Python 替换时间序列中的列值

我想在预设的timeseries数据帧中替换序列中的一组值，例如，序列a中的3个值将替换3个时间戳值。但是，replace或.set\u value命令似乎不起作用。在这种情况下，时间戳已经以索引形式存在。时间戳示例系列如下所示：

>  timestamp        Coloum_1
>  01/01/2010 00:00 21.17
>  01/01/2010 00:15 19.67
>  01/01/2010 00:30 17.95

在python中有点新。感谢您的支持。谢谢

更新 输出可能看起来像底部的输出。这里，时间戳和Column1数据位于一个单独的excel文件中，Python在其中读取并提取以进行分析。时间戳已经以索引形式存在，只有column1值需要替换为单独的序列值。但是，它似乎并没有用时间戳序列替换序列值

In [42]: df4 = pd.DataFrame({'Timestamp': ['2010-01-01 00:00:00', '2010-01-01    00:15:00', '2010-01-01 00:30:00'], 'Column_1':[21.17, 19.17, 17.95]})
df4 = df4.set_index('Timestamp')
df4.head()

Out[42]:
Timestamp            Column_1
2010-01-01 00:00:00   21.17
2010-01-01 00:15:00   19.17
2010-01-01 00:30:00   17.95

In [43]:a = pd.Series([50.0,60.0,70.0])           
df4.replace("'2010-01-01 00:00:00', periods=3, freq='15 min'", 'Column_1',a.all) 
df4.head()

a = pd.Series([50.0,60.0,70.0]) 
df4.replace("'2010-01-01 00:00:00', periods=3, freq='15 min'", 'Column_1',a.all) 
df4.head() 

Out[43]:
Timestamp           Column_1
2010-01-01 00:00:00 21.17 
2010-01-01 00:15:00 19.17 
2010-01-01 00:30:00 17.95

但是如果我只使用一个时间戳，它将替换相应的值

In [50]:

df4.set_value('2010-01-01 00:00:00', 'Column_1', 50);
df4.head()


Out[50]:
Timestamp          Column_1
2010-01-01 00:00:00 50.00
2010-01-01 00:15:00 19.17
2010-01-01 00:30:00 17.95

试试这个：

 a = pd.Series([15.0,16.0,17.0])
    #0    15.0
    #1    16.0
    #2    17.0
   # dtype: float64

   a =  pd.date_range('1/1/2010', periods=3, freq='900S')

  #DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 00:15:00',
  #             '2010-01-01 00:30:00'],
  #            dtype='datetime64[ns]', freq='900S')

print a 

   # DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 00:15:00',
    #               '2010-01-01 00:30:00'],
    #              dtype='datetime64[ns]', freq='900S')


df = pd.DataFrame(a, columns=['Column1'] ) 
# or df = pd.DataFrame(pd.date_range('1/1/2010', periods=3, freq='900S'), columns=['Column1'] ) 
  #               Column1
  #  0  2010-01-01 00:00:00
  #  1  2010-01-01 00:15:00
  #  2  2010-01-01 00:30:00 

s = pd.Series(range(3), name='Column1')
    #0    0
    #1    1
    #2    2
    #Name: Column1, dtype: int64

编辑：

---

已解决

In [68]:
df = pd.DataFrame({'Timestamp': ['2010-01-01 00:00:00', '2010-01-01 00:15:00', '2010-01-01 00:30:00', '2010-01-01 00:45:00'], 
                    'Column_1':[21.17, 19.17, 17.95, 25.0]})
df = df.set_index('Timestamp')
df.head()

Out[68]:
Timestamp           Column_1
2010-01-01 00:00:00  21.17
2010-01-01 00:15:00  19.17
2010-01-01 00:30:00  17.95
2010-01-01 00:45:00  25.00
4 rows × 1 columns
In [69]:

df.loc ['2010-01-01 00:00:00':'2010-01-01 00:15:00', 'Column_1'] = [50,60] 
df.head()

Out[69]:
Timestamp           Column_1
2010-01-01 00:00:00  50.00
2010-01-01 00:15:00  60.00
2010-01-01 00:30:00  17.95
2010-01-01 00:45:00  25.00

---

嗨，梅林，谢谢。只需添加一个单独的部分来更详细地解释问题和输出。你能看看这个吗你想得太多了。。请参见编辑。你不需要创建熊猫对象来处理熊猫。@Merlin:哈哈……我有超过20000个信号，与时间戳的间隔为15分钟。我需要将一些定义的值对应替换为某个时间戳，我认为覆盖这些值的最简单方法是与时间戳匹配。@Merlin:Solved。谢谢…对这个问题想得太多了………>df.loc['timestamp start'：timestamp stop'，'Column']=[X，Y]如果我下面的答案有帮助，也许你应该投上一票，或者标记为正确。

df4['Column_1'] = [50.0,60.0,70.0]

In [68]:
df = pd.DataFrame({'Timestamp': ['2010-01-01 00:00:00', '2010-01-01 00:15:00', '2010-01-01 00:30:00', '2010-01-01 00:45:00'], 
                    'Column_1':[21.17, 19.17, 17.95, 25.0]})
df = df.set_index('Timestamp')
df.head()

Out[68]:
Timestamp           Column_1
2010-01-01 00:00:00  21.17
2010-01-01 00:15:00  19.17
2010-01-01 00:30:00  17.95
2010-01-01 00:45:00  25.00
4 rows × 1 columns
In [69]:

df.loc ['2010-01-01 00:00:00':'2010-01-01 00:15:00', 'Column_1'] = [50,60] 
df.head()

Out[69]:
Timestamp           Column_1
2010-01-01 00:00:00  50.00
2010-01-01 00:15:00  60.00
2010-01-01 00:30:00  17.95
2010-01-01 00:45:00  25.00