Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Welcome To Ask or Share your Answers For Others

Categories

0 votes
1.3k views
in Technique[技术] by (71.8m points)

python - Pandas asfreq with weekly frequency

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.

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

Example:

What I have:

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

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

What I get with asfreq('W'):

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

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.

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

Thanks.

See Question&Answers more detail:os

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
Welcome To Ask or Share your Answers For Others

1 Reply

0 votes
by (71.8m points)

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 weekly frequency (Sundays). Same as 'W'

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

If change frequency in asfreq:

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

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
OGeek|极客中国-欢迎来到极客的世界,一个免费开放的程序员编程交流平台!开放,进步,分享!让技术改变生活,让极客改变未来! Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Click Here to Ask a Question

...