Applying Complex Function To Several Timeseries

What I am trying to achieve is this: I have several Timeseries, which I need to combine on a per-point-basis and return the result as a single new timeseries. I understand that yo

Solution 1:

You can use map:

ts.map(direction_day)2016-1-10.1666672016-1-20.0000002016-1-30.1666672016-1-40.6666672016-1-50.0000002016-1-6-0.166667

Or apply (produce the same result)

ts.apply(direction_day)

Or apply with lambda (produce the same result)

ts.apply(lambda y: direction_day(y))

Each method will be applied element-wise (for value of a Series) since a Series has only one column. DataFrame have methods working element-wise or by row / column (see this question for more detail). In your case, values of the Series are arrays of arrays, so the entire array will be passed to the function. If you want more control, I suggest using a DataFrame instead of a Series containing an array which is not the preferred way to work in pandas. But your data has more than two dimensions (3), pandas also provide another data structure called Panel but I have never worked with Panel so I cannot help you.

As an example, this kind of array will be passed to your direction_day function:

[[ 1.76405235,  0.40015721,  0.97873798],
       [ 2.2408932 ,  1.86755799, -0.97727788],
       [ 0.95008842, -0.15135721, -0.10321885],
       [ 0.4105985 ,  0.14404357,  1.45427351]]

Solution 2:

ts.apply(direction_day)2016-1-1-0.3333332016-1-2-0.5000002016-1-3-0.3333332016-1-40.0000002016-1-50.1666672016-1-60.6666672016-1-70.1666672016-1-80.1666672016-1-90.3333332016-1-100.0000002016-1-11-0.3333332016-1-120.1666672016-1-13-0.5000002016-1-140.1666672016-1-150.0000002016-1-16-0.3333332016-1-17-0.1666672016-1-18-0.1666672016-1-19-0.1666672016-1-200.000000dtype:float64