A Dataclass example

caller.py

series = [1,2,3,4,5]

stats = Statistics(series)
mean = stats.mean()
dev = stats.dev()

stats.py

import numpy as np
from scipy.stats import skew, kurtosis
from .errors import (StepSizeError, 
                     StepTypeError,
                     SeriesError)

def create_stats(series: list, step: int):
    if step:
        if type(step) is not int:
            raise StepTypeError("Type of step invalid")
        elif step > self.length:
            raise StepSizeError("Invalid step size ")
    elif series == None or len(series) == 0:
        raise SeriesError("Empty/Invalid Series")
    else:
        return Statistics(series=series, step=step)

class Statistics:
    def __init__(self, series: list, step: int):
        self.step = step
        self.series = series
        self.length = len(self.series)

    def mean(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            average = np.mean(series)
            return average
        else:
            averages = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                average = np.mean(temp)
                averages.append(average)
            return averages

    def deviation(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            std = np.std(series)
            return std
        else:
            standard_deviations = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                deviation = np.std(temp)
                standard_deviations.append(deviation)
            return standard_deviations

    def variance(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            variances = np.var(series)
            return variances
        else:
            variances = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                variance = np.var(temp)
                variances.append(variance)
            return variances

    def skewness(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            skewness = skew(series)
            return skewness
        else:
            skewnesses = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                skewness = skew(temp)
                skewnesses.append(skewness)
            return skewnesses

    def kurtosis(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            kurt = kurtosis(series)
            return kurt
        else:
            kurts = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                kurt = kurtosis(temp)
                kurts.append(kurt)
            return kurts
    
    def minimum(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            minimum = min(series)
            return minimum
        else:
            minimums = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                minimum = min(temp)
                minimums.append(minimum)
            return minimums

    def maximum(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
            return max(series)
        else:
            maximums = []
            for i in range(0, length, step):
                temp = series[i:i+step]
                maxval = max(temp)
                maximums.append(maxval)
            return maximums
                
    def rangeval(self):
        step = self.step
        length = self.length
        series = self.series
        if step is (None or 0 or length):
                rangevalue= max(series) - min(series)
                return rangevalue
        rangevals = []
        for i in range(0, length, step):
            temp = series[i:i+step]
            rangevalue = max(temp)-min(temp)
            rangevals.append(rangevalue)
        return rangevals    

    def all(self):
        stats = {
            'means': self.mean(),
            'deviations': self.deviation(),
            'minimum': self.minimum(),
            'maximum': self.maximum(),
            'range': self.rangeval(),
            'skewness': self.skewness(),
            'kurtosis': self.kurtosis()
        }
        return stats