Python 熊猫产品的连续日期

我开始与熊猫合作，我有一些问题我真的不知道如何解决

我有一个包含日期、产品、库存和销售额的数据框。缺少一些日期和产品。我想得到一个时间序列的每个产品的日期范围

例如：

           product  udsStock udsSales
date        
2019-12-26  14      161      848
2019-12-27  14      1340     914
2019-12-30  14      856      0
2019-12-25  4       3132     439
2019-12-27  4       3177     616
2020-01-01  4       500      883

所有产品的范围必须相同，即使一个产品没有出现在范围内的某个日期

如果我想要2019-12-25到2020-01-01的范围，最终数据帧应该如下所示：

           product  udsStock udsSales
date
2019-12-25  14      NaN      NaN      
2019-12-26  14      161      848
2019-12-27  14      1340     914
2019-12-28  14      NaN      NaN
2019-12-29  14      NaN      NaN      
2019-12-30  14      856      0
2019-12-31  14      NaN      NaN
2020-01-01  14      NaN      NaN   
2019-12-25  4       3132     439
2019-12-26  4       NaN      NaN  
2019-12-27  4       3177     616
2019-12-28  4       NaN      NaN  
2019-12-29  4       NaN      NaN
2019-12-30  4       NaN      NaN
2019-12-31  4       NaN      NaN
2020-01-01  4       500      883

我曾尝试按范围重新编制索引，但没有效果，因为有相同的索引

idx = pd.date_range('25-12-2019', '01-01-2020')
df = df.reindex(idx)

我还尝试按日期和产品编制索引，然后重新编制索引，但我不知道如何放置缺少的产品

还有什么想法吗

---

提前感谢

将索引转换为日期时间对象：

df2.index = pd.to_datetime(df2.index)

创建日期和产品的独特组合：

import itertools

idx = pd.date_range("25-12-2019", "01-01-2020")
product = df2["product"].unique()
temp = itertools.product(idx, product)
temp = pd.MultiIndex.from_tuples(temp, names=["date", "product"])
temp

MultiIndex([('2019-12-25', 14),
            ('2019-12-25',  4),
            ('2019-12-26', 14),
            ('2019-12-26',  4),
            ('2019-12-27', 14),
            ('2019-12-27',  4),
            ('2019-12-28', 14),
            ('2019-12-28',  4),
            ('2019-12-29', 14),
            ('2019-12-29',  4),
            ('2019-12-30', 14),
            ('2019-12-30',  4),
            ('2019-12-31', 14),
            ('2019-12-31',  4),
            ('2020-01-01', 14),
            ('2020-01-01',  4)],
           names=['date', 'product'])

重新索引数据帧：

df2.set_index("product", append=True).reindex(temp).sort_index(
    level=1, ascending=False
).reset_index(level="product")


          product   udsStock    udsSales
    date            
2020-01-01  14       NaN    NaN
2019-12-31  14       NaN    NaN
2019-12-30  14      856.0   0.0
2019-12-29  14      NaN     NaN
2019-12-28  14      NaN     NaN
2019-12-27  14      1340.0  914.0
2019-12-26  14      161.0   848.0
2019-12-25  14      NaN     NaN
2020-01-01  4       500.0   883.0
2019-12-31  4       NaN     NaN
2019-12-30  4       NaN     NaN
2019-12-29  4       NaN     NaN
2019-12-28  4       NaN     NaN
2019-12-27  4       3177.0  616.0
2019-12-26  4       NaN     NaN
2019-12-25  4       3132.0  439.0

---

在R，特别是tidyverse中，可以使用该方法实现。在Python中，包有一些东西，但仍有一些问题需要解决（已经为此提交了PR）。

我们可以使用

pd.date\u range

和

groupby.reindex

来实现您的结果：

date_range = pd.date_range(start='2019-12-25', end='2020-01-01', freq='D')
df = df.groupby('product', sort=False).apply(lambda x: x.reindex(date_range))
df['product'] = df.groupby(level=0)['product'].ffill().bfill()
df = df.droplevel(0)

            product  udsStock  udsSales
2019-12-25     14.0       NaN       NaN
2019-12-26     14.0     161.0     848.0
2019-12-27     14.0    1340.0     914.0
2019-12-28     14.0       NaN       NaN
2019-12-29     14.0       NaN       NaN
2019-12-30     14.0     856.0       0.0
2019-12-31     14.0       NaN       NaN
2020-01-01     14.0       NaN       NaN
2019-12-25      4.0    3132.0     439.0
2019-12-26      4.0       NaN       NaN
2019-12-27      4.0    3177.0     616.0
2019-12-28      4.0       NaN       NaN
2019-12-29      4.0       NaN       NaN
2019-12-30      4.0       NaN       NaN
2019-12-31      4.0       NaN       NaN
2020-01-01      4.0     500.0     883.0