How to access individual elements within a rolling window on a dataframe

Using a lambda expression within .apply() will pass an array into the custom function (find_recession_start), and so I can just access the elements as I would any list/array e.g. arr[0], arr[1], arr[2]

df = pd.DataFrame(data=np.random.randint(0,10,10), columns=['GDP'])def my_func(arr):    if((arr[2] < arr[1]) & (arr[1] < arr[0])):        return 1    else:        return 0df['Result'] = df.rolling(window=3).apply(lambda x: my_func(x))df    GDP Result0   8   NaN1   0   NaN2   8   0.03   1   0.04   9   0.05   7   0.06   9   0.07   8   0.08   3   1.09   9   0.0

The short answer is: you can't, but you can use your knowledge about the structure of the dataframe/series.

You know the size of the window, you know the current index - therefore, you can output the shift relative to the current index:

Let's pretend, here is your gdp:

In [627]: gdpOut[627]:0    81    02    03    44    05    36    67    28    59    5dtype: int64

The naive approach is just to return the (argmin() - 2) and add it to the current index:

In [630]: gdp.rolling(window=3).apply(lambda win: win.argmin() - 2) + gdp.indexOut[630]:0    NaN1    NaN2    1.03    1.04    2.05    4.06    4.07    7.08    7.09    7.0dtype: float64

The naive approach won't return the correct result, since you can't predict which index it would return when there are equal values, and when there is a rise in the middle. But you understand the idea.