Pandas asfreq 每周频率 [英] Pandas asfreq with weekly frequency

问题描述

我有一些设备的每周日志数据.对于某些设备，它从星期一开始，对于某些设备在星期三等.有时此数据中存在 ~month 的间隔，但我希望 DataFrame 索引仍包含每周具有 NaN 值的行.

I have weekly data of logs for some devices. For some device it start on Monday, for some on Wednesday etc. Sometimes there are gaps of ~month in this data, but I want the DataFrame index to still contain rows for each week with NaN value.

我正在尝试在 Python 中使用 asfreq('W')，但我无法得到预期的结果.

I am trying to use asfreq('W') in Python, but I cannot get what I expect.

示例:

我有什么:

Date            Some_Value
====            ==========
2019-04-10      2
2019-04-17      1
2019-04-24      3
2019-05-01      1
2019-05-08      3
2019-05-15      2
2019-06-06      3
2019-06-13      2

我期望/想要的(注意 2 个带有 NaN 的新行):

What I expect/want to have (note 2 new rows with NaNs):

Date            Some_Value
====            ==========
2019-04-10      2
2019-04-17      1
2019-04-24      3
2019-05-01      1
2019-05-08      3
2019-05-15      2
2019-05-22      NaN
2019-05-30      NaN
2019-06-06      3
2019-06-13      2

我用 asfreq('W') 得到了什么:

Date            Some_Value
====            ==========
2019-03-31      NaN
2019-04-07      NaN
2019-04-14      NaN
...................

所以，我得到所有 NaN 值和每个星期日的日期.但我不需要每个星期天的日期.我需要获取 DataFrame 的第一个日期(在 Pandas 的 groupby 中的组中的第一行，如果有很多时间序列)并每周重新采样第一行.

So, I get all NaN values and the dates from each Sunday. But I do not need dates from each Sunday. I need to take the first date of a DataFrame (of first row in a group in pandas' groupby in case of many time-series) and resample weekly form that first row.

是否可以直接使用 Pandas asfreq 实现?使用其他一些熊猫方法?还是应该是一些更复杂的自定义函数?

Is it achievable directly with pandas asfreq? With some other pandas method? Or should it be some more complex custom function?

谢谢.

推荐答案

问题是你的数据是星期三的第一个值，星期四的最后两个值，所以 asfreq 返回 NaNs，因为尝试改变它到周日工作日频率 - docs:

Problem is in your data are first values in Wednesday, last 2 in Thursday, so asfreq return NaNs, because try change it to Sunday weekday freq - docs:

W-SUN 每周频率(星期日).与 'W'

W-SUN weekly frequency (Sundays). Same as 'W'

一种可能的解决方案，但 DatetimeIndex 已更改为星期日:

One possible solution, but DatetimeIndex is changed for Sundays:

print (df.resample('W').first())
            Some_Value
Date                  
2019-04-14         2.0
2019-04-21         1.0
2019-04-28         3.0
2019-05-05         1.0
2019-05-12         3.0
2019-05-19         2.0
2019-05-26         NaN
2019-06-02         NaN
2019-06-09         3.0
2019-06-16         2.0

如果改变asfreq的频率:

print (df.asfreq('W-Wed'))
            Some_Value
Date                  
2019-04-10         2.0
2019-04-17         1.0
2019-04-24         3.0
2019-05-01         1.0
2019-05-08         3.0
2019-05-15         2.0
2019-05-22         NaN
2019-05-29         NaN
2019-06-05         NaN
2019-06-12         NaN

print (df.asfreq('W-Thu'))
            Some_Value
Date                  
2019-04-11         NaN
2019-04-18         NaN
2019-04-25         NaN
2019-05-02         NaN
2019-05-09         NaN
2019-05-16         NaN
2019-05-23         NaN
2019-05-30         NaN
2019-06-06         3.0
2019-06-13         2.0

这篇关于Pandas asfreq 每周频率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IT屋！