重新采样非时间序列数据 [英] resampling non-time-series data
问题描述
我有一些要用数据框和熊猫处理的数据. 它们包含约10,000行和6列.
I have some data which I'm handling with dataframes and pandas. They contain about 10 000 rows and 6 columns.
问题是，我已经进行了几次试验，并且不同的数据集的索引编号略有不同. (这是使用多种材料进行的力-长度"测试，当然，测量点并不是完美的.)
The problem is, that I have done several trials and the different datasets have slightly different index numbers. (It's a "force - length" testing with several materials and of course the measurement points are not alined perfectly.)
现在我的想法是，使用包含长度值的索引来重新采样"数据. 似乎pandas中的重采样功能仅适用于日期时间数据类型.
Now my idea was, to "resample" the data using the index which contains the value for the length. It seems that the resampling function in pandas is only available for datetime datatypes.
我尝试通过to_datetime转换索引并成功.但是在重新采样后，我需要恢复到原始比例.某种from_datetime函数.
I tried to convert the index via to_datetime and succeeded. But after the resampling, I need to get back to the original scale. some kind of from_datetime function.
有什么办法吗，或者我走在完全错误的轨道上，应该更好地使用groupby之类的功能吗?
Is there any way or am I on the completely wrong track and should better use functions like groupby?
编辑以添加:
数据如下所示.长度用作索引.在这些数据帧中，我有一些，因此将它们全部都声明为相同的帧速率"，然后将其剪切例如，这真是太好了.这样我就可以比较不同的数据集.
Data loks like below. Length is usesed as index. Of those Dataframes I have a few so that it woulf be really nice to allign them all to the same "framerate" and then cut them e.g. so that I can compare different datasets.
我已经尝试过的想法就是这个:
The Idea I already tried was this one:
df_1_dt = df_1 #generate a table for the conversion
df_1_dt.index = pd.to_datetime(df_1_dt.index, unit='s') # convert it simulating seconds.. good idea?!
df_1_dt_rs= df_1_dt # generate a df for the resampling
df_1_dt_rs = df_1_dt_rs.resample (rule='s') #resample by the generatet time
数据:
+---------------------------------------------------+
¦ Index (Lenght) ¦ Force1 ¦ Force2 ¦
¦-------------------+---------------+---------------¦
¦ 8.04662074828e-06 ¦ 4.74251270294 ¦ 4.72051584721 ¦
¦ 8.0898882798e-06 ¦ 4.72051584721 ¦ 4.72161570191 ¦
¦ 1.61797765596e-05 ¦ 4.69851899147 ¦ 4.72271555662 ¦
¦ 1.65476570973e-05 ¦ 4.65452528 ¦ 4.72491526604 ¦
¦ 2.41398605024e-05 ¦ 4.67945501539 ¦ 4.72589291467 ¦
¦ 2.42696630876e-05 ¦ 4.70438475079 ¦ 4.7268705633 ¦
¦ 9.60953101751e-05 ¦ 4.72931448619 ¦ 4.72784821192 ¦
¦ 0.00507703541206 ¦ 4.80410369237 ¦ 4.73078115781 ¦
¦ 0.00513927175509 ¦ 4.87889289856 ¦ 4.7337141037 ¦
¦ 0.00868965311878 ¦ 4.9349848032 ¦ 4.74251282215 ¦
¦ 0.00902026197556 ¦ 4.99107670784 ¦ 4.7513115406 ¦
¦ 0.00929150878827 ¦ 5.10326051712 ¦ 4.76890897751 ¦
¦ 0.0291729332784 ¦ 5.14945375919 ¦ 4.78650641441 ¦
¦ 0.0296332588857 ¦ 5.17255038023 ¦ 4.79530513287 ¦
¦ 0.0297080942518 ¦ 5.19564700127 ¦ 4.80410385132 ¦
¦ 0.0362595526707 ¦ 5.2187436223 ¦ 4.80850321054 ¦
¦ 0.0370305483177 ¦ 5.24184024334 ¦ 4.81290256977 ¦
¦ 0.0381506204153 ¦ 5.28803348541 ¦ 4.82170128822 ¦
¦ 0.0444440795306 ¦ 5.30783069134 ¦ 4.83050000668 ¦
¦ 0.0450121369102 ¦ 5.3177292943 ¦ 4.8348993659 ¦
¦ 0.0453465140473 ¦ 5.32762789726 ¦ 4.83929872513 ¦
¦ 0.0515533437013 ¦ 5.33752650023 ¦ 4.85359662771 ¦
¦ 0.05262489708 ¦ 5.34742510319 ¦ 4.8678945303 ¦
¦ 0.0541273847206 ¦ 5.36722230911 ¦ 4.89649033546 ¦
¦ 0.0600755845953 ¦ 5.37822067738 ¦ 4.92508614063 ¦
¦ 0.0607712385295 ¦ 5.38371986151 ¦ 4.93938404322 ¦
¦ 0.0612954159368 ¦ 5.38921904564 ¦ 4.9536819458 ¦
¦ 0.0670288249293 ¦ 5.39471822977 ¦ 4.97457891703 ¦
¦ 0.0683640870058 ¦ 5.4002174139 ¦ 4.99547588825 ¦
¦ 0.0703192637772 ¦ 5.41121578217 ¦ 5.0372698307 ¦
¦ 0.0757871634772 ¦ 5.43981158733 ¦ 5.07906377316 ¦
¦ 0.0766597757545 ¦ 5.45410948992 ¦ 5.09996074438 ¦
¦ 0.077317850103 ¦ 5.4684073925 ¦ 5.12085771561 ¦
¦ 0.0825991083545 ¦ 5.48270529509 ¦ 5.13295596838 ¦
¦ 0.0841354654428 ¦ 5.49700319767 ¦ 5.14505422115 ¦
¦ 0.0865525182528 ¦ 5.52559900284 ¦ 5.1692507267 ¦
+---------------------------------------------------+
推荐答案
听起来，您要做的就是将长度数字四舍五入到较低的精度.
It sounds like all you want to do is round the length figures to a lower precision.
在这种情况下，您可以使用内置的舍入函数:
If this is the case, you could just use the in-built rounding function:
(虚拟数据)
>>> df=pd.DataFrame([[1.0000005,4],[1.232463632,5],[5.234652,9],[5.675322,10]],columns=['length','force'])
>>> df
33: length force
0 1.000001 4
1 1.232464 5
2 5.234652 9
3 5.675322 10
>>> df['rounded_length'] = df.length.apply(round, ndigits=0)
>>> df
34: length force rounded_length
0 1.000001 4 1.0
1 1.232464 5 1.0
2 5.234652 9 5.0
3 5.675322 10 6.0
>>>
然后，您可以使用groupby复制resample()....工作流程:
Then you could replicate the resample().... workflow using groupby:
>>> df.groupby('rounded_length').mean().force
35: rounded_length
1.0 4.5
5.0 9.0
6.0 10.0
Name: force, dtype: float64
通常，仅对日期重新采样IS.如果您将它用于日期以外的其他用途，则可能有一个更优雅的解决方案！
Generally, resample IS just for dates. If you're using it for something other than dates, there's probably a more elegant solution!
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