如何在python中将时间序列数据的缺失行添加到panda数据帧中

Question

我有一个产品的时间序列数据集，如下所示：

date    product price   amount
11/17/2019  A   10  20
11/19/2019  A   15  20
11/24/2019  A   20  30
12/01/2019  C   40  50
12/05/2019  C   45  35

该数据在每个产品的数据的开始日期和结束日期之间缺少天数("MM/dd/YYYY"(。我正在尝试用零行填充缺失的日期，并将上一个表转换为下面给出的表：

date    product price   amount
11/17/2019  A   10  20
11/18/2019  A   0   0
11/19/2019  A   15  20
11/20/2019  A   0   0
11/21/2019  A   0   0
11/22/2019  A   0   0
11/23/2019  A   0   0
11/24/2019  A   20  30
12/01/2019  C   40  50
12/02/2019  C   0   0
12/03/2019  C   0   0
12/04/2019  C   0   0
12/05/2019  C   45  35

为了获得这种转换，我使用了代码：

import pandas as pd
import numpy as np
data=pd.read_csv("test.txt", sep="t", parse_dates=['date'])
data=data.set_index(["date", "product"])
start=data.first_valid_index()[0]
end=data.last_valid_index()[0]
df=data.set_index("date").reindex(pd.date_range(start,end, freq="1D"), fill_values=0)

但是，代码给出了一个错误。有什么方法可以有效地进行转换吗？

Answer 1

如果需要为每个product的缺少日期时间添加0，请分别使用GroupBy.apply中的自定义函数和DataFrame.reindex中的最小和最大日期时间：

df = pd.read_csv("test.txt", sep="t", parse_dates=['date'])
f = lambda x: x.reindex(pd.date_range(x.index.min(), 
x.index.max(), name='date'), fill_value=0)
df = (df.set_index('date')
.groupby('product')
.apply(f)
.drop('product', axis=1)
.reset_index())
print (df)
product       date  price  amount
0        A 2019-11-17     10      20
1        A 2019-11-18      0       0
2        A 2019-11-19     15      20
3        A 2019-11-20      0       0
4        A 2019-11-21      0       0
5        A 2019-11-22      0       0
6        A 2019-11-23      0       0
7        A 2019-11-24     20      30
8        C 2019-12-01     40      50
9        C 2019-12-02      0       0
10       C 2019-12-03      0       0
11       C 2019-12-04      0       0
12       C 2019-12-05     45      35

Answer 2

一个选项是使用pyjanitor的完整函数来暴露每个组缺少的行：

#pip install git+https://github.com/pyjanitor-devs/pyjanitor.git
import pandas as pd
import janitor
# build the dates to be applied per group
dates = dict(date = lambda df: pd.date_range(df.min(), df.max(), freq='1D'))
df.complete(dates, by='product', sort = True).fillna(0, downcast='infer')
date product  price  amount
0   2019-11-17 00:00:00       A     10      20
1   2019-11-18 00:00:00       A      0       0
2   2019-11-19 00:00:00       A     15      20
3   2019-11-20 00:00:00       A      0       0
4   2019-11-21 00:00:00       A      0       0
5   2019-11-22 00:00:00       A      0       0
6   2019-11-23 00:00:00       A      0       0
7   2019-11-24 00:00:00       A     20      30
8   2019-12-01 00:00:00       C     40      50
9   2019-12-02 00:00:00       C      0       0
10  2019-12-03 00:00:00       C      0       0
11  2019-12-04 00:00:00       C      0       0
12  2019-12-05 00:00:00       C     45      35

Answer 3

对于这种情况，有一种更简单的方法：

#create the full date range, and then create a DataFrame with the range
#if needed, you can expand the range a bit using datetime.timedelta()
alldates=pd.DataFrame(pd.date_range(data.index.min()-timedelta(1),data.index.max()+timedelta(4), freq="1D",name="newdate"))
#make 'newdate' the index, and you no longer need it as a column
alldates.index=alldates.newdate
alldates.drop(columns="newdate", inplace=True)
#now, join the tables, missing dates in the original table will be filled with NaN
data=alldates.join(data)