与日期时间相关的值的Python Numpy或Pandas线性插值 [英] Python Numpy or Pandas Linear Interpolation For Datetime related Values
问题描述
我拥有的数据如下所示，但我也可以控制其格式.基本上，我想将Python与Numpy或Pandas配合使用以对数据集进行插值，以实现每秒一秒的插值数据，因此分辨率更高.
I have data that looks like the following but I also have control of how it is formatted. Basically, I want to use Python with Numpy or Pandas to interpolate the dataset to achieve second by second interpolated data so that it is a much higher resolution.
所以我想线性插值并在当前拥有的每个实数值之间生成新值，同时还要保留原始数值.
So I want to linearly interpolate and produce new values between each of the real values I currently have while keeping the original values as well.
如何用Pandas或Numpy做到这一点?
How can I accomplish this with Pandas or Numpy?
例如，我有这种类型的数据:
As an example, I have this type of data:
TIME ECI_X ECI_Y ECI_Z
2013-12-07 00:00:00, -7346664.77912, -13323447.6311, 21734849.5263,@
2013-12-07 00:01:00, -7245621.40363, -13377562.35, 21735850.3527,@
2013-12-07 00:01:30, -7142326.20854, -13432541.9267, 21736462.4521,@
2013-12-07 00:02:00, -7038893.48454, -13487262.8599, 21736650.3293,@
2013-12-07 00:02:30, -6935325.24526, -13541724.0946, 21736413.9937,@
2013-12-07 00:03:00, -6833738.23865, -13594806.9333, 21735778.2218,@
2013-12-07 00:03:30, -6729905.37597, -13648746.6281, 21734705.6406,@
2013-12-07 00:04:00, -6625943.01291, -13702423.5112, 21733208.9233,@
2013-12-07 00:04:30, -6521853.17291, -13755836.5481, 21731288.1125,@
2013-12-07 00:05:00, -6419753.85176, -13807871.3011, 21729016.1386,@
2013-12-07 00:05:30, -6315415.32918, -13860754.6497, 21726259.4135,@
2013-12-07 00:06:00, -6210955.33186, -13913371.1187, 21723078.7695,@
...
我希望它可以每秒获得第二个，即
And I'd like it to be second by second - i.e.
2013-12-07 00:00:00, -7346664.77912, -13323447.6311, 21734849.5263,@
2013-12-07 00:00:01, -7346665.10000, -13323448.1000, 21734850.1000,@
...
2013-12-07 00:00:59, -7346611.10000, -13323461.1000, 21734850.1000,@
2013-12-07 00:01:00, -7245621.40363, -13377562.3500, 21735850.3527,@
请向我展示一个如何完成此操作的示例.谢谢！
Please show me an example of how I can accomplish this. Thanks!
我已经尝试过了:
#! /usr/bin/python
import datetime
from pandas import *
first = datetime(2013,12,8,0,0,0)
second = datetime(2013,12,8,0,2,0)
dates = [first,second]
x = np.array([617003.390723, 884235.38059])
newRange = date_range(first, second, freq='S')
ts = Series(x, index=dates)
ts.interpolate()
print ts.head()
#2013-12-08 00:00:00, 617003.390723, -26471116.2566, 3974868.93334,@
#2013-12-08 00:02:00, 884235.38059, -26519366.9219, 3601627.52947,@
如何使用"newRange"在"x"中的实际值之间创建线性插值?
How do I use the "newRange" to create linearly interpolated values between the real values in "x"?
推荐答案
使用pandas git master(98e48ca)，您可以执行以下操作:
Using pandas git master (98e48ca) you can do the following:
In [27]: n = 4
In [28]: df = DataFrame(randn(n, 2), index=date_range('1/1/2001', periods=n, freq='30S'))
In [29]: resampled = df.resample('S')
In [30]: resampled.head()
Out[30]:
0 1
2001-01-01 00:00:00 -1.045 -1.067
2001-01-01 00:00:01 NaN NaN
2001-01-01 00:00:02 NaN NaN
2001-01-01 00:00:03 NaN NaN
2001-01-01 00:00:04 NaN NaN
[5 rows x 2 columns]
In [31]: interp = resampled.interpolate()
In [32]: interp.head()
Out[32]:
0 1
2001-01-01 00:00:00 -1.045 -1.067
2001-01-01 00:00:01 -1.014 -1.042
2001-01-01 00:00:02 -0.983 -1.018
2001-01-01 00:00:03 -0.952 -0.993
2001-01-01 00:00:04 -0.921 -0.969
[5 rows x 2 columns]
In [33]: interp.tail()
Out[33]:
0 1
2001-01-01 00:01:26 0.393 0.622
2001-01-01 00:01:27 0.337 0.571
2001-01-01 00:01:28 0.281 0.519
2001-01-01 00:01:29 0.225 0.468
2001-01-01 00:01:30 0.169 0.416
[5 rows x 2 columns]
默认情况下，Series.interpolate()执行线性插值.您也可以将DataFrame.resample()用于不规则采样的数据.
By default Series.interpolate() performs linear interpolation. You can use DataFrame.resample() with irregularly sampled data as well.
这篇关于与日期时间相关的值的Python Numpy或Pandas线性插值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
