填写groupby的缺失日期 [英] Fill in missing dates of groupby

问题描述

想象一下，我有一个看起来像这样的数据框:

Imagine I have a dataframe that looks like:

ID      DATE         VALUE
1    31-01-2006        5
1    28-02-2006        5
1    31-05-2006        10
1    30-06-2006        11
2    31-01-2006        5
2    31-02-2006        5
2    31-03-2006        5
2    31-04-2006        5

如您所见，这是面板数据，在同一日期具有多个条目，且具有不同的ID.我要做的是为每个ID填写缺少的日期.您可以看到，对于ID"1"，第二个和第三个条目之间的间隔为几个月.

As you can see this is panel data with multiple entries on the same date for different IDs. What I want to do is fill in missing dates for each ID. You can see that for ID "1" there is a jump in months between the second and third entry.

我想要一个看起来像这样的数据框:

I would like a dataframe that looks like:

ID      DATE         VALUE
1    31-01-2006        5
1    28-02-2006        5
1    31-03-2006        NA
1    30-04-2006        NA
1    31-05-2006        10
1    30-06-2006        11
2    31-01-2006        5
2    31-02-2006        5
2    31-03-2006        5
2    31-04-2006        5

我不知道如何执行此操作，因为存在重复的日期，因此无法按日期编制索引.

I have no idea how to do this since I can not index by date since there are duplicate dates.

推荐答案

一种方法是使用数据透视表，然后将其拆栈:

One way is to use pivot_table and then unstack:

In [11]: df.pivot_table("VALUE", "DATE", "ID")
Out[11]:
ID             1    2
DATE
28-02-2006   5.0  NaN
30-06-2006  11.0  NaN
31-01-2006   5.0  5.0
31-02-2006   NaN  5.0
31-03-2006   NaN  5.0
31-04-2006   NaN  5.0
31-05-2006  10.0  NaN

In [12]: df.pivot_table("VALUE", "DATE", "ID").unstack().reset_index()
Out[12]:
    ID        DATE     0
0    1  28-02-2006   5.0
1    1  30-06-2006  11.0
2    1  31-01-2006   5.0
3    1  31-02-2006   NaN
4    1  31-03-2006   NaN
5    1  31-04-2006   NaN
6    1  31-05-2006  10.0
7    2  28-02-2006   NaN
8    2  30-06-2006   NaN
9    2  31-01-2006   5.0
10   2  31-02-2006   5.0
11   2  31-03-2006   5.0
12   2  31-04-2006   5.0
13   2  31-05-2006   NaN

---

另一种也许更有效的方法是重新索引from_product:

---

An alternative, perhaps slightly more efficient way is to reindex from_product:

In [21] df1 = df.set_index(['ID', 'DATE'])

In [22]: df1.reindex(pd.MultiIndex.from_product(df1.index.levels))
Out[22]:
              VALUE
1 28-02-2006    5.0
  30-06-2006   11.0
  31-01-2006    5.0
  31-02-2006    NaN
  31-03-2006    NaN
  31-04-2006    NaN
  31-05-2006   10.0
2 28-02-2006    NaN
  30-06-2006    NaN
  31-01-2006    5.0
  31-02-2006    5.0
  31-03-2006    5.0
  31-04-2006    5.0
  31-05-2006    NaN

这篇关于填写groupby的缺失日期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！