使用Python将每周数据转换为每日数据

问题描述

在这个问题上，我会尽量保持清楚.假设我有一个数据框，格式为:

I will try to be as clear as possible in this question. Let's say I have a dataframe formed as:

Date          Quantity
05/05/2017    34
12/05/2017    24
19/05/2017    45
26/05/2017    23
2/06/2017     56
9/06/2017     32

我想将此具有每周数据(如您所见)的数据框转换为具有每日数据的数据框.但是，我的数据框中(例如周末)会出现一些漏洞".我已经将每日日期存储到另一个变量中.我想获得这样的东西:

I would like to convert this dataframe having weekly data (as you see) into one having daily data. However, there will be some "holes" in my dataframe (ex. week-end days). I already have stored the daily dates into another variable.I would like to obtain something like this:

Date          Quantity
05/05/2017    34
08/05/2017    34
09/05/2017    34
...           ...
2/06/2017     56
5/06/2017     56
6/06/2017     56
...           ...

我的想法是要有一个循环，其内容是:当日期早于每周数据帧中的日期(例如2017年5月19日)但高于前一个日期(所以12/05/2017)时，我想要在每日数据帧的数量"列中添加正确的数字(在本例中为45).

My idea is to have a loop that says "whenever the date is prior to the date in the weekly dataframe (ex. 19/05/2017) but higher than the previous date (so 12/05/2017), I want to append to the column "Quantity" in the daily dataframe the correct number (in this case 45).

但是，我不知道如何在Python中执行此操作.我应该将日期转换成数字，然后再转换回日期吗?有谁有更快的方法来做到这一点?非常感谢

However, I do not know how to do this in Python. Should I convert the dates into numbers and then back to dates? Does anyone have any faster way to do this? Thank you very much

推荐答案

以下是使用 resample 与 工作日频率 (B)并预先填写:

Here's an option using resample with business day frequency (B) and forward fill:

df['Date'] = pd.to_datetime(df.Date, format='%d/%m/%Y')
df.set_index('Date').resample('B').ffill().reset_index()

#         Date  Quantity
#0  2017-05-05      34
#1  2017-05-08      34
#2  2017-05-09      34
#...
#20 2017-06-02      56
#21 2017-06-05      56
#22 2017-06-06      56
#...

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