Implementing Pandas Function To Numpy Functions

Is there a way I could convert the xy_mean function to be computed using the pandas library just like the y_mean function. I found out that the pandas function Y_mean = pd.Series(P

Solution 1:

You would need to define a custom function for that, and pass it to rolling.apply:

>>> multiplier = np.arange(0, number)

>>> def xymean(series):
        return series.mul(multiplier).sum()

>>> pd.Series(PC_list).rolling(number).apply(xymean).dropna().to_numpy()[:-1]

array([2490.601989, 2440.743958, 2409.067016, 2413.002044, 2510.497985,
       2543.348939, 2516.922974, 2459.627961, 2418.983948, 2335.007966,
       2280.283019, 2288.94702 , 2300.19998 , 2279.389953, 2212.294951,
       2080.693968, 1978.774017, 1960.123047, 1989.229066, 2061.27304 ,
       2137.145019, 2167.67804 , 2175.047058, 2221.807067, 2290.639036,
       2361.986998, 2376.473021])

>>> (np.convolve(PC_list, np.arange(number, 0, -1), mode='valid'))[:-1]
 
array([2490.601989, 2440.743958, 2409.067016, 2413.002044, 2510.497985,
       2543.348939, 2516.922974, 2459.627961, 2418.983948, 2335.007966,
       2280.283019, 2288.94702 , 2300.19998 , 2279.389953, 2212.294951,
       2080.693968, 1978.774017, 1960.123047, 1989.229066, 2061.27304 ,
       2137.145019, 2167.67804 , 2175.047058, 2221.807067, 2290.639036,
       2361.986998, 2376.473021])

However, this will be a little slower, owing to the apply. Furthermore, it seems like your numpy version creates xy_sum as opposed to xy_mean, to make it calculate mean you would need:

>>> (np.convolve(PC_list, np.arange(number, 0, -1), mode='valid')/number)[:-1]

array([830.200663  , 813.58131933, 803.02233867, 804.33401467,
       836.83266167, 847.78297967, 838.97432467, 819.875987  ,
       806.32798267, 778.33598867, 760.09433967, 762.98234   ,
       766.73332667, 759.796651  , 737.43165033, 693.564656  ,
       659.591339  , 653.374349  , 663.07635533, 687.09101333,
       712.381673  , 722.55934667, 725.015686  , 740.60235567,
       763.54634533, 787.32899933, 792.15767367])

>>> def xymean(series):
        return series.mul(multiplier).mean()

>>> pd.Series(PC_list).rolling(number).apply(xymean).dropna().to_numpy()[:-1]

array([830.200663  , 813.58131933, 803.02233867, 804.33401467,
       836.83266167, 847.78297967, 838.97432467, 819.875987  ,
       806.32798267, 778.33598867, 760.09433967, 762.98234   ,
       766.73332667, 759.796651  , 737.43165033, 693.564656  ,
       659.591339  , 653.374349  , 663.07635533, 687.09101333,
       712.381673  , 722.55934667, 725.015686  , 740.60235567,
       763.54634533, 787.32899933, 792.15767367])