pandas时间序列索引报错的问题

Question

    #执行以下代码没有问题    
    ts_data_load= pd.read_csv(os.path.join(data_dir, "energy.csv"), parse_dates=["timestamp"] )

    # Reindex the dataframe such that the dataframe has a record for every time point
    # between the minimum and maximum timestamp in the time series. This helps to
    # identify missing time periods in the data (there are none in this dataset).
    ts_data_load.index = ts_data_load["timestamp"]
    ts_data_load = ts_data_load .reindex( pd.date_range(min(energy["timestamp"]), max(energy["timestamp"]), freq="H")  )
    ts_data_load= ts_data_load .drop("timestamp", axis=1)
    ts_data_load = load_data(data_dir)[["load"]]
    ts_data_load.head()
    #输出结果

load
2012-01-01 00:00:00 2,698.00
2012-01-01 01:00:00 2,558.00
2012-01-01 02:00:00 2,444.00
2012-01-01 03:00:00 2,402.00
2012-01-01 04:00:00 2,403.00

#执行下面这句代码，输出正常  
ts_data_load.index

#输出结果
DatetimeIndex(['2012-01-01 00:00:00', '2012-01-01 01:00:00',
               '2012-01-01 02:00:00', '2012-01-01 03:00:00',
               '2012-01-01 04:00:00', '2012-01-01 05:00:00',
               '2012-01-01 06:00:00', '2012-01-01 07:00:00',
               '2012-01-01 08:00:00', '2012-01-01 09:00:00',
               ...
               '2014-12-31 14:00:00', '2014-12-31 15:00:00',
               '2014-12-31 16:00:00', '2014-12-31 17:00:00',
               '2014-12-31 18:00:00', '2014-12-31 19:00:00',
               '2014-12-31 20:00:00', '2014-12-31 21:00:00',
               '2014-12-31 22:00:00', '2014-12-31 23:00:00'],
              dtype='datetime64[ns]', length=26304, freq='H')

    #如果执行下面这句代码，就报错
    ts_data_load['2012-06-01']
    #报错，请问这是什么原因？？谢谢

---

KeyError Traceback (most recent call last)
File d:\ProgramData\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3653, in Index.get_loc(self, key)
3652 try:
-> 3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:

File d:\ProgramData\anaconda3\Lib\site-packages\pandas_libs\index.pyx:147, in pandas._libs.index.IndexEngine.get_loc()

File d:\ProgramData\anaconda3\Lib\site-packages\pandas_libs\index.pyx:176, in pandas._libs.index.IndexEngine.get_loc()

File pandas_libs\hashtable_class_helper.pxi:7080, in pandas._libs.hashtable.PyObjectHashTable.get_item()

File pandas_libs\hashtable_class_helper.pxi:7088, in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: '20120601'

The above exception was the direct cause of the following exception:

KeyError Traceback (most recent call last)
Cell In[33], line 1
----> 1 ts_data_load['20120601']

File d:\ProgramData\anaconda3\Lib\site-packages\pandas\core\frame.py:3761, in DataFrame.getitem(self, key)
3759 if self.columns.nlevels > 1:
3760 return self._getitem_multilevel(key)
-> 3761 indexer = self.columns.get_loc(key)
3762 if is_integer(indexer):
3763 indexer = [indexer]

File d:\ProgramData\anaconda3\Lib\site-packages\pandas\core\indexes\base.py:3655, in Index.get_loc(self, key)
3653 return self._engine.get_loc(casted_key)
3654 except KeyError as err:
-> 3655 raise KeyError(key) from err
3656 except TypeError:
3657 # If we have a listlike key, _check_indexing_error will raise
3658 # InvalidIndexError. Otherwise we fall through and re-raise
3659 # the TypeError.
3660 self._check_indexing_error(key)

KeyError: '20120601'

Answer 1

该回答引用自GPT-3.5,由博主GISer Liu编写：

根据问题描述，报错信息显示KeyError: '20120601'，这意味着在尝试使用ts_data_load['20120601']这样的语法时发生了错误，因为该索引值无法在DataFrame中找到。问题的根本原因是尝试使用字符串``` '20120601'

解决方案如下：
1.  **索引时使用时间戳对象而不是字符串** ：确保使用正确的时间戳对象进行索引，而不是字符串。在pandas中，使用``` pd.Timestamp('2012-06-01') ```来表示时间戳对象。
2.  **转换索引为时间戳对象** ：在读取CSV文件后，将DataFrame的索引转换为时间戳对象，以便正确地使用时间戳进行索引。
下面是解决问题的具体代码：
pythonCopy code

确保正确导入所需的库

import pandas as pd
import os

读取CSV文件，确保parse_dates参数正确解析时间戳列

ts_data_load = pd.read_csv(os.path.join(data_dir, "energy.csv"), parse_dates=["timestamp"])

将索引转换为时间戳对象

ts_data_load.index = pd.to_datetime(ts_data_load["timestamp"])

删除'timestamp'列，因为索引已经包含了这个信息

ts_data_load = ts_data_load.drop("timestamp", axis=1)

现在可以使用时间戳对象进行索引

例如，获取2012年6月1日的数据

ts_data_load.loc['2012-06-01']

或者使用pd.Timestamp('2012-06-01')进行索引

ts_data_load.loc[pd.Timestamp('2012-06-01')]

```
以上代码会正确地索引到2012年6月1日的数据，避免了报错。
这样的修改可以确保代码在进行时间序列数据索引时不会出现错误，并且能够按预期工作。

如果该回答解决了您的问题，请采纳！如果没有，请参考以下方案进行修订