@ AndyHayden的答案-可能reindex()与fill_value=0将做到这一点最efficien方式，但你必须让自己的测试。
回答by Andy Hayden
I think I would use a resample(note if there are dupes it takes the mean by default):
我想我会使用重采样（请注意，如果有重复，默认情况下取平均值）：
In [11]: x.resample('D') # you could use how='first'
Out[11]:
2013-11-01 1
2013-11-02 2
2013-11-03 NaN
2013-11-04 4
Freq: D, dtype: float64
In [12]: x.resample('D').fillna(0)
Out[12]:
2013-11-01 1
2013-11-02 2
2013-11-03 0
2013-11-04 4
Freq: D, dtype: float64
If you prefered dupes to raise, then use reindex:
如果您更喜欢欺骗者，请使用reindex：
In [13]: x.reindex(pd.date_range('2013-11-1', periods=4), fill_value=0)
Out[13]:
2013-11-01 1
2013-11-02 2
2013-11-03 0
2013-11-04 4
Freq: D, dtype: float64
