pandas 重新采样上采样日期/边缘数据 [英] Pandas Resample Upsample last date / edge of data

问题描述

我正在尝试将每周数据上采样到每日数据，但是，我在上采样最后一个边缘时遇到困难.我该怎么办?

I'm trying to upsample weekly data to daily data, however, I'm having difficulty upsampling the last edge. How can I go about this?

import pandas as pd
import datetime
df = pd.DataFrame({'wk start': ['2018-08-12', '2018-08-12', '2018-08-19'], 
    'car': [ 'tesla model 3', 'tesla model x', 'tesla model 3'],
    'sales':[38000,98000, 40000]})
df['wk start'] = df['wk start'].apply(lambda x: datetime.datetime.strptime(x, '%Y-%m-%d'))
df.set_index('wk start').groupby('car').resample('D').pad()

这将返回:

                             car            sales
car             wk start        
tesla model 3   2018-08-12  tesla model 3   38000
                2018-08-13  tesla model 3   38000
                2018-08-14  tesla model 3   38000
                2018-08-15  tesla model 3   38000
                2018-08-16  tesla model 3   38000
                2018-08-17  tesla model 3   38000
                2018-08-18  tesla model 3   38000
                2018-08-19  tesla model 3   40000

tesla model x   2018-08-12  tesla model x   98000

我想要的输出是:

                             car            sales
car             wk start        
tesla model 3   2018-08-12  tesla model 3   38000
                2018-08-13  tesla model 3   38000
                2018-08-14  tesla model 3   38000
                2018-08-15  tesla model 3   38000
                2018-08-16  tesla model 3   38000
                2018-08-17  tesla model 3   38000
                2018-08-18  tesla model 3   38000
                2018-08-19  tesla model 3   40000
                2018-08-20  tesla model 3   40000
                2018-08-21  tesla model 3   40000
                2018-08-22  tesla model 3   40000
                2018-08-23  tesla model 3   40000
                2018-08-24  tesla model 3   40000
                2018-08-25  tesla model 3   40000
tesla model x   2018-08-12  tesla model x   98000
                2018-08-13  tesla model x   98000
                2018-08-14  tesla model x   98000
                2018-08-15  tesla model x   98000
                2018-08-16  tesla model x   98000
                2018-08-17  tesla model x   98000
                2018-08-18  tesla model x   98000

我查看了，但是他们正在使用句点，而我正在查看日期时间.预先感谢！

I looked at this, but they're using periods and I'm looking at datetimes. Thanks in advance!

推荐答案

是的，您是正确的，排除了最后的边缘数据.解决方案是将它们添加到输入 DataFrame 中-我的解决方案使用

Yes, you are right, last edge data are excluded. Solution is add them to input DataFrame - my solution creates a helper Dataframe using drop_duplicates, adds 6 days and concat's to original df before using your solution:

df1 = df.sort_values('wk start').drop_duplicates('car', keep='last').copy()
df1['wk start'] = df1['wk start'] + pd.Timedelta(6, unit='d')

df = pd.concat([df, df1], ignore_index=True)
df = df.set_index('wk start').groupby('car').resample('D').pad()
print (df)
                                    car  sales
car           wk start                        
tesla model 3 2018-08-12  tesla model 3  38000
              2018-08-13  tesla model 3  38000
              2018-08-14  tesla model 3  38000
              2018-08-15  tesla model 3  38000
              2018-08-16  tesla model 3  38000
              2018-08-17  tesla model 3  38000
              2018-08-18  tesla model 3  38000
              2018-08-19  tesla model 3  40000
              2018-08-20  tesla model 3  40000
              2018-08-21  tesla model 3  40000
              2018-08-22  tesla model 3  40000
              2018-08-23  tesla model 3  40000
              2018-08-24  tesla model 3  40000
              2018-08-25  tesla model 3  40000
tesla model x 2018-08-12  tesla model x  98000
              2018-08-13  tesla model x  98000
              2018-08-14  tesla model x  98000
              2018-08-15  tesla model x  98000
              2018-08-16  tesla model x  98000
              2018-08-17  tesla model x  98000
              2018-08-18  tesla model x  98000

这篇关于 pandas 重新采样上采样日期/边缘数据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！