Pandas分组日期滚动中最新非空值的日期索引

Question

我正在尝试按组获取滚动时间 window 中某个值不为空的最新日期。它在没有分组的情况下工作得很好，但似乎分组会打乱一切。

这是可重现的例子：

import pandas as pd
from datetime import datetime as dt
import numpy as np

df = pd.DataFrame({})

df["date"] = [dt(2020, 10, i+1) for i in range(10)]
df["group"] = ["a" if int(i/3) == (i/3) else "b" for i in range(10)]
df["value"] = [i if int(i/2) == (i/2) else np.nan for i in range(10)]

数据帧

        date group  value
0 2020-10-01     a    0.0
1 2020-10-02     b    NaN
2 2020-10-03     b    2.0
3 2020-10-04     a    NaN
4 2020-10-05     b    4.0
5 2020-10-06     b    NaN
6 2020-10-07     a    6.0
7 2020-10-08     b    NaN
8 2020-10-09     b    8.0
9 2020-10-10     a    NaN

目标输出：

        date group  value  output
0 2020-10-01     a    0.0  2020-10-01
1 2020-10-02     b    NaN  NaT
2 2020-10-03     b    2.0  2020-10-03
3 2020-10-04     a    NaN  2020-10-01
4 2020-10-05     b    4.0  2020-10-05
5 2020-10-06     b    NaN  2020-10-05
6 2020-10-07     a    6.0  2020-10-07
7 2020-10-08     b    NaN  2020-10-05
8 2020-10-09     b    8.0  2020-10-09
9 2020-10-10     a    NaN  2020-10-07

我的尝试：

df = df.set_index("date").sort_index(ascending = True)

def latest_non_null_value_index(x):
        y = x[np.isnan(x) == False]
        print(y.index)
        if len(y) > 0:
            return y.index[-1]
        else:
            return np.nan

latest_index = df\
        .groupby(["group"])\
        .rolling("35D")\
        ["value"]\
        .apply(lambda x: latest_non_null_value_index(x).timestamp())\
        .reset_index()
  
def to_datetime_from_timestamp(x):
  if pd.isnull(x) == False:
      return dt.fromtimestamp(x)
  else:
      return pd.NaT
           
latest_index["value"] = latest_index["value"]\
    .apply(to_datetime_from_timestamp)

我得到的：

  group       date               value
0     a 2020-10-01 2020-10-01 02:00:00
1     a 2020-10-04 2020-10-01 02:00:00
2     a 2020-10-07 2020-10-03 02:00:00
3     a 2020-10-10 2020-10-03 02:00:00
4     b 2020-10-02                 NaT
5     b 2020-10-03 2020-10-06 02:00:00
6     b 2020-10-05 2020-10-07 02:00:00
7     b 2020-10-06 2020-10-07 02:00:00
8     b 2020-10-08 2020-10-07 02:00:00
9     b 2020-10-09 2020-10-10 02:00:00

知道我错过了什么吗？

编辑：我在获取最新值时似乎也没有这个问题......这确实与索引有关。

EDIT2：如果我能以某种方式将函数应用于 2 列，我可以将日期作为第二列并获得解决方法

Answer 1

您可以使用 pd.fillna 和“ffill”来向前填充缺失值

import pandas as pd
from datetime import datetime as dt
import numpy as np

df = pd.DataFrame({})

df["date"] = [dt(2020, 10, i+1) for i in range(10)]
df["group"] = ["a" if int(i/3) == (i/3) else "b" for i in range(10)]
df["value"] = [i if int(i/2) == (i/2) else np.nan for i in range(10)]

df = df.sort_values("date")  # Just make sure that row are properly ordered

date = df["date"].copy()
date[df.value.isna()] = pd.NaT
latest_index = date.groupby(df.group).fillna(method="ffill")

这不会处理您的滚动时间范围，但您可以像这样删除时间 window 之外的值：

latest_index[(df.date - latest_index).dt.days > 35] = pd.NaT

但这不是超级整洁，因此您可以尝试使用最大聚合来对抗滚动 window，如下所示：

df = df.set_index("date", drop=False)
df = df.sort_index()

date = pd.to_numeric(df["date"].copy())  # it wasn't letting me aggregate dates so we have to convert to float then back to dates
date[df.value.isna()] = None
latest_index = date.groupby(df.group).rolling("35D").max()
latest_index = pd.to_datetime(latest_index)

Pandas分组日期滚动中最新非空值的日期索引

Pandas date index of latest non null value in grouped date rolling

python

group-by

date

pandas

rolling-computation