我有每月（适用于不同证券）的数据，我想通过添加缺少的日期并向前填充该月所有天的月度数据（例如12/3的数据）来转换为每日数据/ 2015 = 2015年1月1日的数据，以此类推，以此类推）。
我的数据如下：
x = pd.DataFrame({'ticker': ['a','a','a','b','b'], 'dt': ['12/1/2015','1/1/2016','2/1/2016','1/1/2016','2/1/2016'], 'score': [2.8,3.8,3.8,1.9,1.7]})
我尝试使用日期和行情栏创建多索引，并使用每日频率重新采样。但是，我收到“ ValueError：无法从重复的轴重新索引”错误。
x['dt'] = pd.to_datetime(x['dt'])
dates = x.set_index('dt').resample('D').asfreq().index
ticker = x['ticker'].unique()
idx = pd.MultiIndex.from_product((dates, ticker), names=['dt', 'ticker'])
x.set_index(['dt', 'ticker']).reindex(idx, method='bfill').reset_index()
x.set_index(['dt', 'ticker']).reindex(idx, method='ffill').reset_index().sort_values(by='ticker')
预期结果：
x = pd.DataFrame({'ticker': ['a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b'], 'dt': ['12/1/2015', '12/2/2015', '12/3/2015', '12/4/2015', '12/5/2015', '12/6/2015', '12/7/2015', '12/8/2015', '12/9/2015', '12/10/2015', '12/11/2015', '12/12/2015', '12/13/2015', '12/14/2015', '12/15/2015', '12/16/2015', '12/17/2015', '12/18/2015', '12/19/2015', '12/20/2015', '12/21/2015', '12/22/2015', '12/23/2015', '12/24/2015', '12/25/2015', '12/26/2015', '12/27/2015', '12/28/2015', '12/29/2015', '12/30/2015', '12/31/2015', '1/1/2016', '1/2/2016', '1/3/2016', '1/4/2016', '1/5/2016', '1/6/2016', '1/7/2016', '1/8/2016', '1/9/2016', '1/10/2016', '1/11/2016', '1/12/2016', '1/13/2016', '1/14/2016', '1/15/2016', '1/16/2016', '1/17/2016', '1/18/2016', '1/19/2016', '1/20/2016', '1/21/2016', '1/22/2016', '1/23/2016', '1/24/2016', '1/25/2016', '1/26/2016', '1/27/2016', '1/28/2016', '1/29/2016', '1/30/2016', '1/31/2016', '2/1/2016','12/1/2015', '12/2/2015', '12/3/2015', '12/4/2015', '12/5/2015', '12/6/2015', '12/7/2015', '12/8/2015', '12/9/2015', '12/10/2015', '12/11/2015', '12/12/2015', '12/13/2015', '12/14/2015', '12/15/2015', '12/16/2015', '12/17/2015', '12/18/2015', '12/19/2015', '12/20/2015', '12/21/2015', '12/22/2015', '12/23/2015', '12/24/2015', '12/25/2015', '12/26/2015', '12/27/2015', '12/28/2015', '12/29/2015', '12/30/2015', '12/31/2015', '1/1/2016', '1/2/2016', '1/3/2016', '1/4/2016', '1/5/2016', '1/6/2016', '1/7/2016', '1/8/2016', '1/9/2016', '1/10/2016', '1/11/2016', '1/12/2016', '1/13/2016', '1/14/2016', '1/15/2016', '1/16/2016', '1/17/2016', '1/18/2016', '1/19/2016', '1/20/2016', '1/21/2016', '1/22/2016', '1/23/2016', '1/24/2016', '1/25/2016', '1/26/2016', '1/27/2016', '1/28/2016', '1/29/2016', '1/30/2016', '1/31/2016', '2/1/2016'], 'score': [2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 3.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.9, 1.7]})
实际结果：出现错误-'ValueError：无法从重复的轴重新索引'
我将不胜感激。谢谢
答案 0 :(得分：1)
IIUC，让我们尝试使用groupby和resample：
x['dt'] = pd.to_datetime(x['dt'])
x = x.set_index('dt')
df_out = x.groupby('ticker', as_index=False, group_keys=False)\
.apply(lambda d: d.resample('D').ffill())
输出：
ticker score
dt
2015-12-01 a 2.8
2015-12-02 a 2.8
2015-12-03 a 2.8
2015-12-04 a 2.8
2015-12-05 a 2.8
2015-12-06 a 2.8
2015-12-07 a 2.8
2015-12-08 a 2.8
2015-12-09 a 2.8
2015-12-10 a 2.8
2015-12-11 a 2.8
2015-12-12 a 2.8
2015-12-13 a 2.8
2015-12-14 a 2.8
2015-12-15 a 2.8
2015-12-16 a 2.8
2015-12-17 a 2.8
2015-12-18 a 2.8
2015-12-19 a 2.8
2015-12-20 a 2.8
2015-12-21 a 2.8
2015-12-22 a 2.8
2015-12-23 a 2.8
2015-12-24 a 2.8
2015-12-25 a 2.8
2015-12-26 a 2.8
2015-12-27 a 2.8
2015-12-28 a 2.8
2015-12-29 a 2.8
2015-12-30 a 2.8
... ... ...
2016-01-03 b 1.9
2016-01-04 b 1.9
2016-01-05 b 1.9
2016-01-06 b 1.9
2016-01-07 b 1.9
2016-01-08 b 1.9
2016-01-09 b 1.9
2016-01-10 b 1.9
2016-01-11 b 1.9
2016-01-12 b 1.9
2016-01-13 b 1.9
2016-01-14 b 1.9
2016-01-15 b 1.9
2016-01-16 b 1.9
2016-01-17 b 1.9
2016-01-18 b 1.9
2016-01-19 b 1.9
2016-01-20 b 1.9
2016-01-21 b 1.9
2016-01-22 b 1.9
2016-01-23 b 1.9
2016-01-24 b 1.9
2016-01-25 b 1.9
2016-01-26 b 1.9
2016-01-27 b 1.9
2016-01-28 b 1.9
2016-01-29 b 1.9
2016-01-30 b 1.9
2016-01-31 b 1.9
2016-02-01 b 1.7
[95 rows x 2 columns]