Pandas 熊猫，按重采样分组并用零填充缺失值

我有以下代码

import pandas as pd

data = {'date': ['2014-05-01', '2014-05-02', '2014-05-04', '2014-05-01', '2014-05-03', '2014-05-04'],
        'battle_deaths': [34, 25, 26, 15, 15, 14],
        'group': [1, 1, 1, 2, 2, 2]}

df = pd.DataFrame(data, columns=['date', 'battle_deaths', 'group'])

df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date')
df = df.sort_index()

我想有一个每个小组的战斗死亡人数，没有任何时间间隔。差不多

            battle_deaths  group
date                            
2014-05-01             34      1
2014-05-01             15      2
2014-05-02             25      1
2014-05-02              0      2 <--added with battle_deaths = 0 to fill the date range
2014-05-03              0      1 <--added
2014-05-03             15      2
2014-05-04             26      1
2014-05-04             14      2

如何使用pandas执行此操作？

与参数一起使用

fill\u value=0

：

df = df.groupby('group').resample('D')['battle_deaths'].asfreq(fill_value=0).reset_index()
print (df)
   group       date  battle_deaths
0      1 2014-05-01             34
1      1 2014-05-02             25
2      1 2014-05-03              0
3      1 2014-05-04             26
4      2 2014-05-01             15
5      2 2014-05-02              0
6      2 2014-05-03             15
7      2 2014-05-04             14

df = df.groupby('group').resample('D')['battle_deaths'].asfreq(fill_value=0).reset_index()
print (df)
   group       date  battle_deaths
0      1 2014-05-01             34
1      1 2014-05-02             25
2      1 2014-05-03              0
3      1 2014-05-04             26
4      2 2014-05-01             15
5      2 2014-05-02              0
6      2 2014-05-03             15
7      2 2014-05-04             14