使用熊猫重新采样时间序列

Question

我必须使用熊猫下采样以下时间序列：

Time                            ch1     ,ch2   ,ch3     ,ch4
09/19/2019 22:00:00.000000000  ,0       ,0     ,8675601 ,0
09/19/2019 22:00:00.000976562  ,8028645 ,0     ,8662525 ,7706467
09/19/2019 22:00:00.001953124  ,8027705 ,0     ,0       ,7704373
09/19/2019 22:00:00.001953125  ,0       ,0     ,8685515 ,0
09/19/2019 22:00:00.002929687  ,8028089 ,0     ,8699659 ,7704202
09/19/2019 22:00:00.003906249  ,8027918 ,0     ,0       ,7705569
09/19/2019 22:00:00.003906250  ,0       ,0     ,8703334 ,0

由于csv文件很大，因此我使用以下代码读取每个块，然后尝试将其缩减大小：

def resampleSignal(inPath,separator,firstDataLine,chunkSize):
    columsDataFrame=[]
    tempIndex=0
    for chunk in tqdm(pd.read_csv(inPath,skiprows=range(0,firstDataLine),chunksize=chunkSize,sep=separator)):
        columsDataFrame=chunk.columns
        chunk.index = pd.to_datetime(chunk.index, unit='ns')
        resampled = pd.DataFrame()
        resampled=chunk.resample('1S').last()
        resampled_np=(resampled).values
        if tempIndex==0:
            finalDataSet=np.array(resampled_np)
        else:
            finalDataSet=np.append(finalDataSet,np.array(resampled_np),axis=0)
        tempIndex+=1
    return finalDataSet

问题是，无论我是否更改参数'1S'，输出始终为：

Time                          ,ch1       ,ch2       ,ch3       ,ch4
09/19/2019 12:03:21.906250000 ,8471473.0 ,5633804.0 ,8578007.0  ,7515027.0
09/19/2019 12:16:20.657226562 ,8463397.0 ,5616594.0 ,8582878.0  ,7536395.0
09/19/2019 12:28:45.581054687 ,7711094.0 ,0.0       ,16777215.0 ,7773021.0
09/19/2019 12:41:04.551757812 ,7690984.0 ,5697459.0 ,16777215.0 ,7795462.0

基本上，它总是获取块的最后一行，而不是每秒获取一行。我当前的熊猫版本为0.25.3，如果我打印重采样器对象chunk.resample('1S')，则会得到以下输出：

DatetimeIndexResampler [freq=<Second>, axis=0, closed=left, label=left, convention=start, base=0]

所以我知道它使用的是右轴。我究竟做错了什么？预先感谢您的帮助！

Answer 1

好吧，我意识到自己的错误，我试图对错误的chunk.index进行时间采样，我将代码修改如下：

def resampleSignal(inPath,separator,firstDataLine,chunkSize):
    columsDataFrame=[]
    tempIndex=0
    for chunk in tqdm(pd.read_csv(inPath,skiprows=range(0,firstDataLine),chunksize=chunkSize,sep=separator)):
        columsDataFrame=chunk.columns
        chunk.Time = pd.Index(pd.to_datetime(chunk.Time, unit='ns'))
        resampled = pd.DataFrame()
        resampled=chunk.resample('100L', on='Time').last()
        resampled_np=(resampled).values
        if tempIndex==0:
            finalDataSet=np.array(resampled_np)
        else:
            finalDataSet=np.append(finalDataSet,np.array(resampled_np),axis=0)
        tempIndex+=1
    return finalDataSet