[英]How to reindex a datetime-based multiindex in pandas
我有一个 dataframe,它计算每个用户每天发生事件的次数。 用户每天可能有 0 个事件,并且(因为该表是原始事件日志的汇总)dataframe 中缺少具有 0 个事件的行。我想添加这些缺失的行并按周对数据进行分组,以便每个用户都有每周一次(如果适用,包括 0 次)。
这是我的输入示例:
import numpy as np
import pandas as pd
np.random.seed(42)
df = pd.DataFrame({
"person_id": np.arange(3).repeat(5),
"date": pd.date_range("2022-01-01", "2022-01-15", freq="d"),
"event_count": np.random.randint(1, 7, 15),
})
# end of each week
# Note: week 2022-01-23 is not in df, but should be part of the result
desired_index = pd.to_datetime(["2022-01-02", "2022-01-09", "2022-01-16", "2022-01-23"])
df
| | person_id | date | event_count |
|---:|------------:|:--------------------|--------------:|
| 0 | 0 | 2022-01-01 00:00:00 | 4 |
| 1 | 0 | 2022-01-02 00:00:00 | 5 |
| 2 | 0 | 2022-01-03 00:00:00 | 3 |
| 3 | 0 | 2022-01-04 00:00:00 | 5 |
| 4 | 0 | 2022-01-05 00:00:00 | 5 |
| 5 | 1 | 2022-01-06 00:00:00 | 2 |
| 6 | 1 | 2022-01-07 00:00:00 | 3 |
| 7 | 1 | 2022-01-08 00:00:00 | 3 |
| 8 | 1 | 2022-01-09 00:00:00 | 3 |
| 9 | 1 | 2022-01-10 00:00:00 | 5 |
| 10 | 2 | 2022-01-11 00:00:00 | 4 |
| 11 | 2 | 2022-01-12 00:00:00 | 3 |
| 12 | 2 | 2022-01-13 00:00:00 | 6 |
| 13 | 2 | 2022-01-14 00:00:00 | 5 |
| 14 | 2 | 2022-01-15 00:00:00 | 2 |
这就是我想要的结果:
| | person_id | level_1 | event_count |
|---:|------------:|:--------------------|--------------:|
| 0 | 0 | 2022-01-02 00:00:00 | 9 |
| 1 | 0 | 2022-01-09 00:00:00 | 13 |
| 2 | 0 | 2022-01-16 00:00:00 | 0 |
| 3 | 0 | 2022-01-23 00:00:00 | 0 |
| 4 | 1 | 2022-01-02 00:00:00 | 0 |
| 5 | 1 | 2022-01-09 00:00:00 | 11 |
| 6 | 1 | 2022-01-16 00:00:00 | 5 |
| 7 | 1 | 2022-01-23 00:00:00 | 0 |
| 8 | 2 | 2022-01-02 00:00:00 | 0 |
| 9 | 2 | 2022-01-09 00:00:00 | 0 |
| 10 | 2 | 2022-01-16 00:00:00 | 20 |
| 11 | 2 | 2022-01-23 00:00:00 | 0 |
我可以使用以下方法生产它:
(
df
.groupby(["person_id", pd.Grouper(key="date", freq="w")]).sum()
.groupby("person_id").apply(
lambda df: (
df
.reset_index(drop=True, level=0)
.reindex(desired_index, fill_value=0))
)
.reset_index()
)
但是,根据reindex
的文档,我应该可以直接将它与level=1
一起用作 kwarg,而无需再执行一次groupby
。 但是,当我这样做时,我得到了两个索引的“内部连接”而不是“外部连接”:
result = (
df
.groupby(["person_id", pd.Grouper(key="date", freq="w")]).sum()
.reindex(desired_index, level=1)
.reset_index()
)
| | person_id | date | event_count |
|---:|------------:|:--------------------|--------------:|
| 0 | 0 | 2022-01-02 00:00:00 | 9 |
| 1 | 0 | 2022-01-09 00:00:00 | 13 |
| 2 | 1 | 2022-01-09 00:00:00 | 11 |
| 3 | 1 | 2022-01-16 00:00:00 | 5 |
| 4 | 2 | 2022-01-16 00:00:00 | 20 |
为什么会这样,我应该如何正确使用df.reindex
?
我发现了一个关于重新索引多索引级别的类似 SO 问题,但那里接受的答案使用df.unstack
,这对我不起作用,因为并不是我想要的索引的每个级别都出现在我当前的索引中(反之亦然) ).
您需要通过两个级别的MultiIndex
重新索引:
mux = pd.MultiIndex.from_product([df['person_id'].unique(), desired_index],
names=['person_id','date'])
result = (
df
.groupby(["person_id", pd.Grouper(key="date", freq="w")]).sum()
.reindex(mux, fill_value=0)
.reset_index()
)
print (result)
person_id date event_count
0 0 2022-01-02 9
1 0 2022-01-09 13
2 0 2022-01-16 0
3 0 2022-01-23 0
4 1 2022-01-02 0
5 1 2022-01-09 11
6 1 2022-01-16 5
7 1 2022-01-23 0
8 2 2022-01-02 0
9 2 2022-01-09 0
10 2 2022-01-16 20
11 2 2022-01-23 0
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