我有一个数据框架，如下所示
x = pd.DataFrame({'user': ['a','a','b','b'], 'dt': ['2016-01-01','2016-01-02', '2016-01-05','2016-01-06'], 'val': [1,33,2,1]})
我想做的是在date列中找到最小和最大日期，并扩展该列使其具有所有日期，同时0为该val列填写。所以所需的输出是
0
val
dt user val 0 2016-01-01 a 1 1 2016-01-02 a 33 2 2016-01-03 a 0 3 2016-01-04 a 0 4 2016-01-05 a 0 5 2016-01-06 a 0 6 2016-01-01 b 0 7 2016-01-02 b 0 8 2016-01-03 b 0 9 2016-01-04 b 0 10 2016-01-05 b 2 11 2016-01-06 b 1
初始数据框：
dt user val 0 2016-01-01 a 1 1 2016-01-02 a 33 2 2016-01-05 b 2 3 2016-01-06 b 1
首先，将日期转换为日期时间：
x['dt'] = pd.to_datetime(x['dt'])
然后，生成日期和唯一用户：
dates = x.set_index('dt').resample('D').asfreq().index >> DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04', '2016-01-05', '2016-01-06'], dtype='datetime64[ns]', name='dt', freq='D') users = x['user'].unique() >> array(['a', 'b'], dtype=object)
这将允许您创建一个MultiIndex：
idx = pd.MultiIndex.from_product((dates, users), names=['dt', 'user']) >> MultiIndex(levels=[[2016-01-01 00:00:00, 2016-01-02 00:00:00, 2016-01-03 00:00:00, 2016-01-04 00:00:00, 2016-01-05 00:00:00, 2016-01-06 00:00:00], ['a', 'b']], labels=[[0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1]], names=['dt', 'user'])
您可以使用它来重新索引您的DataFrame：
x.set_index(['dt', 'user']).reindex(idx, fill_value=0).reset_index() Out: dt user val 0 2016-01-01 a 1 1 2016-01-01 b 0 2 2016-01-02 a 33 3 2016-01-02 b 0 4 2016-01-03 a 0 5 2016-01-03 b 0 6 2016-01-04 a 0 7 2016-01-04 b 0 8 2016-01-05 a 0 9 2016-01-05 b 2 10 2016-01-06 a 0 11 2016-01-06 b 1
然后可以按用户排序：
x.set_index(['dt', 'user']).reindex(idx, fill_value=0).reset_index().sort_values(by='user') Out: dt user val 0 2016-01-01 a 1 2 2016-01-02 a 33 4 2016-01-03 a 0 6 2016-01-04 a 0 8 2016-01-05 a 0 10 2016-01-06 a 0 1 2016-01-01 b 0 3 2016-01-02 b 0 5 2016-01-03 b 0 7 2016-01-04 b 0 9 2016-01-05 b 2 11 2016-01-06 b 1
