熊猫填写组中缺少的日期和值

初始数据框：

 dt  user    val0   2016-01-01     a      11   2016-01-02     a     332   2016-01-05     b      23   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  val0  2016-01-01    a    11  2016-01-01    b    02  2016-01-02    a   333  2016-01-02    b    04  2016-01-03    a    05  2016-01-03    b    06  2016-01-04    a    07  2016-01-04    b    08  2016-01-05    a    09  2016-01-05    b    210 2016-01-06    a    011 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  val0  2016-01-01    a    12  2016-01-02    a   334  2016-01-03    a    06  2016-01-04    a    08  2016-01-05    a    010 2016-01-06    a    01  2016-01-01    b    03  2016-01-02    b    05  2016-01-03    b    07  2016-01-04    b    09  2016-01-05    b    211 2016-01-06    b    1