将数据帧的所有内容传递到 pandas 中的函数 [英] Pass entire contents of a dataframe to a function in Pandas
问题描述
我正在尝试使用Dataframes而不是numpy数组对我的分析代码进行信号处理。但是，我很难弄清楚如何将整个数据帧矩阵作为整个单元传递给一个函数。
I'm trying to rework much of my analysis code for signal processing using Dataframes instead of numpy arrays. However, I'm having a hard time figuring out how to pass the entire matrix of a dataframe to a function as an entire unit.
例如，如果我正在计算通用平均参考值的信号，我有一些像：
E.g., If I'm computing the common average reference a signal, I have something like:
avg = signal.mean(axis=1)
CAR = signal - avg
我想做的是将一个熊猫数组传递给这个函数，并使它返回一个具有CAR值的数据帧作为值。我想这样做，而不用返回一个数组，然后将其重新转换回数据框。
What I'd like to do is pass a pandas array to this function and have it return a dataframe with CAR as the values now. I'd like to do this without just returning an array and then re-converting it back into a dataframe.
这听起来像是使用df.apply（）它是行方式或列方式，并不放在整个矩阵中。我可以改变CAR的代码来使这个工作，但它似乎会减慢相当一点，而不是只是使用numpy的代码一次性完成所有这一切。这可能不会对计算平均值产生很大的影响，但是我预计这是未来可能需要更长时间的其他功能的问题。
It sounds like when you use df.apply(), it goes row-wise or column-wise, and doesn't put in the whole matrix. I could alter the code of CAR to make this work, but it seems like it would slow it down quite a bit rather than just using numpy's code to do it all at once. It probably wouldn't make a big difference for computing the mean, but I foresee this being a problem with other functions in the future that might take longer.
任何人都可以指出我在正确的方向？
Can anyone point me in the right direction?
编辑：澄清，我不只是为了减去平均值，这只是一个简单的例子。一个更逼真的例子是沿轴线0对阵列进行线性过滤。我想使用scipy.signal filtfilt函数来过滤我的数组。这很容易，如果我可以传递一个tpts x专长矩阵，但现在似乎唯一的方法是列列使用应用
To clarify, I'm not just doing this for subtracting the mean, it was just a simple example. A more realistic example would be linearly filtering the array along axis 0. I'd like to use the scipy.signal filtfilt function to filter my array. This is quite easy if I can just pass it a tpts x feats matrix, but right now it seems that the only way to do it is column-wise using "apply"
推荐答案
您可以使用 df.values 获取DataFrame的原始numpy数组版本。然而，在许多情况下，您可以传递DataFrame本身，因为它仍然允许使用正常的numpy API（即它具有所有正确的方法）。
You can get the raw numpy array version of a DataFrame with df.values. However, in many cases you can just pass the DataFrame itself, since it still allows use of the normal numpy API (i.e., it has all the right methods).
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