Question

我正在尝试按用户组填写缺少的日期，但是我的一个索引列有一个重复的日期，所以我尝试使用唯一的日期并重新索引它然后我得到长度不匹配错误。我如何重新采样日频率，不会出现重复错误。

import pandas as pandas

x = pandas.DataFrame({'user': ['a','a','b','b','a'], 'dt': ['2016-01-01','2016-01-02', '2016-01-05','2016-01-06','2016-01-06'], 'val': [1,33,2,1,2]})
udates=x['dt'].unique()
x['dt'] = pandas.to_datetime(x['dt'])
dates = x.set_index(udates).resample('D').asfreq().index
users=x['user'].unique()
idx = pandas.MultiIndex.from_product((dates, users), names=['dt', 'user'])
x.set_index(['dt', 'user']).reindex(idx, fill_value=0).reset_index()
print(x)

期望的输出

          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    2
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

Answer 1

以下是一种方法，将每个user重新编制索引，使其具有从最短日期到最长日期的日期范围：

# setup your dataframe as you had it before:
x = pandas.DataFrame({'user': ['a','a','b','b','a'], 'dt': ['2016-01-01','2016-01-02', '2016-01-05','2016-01-06','2016-01-06'], 'val': [1,33,2,1,2]})
udates=x['dt'].unique()
x['dt'] = pandas.to_datetime(x['dt'])

# fill with new dates:
filled_df = (x.set_index('dt')
             .groupby('user')
             .apply(lambda d: d.reindex(pd.date_range(min(x.dt),
                                                      max(x.dt),
                                                      freq='D')))
             .drop('user', axis=1)
             .reset_index('user')
             .fillna(0))


>>> filled_df
           user   val
2016-01-01    a   1.0
2016-01-02    a  33.0
2016-01-03    a   0.0
2016-01-04    a   0.0
2016-01-05    a   0.0
2016-01-06    a   2.0
2016-01-01    b   0.0
2016-01-02    b   0.0
2016-01-03    b   0.0
2016-01-04    b   0.0
2016-01-05    b   2.0
2016-01-06    b   1.0

Answer 2

另一种不如@sacul优雅的方式......但几乎相同的速度。

import pandas as pd
x = pd.DataFrame({'user': ['a','a','b','b','a'],
                  'dt': ['2016-01-01','2016-01-02',
                         '2016-01-05','2016-01-06','2016-01-06'],
                  'val': [1,33,2,1,2]})

users = pd.unique(x.user)
x.dt = pd.to_datetime(x.dt)
dates = pd.date_range(min(x.dt), max(x.dt))
x.set_index('dt', inplace=True)

df = pd.DataFrame(index=dates)
for u in users:
    df[u] = x[x.user==u].val

df = df.unstack().reset_index()
df.rename(columns={'level_0': 'user',
                    'level_1': 'dt',
                    0: 'val'}, inplace=True)
df.val.fillna(0, inplace=True)
df.val = df.val.astype(int)
df = df[['dt', 'user', 'val']]

DF：

            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    2
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

Pandas使用重复的索引值填充组内缺少的日期和值

2 个答案: