我在将pandas DataFrame索引从整数更改为日期时遇到问题。我想这样做，以便我可以调用reindex并填写表中列出的日期之间的日期。请注意，我现在必须使用pandas 0.7.3因为我也使用qstk，而qstk依赖于pandas 0.7.3
首先，这是我的布局：
(Pdb) df
AAPL GOOG IBM XOM date
1 0 0 4000 0 2011-01-13 16:00:00
2 0 1000 4000 0 2011-01-26 16:00:00
3 0 1000 4000 0 2011-02-02 16:00:00
4 0 1000 4000 4000 2011-02-10 16:00:00
6 0 0 1800 4000 2011-03-03 16:00:00
7 0 0 3300 4000 2011-06-03 16:00:00
8 0 0 0 4000 2011-05-03 16:00:00
9 1200 0 0 4000 2011-06-10 16:00:00
11 1200 0 0 4000 2011-08-01 16:00:00
12 0 0 0 4000 2011-12-20 16:00:00
(Pdb) type(df['date'])
<class 'pandas.core.series.Series'>
(Pdb) df2 = DataFrame(index=df['date'])
(Pdb) df2
Empty DataFrame
Columns: array([], dtype=object)
Index: array([2011-01-13 16:00:00, 2011-01-26 16:00:00, 2011-02-02 16:00:00,
2011-02-10 16:00:00, 2011-03-03 16:00:00, 2011-06-03 16:00:00,
2011-05-03 16:00:00, 2011-06-10 16:00:00, 2011-08-01 16:00:00,
2011-12-20 16:00:00], dtype=object)
(Pdb) df2.merge(df,left_index=True,right_on='date')
AAPL GOOG IBM XOM date
1 0 0 4000 0 2011-01-13 16:00:00
2 0 1000 4000 0 2011-01-26 16:00:00
3 0 1000 4000 0 2011-02-02 16:00:00
4 0 1000 4000 4000 2011-02-10 16:00:00
6 0 0 1800 4000 2011-03-03 16:00:00
8 0 0 0 4000 2011-05-03 16:00:00
7 0 0 3300 4000 2011-06-03 16:00:00
9 1200 0 0 4000 2011-06-10 16:00:00
11 1200 0 0 4000 2011-08-01 16:00:00
12 0 0 0 4000 2011-12-20 16:00:00
我尝试了多种方法来获取日期时间索引：
1。）将reindex（）方法与日期时间值列表一起使用。这会创建一个日期时间索引，但随后会为DataFrame中的数据填充NaN。我猜这是因为原始值与整数索引相关联并且重新索引到datetime会尝试使用默认值填充新索引（如果未指示填充方法，则为NaN）。正是如此：
(Pdb) df.reindex(index=df['date'])
AAPL GOOG IBM XOM date
date
2011-01-13 16:00:00 NaN NaN NaN NaN NaN
2011-01-26 16:00:00 NaN NaN NaN NaN NaN
2011-02-02 16:00:00 NaN NaN NaN NaN NaN
2011-02-10 16:00:00 NaN NaN NaN NaN NaN
2011-03-03 16:00:00 NaN NaN NaN NaN NaN
2011-06-03 16:00:00 NaN NaN NaN NaN NaN
2011-05-03 16:00:00 NaN NaN NaN NaN NaN
2011-06-10 16:00:00 NaN NaN NaN NaN NaN
2011-08-01 16:00:00 NaN NaN NaN NaN NaN
2011-12-20 16:00:00 NaN NaN NaN NaN NaN
2。）将DataFrame.merge与我的原始df和第二个数据帧df2一起使用，这基本上只是一个没有别的日期时间索引。所以我最终做了类似的事情：
(pdb) df2.merge(df,left_index=True,right_on='date')
AAPL GOOG IBM XOM date
1 0 0 4000 0 2011-01-13 16:00:00
2 0 1000 4000 0 2011-01-26 16:00:00
3 0 1000 4000 0 2011-02-02 16:00:00
4 0 1000 4000 4000 2011-02-10 16:00:00
6 0 0 1800 4000 2011-03-03 16:00:00
8 0 0 0 4000 2011-05-03 16:00:00
7 0 0 3300 4000 2011-06-03 16:00:00
9 1200 0 0 4000 2011-06-10 16:00:00
11 1200 0 0 4000 2011-08-01 16:00:00
（反之亦然）。但我总是最终得到这样的东西，整数指数。
3。）从一个空的DataFrame开始，该日期时间索引（从df的'date'字段创建）和一堆空列。然后我尝试通过设置相同的列来分配每列 名称等于df：
中的列(Pdb) df2['GOOG']=0
(Pdb) df2
GOOG
date
2011-01-13 16:00:00 0
2011-01-26 16:00:00 0
2011-02-02 16:00:00 0
2011-02-10 16:00:00 0
2011-03-03 16:00:00 0
2011-06-03 16:00:00 0
2011-05-03 16:00:00 0
2011-06-10 16:00:00 0
2011-08-01 16:00:00 0
2011-12-20 16:00:00 0
(Pdb) df2['GOOG'] = df['GOOG']
(Pdb) df2
GOOG
date
2011-01-13 16:00:00 NaN
2011-01-26 16:00:00 NaN
2011-02-02 16:00:00 NaN
2011-02-10 16:00:00 NaN
2011-03-03 16:00:00 NaN
2011-06-03 16:00:00 NaN
2011-05-03 16:00:00 NaN
2011-06-10 16:00:00 NaN
2011-08-01 16:00:00 NaN
2011-12-20 16:00:00 NaN
那么，在pandas 0.7.3中如何使用日期时间索引而不是整数索引重新创建df？我错过了什么？
答案 0 :(得分：6)
我认为您正在寻找set_index：
In [11]: df.set_index('date')
Out[11]:
AAPL GOOG IBM XOM
date
2011-01-13 16:00:00 0 0 4000 0
2011-01-26 16:00:00 0 1000 4000 0
2011-02-02 16:00:00 0 1000 4000 0
2011-02-10 16:00:00 0 1000 4000 4000
2011-03-03 16:00:00 0 0 1800 4000
2011-06-03 16:00:00 0 0 3300 4000
2011-05-03 16:00:00 0 0 0 4000
2011-06-10 16:00:00 1200 0 0 4000
2011-08-01 16:00:00 1200 0 0 4000
2011-12-20 16:00:00 0 0 0 4000