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# Math Programms
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[Overmaymant.py](overmaymant.py) - calculate loan overpaymant.
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nothing yet
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practica/math/statistic/README.md
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practica/math/statistic/README.md
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### Math|stat tasks
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***
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Here you can find statistic tasks, which can be usefull in econometric calculations
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# Variance
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- [average value](average_value.py) -
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- [Greatest common divisor](Greatest_common_divisor.py) -
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# Statistic tasks
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@ -34,5 +34,3 @@
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# print(i * n, end="\t")
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# print()
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@ -1,127 +0,0 @@
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a = input().split()
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len_a = len(a) - 1
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for index, number in enumerate(a):
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if len_a == 0:
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print (number)
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else:
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if index == 0:
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S = int(a[-1]) + int(a[1])
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b = str(S)
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elif index != 0 and index != int(len_a) :
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n_index_0 = int(int(a.index(number)) + 1)
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n_index_2 = int(int(a.index(number)) - 1)
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n_0 = a[n_index_0]
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N_2 = a[n_index_2]
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S1 = int(n_0) + int(N_2)
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b += " " + str(S1)
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elif index == int(len_a):
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S2 = int(a[0]) + int(a[-2])
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b += " " + str(S2)
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print (b)
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x = [int(m) for m in str(b)]
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print (x) в список
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print (" ".join(b))
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# initial_list = input().split()
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# sum_list = []
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# left_index = -1
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# right_index = -len(initial_list) + 1
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# middle_index = 0
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# while middle_index < len(initial_list):
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# sum_list.append(initial_list[left_index] + initial_list[right_index])
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# left_index += 1
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# right_index += 1
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# middle_index += 1
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# print(sum_list)
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# a = [int(item) for item in input().split()]
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# a2 = []
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# for i in range(len(a)):
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# if len(a) == 1:
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# print(a[0])
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# break
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# else:
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# if i == 0:
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# a2.append(a[-1] + a[i + 1])
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# elif i > 0 and i != len(a) - 1:
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# a2.append(a[i - 1] + a[i + 1])
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# else:
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# a2.append(a[i - 1] + a[0])
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# if a2 != 0:
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# for i in a2:
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# print(i, end=' ')
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# a = input().split()
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# a_sorted= a.sort()
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# int=0
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# for i, item in enumerate(a):
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# if len(a) == 1:
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# None
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# else:
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# # if a [i] == a [i+1]:
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# n =
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# a = input().split()
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# a2 = []
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# for item in a:
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# c = a.count(item)
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# if c > 1:
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# a2.append(item)
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# if c == 1:
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# None
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# def del_dubl(a2):
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# seen = set()
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# seen_add = seen.add
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# return [x for x in a2 if not (x in seen or seen_add(x))]
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# for i in del_dubl(a2):
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# print(i, end=' ')
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# удаление дубликатов
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# a = input().split()
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# def del_dubl(a):
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# seen = set()
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# seen_add = seen.add
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# return [x for x in a if not (x in seen or seen_add(x))]
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# print (del_dubl(a))
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# n =3
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# a = [[0]*n]*n
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# a[0][0]= 5
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# print (a)
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# from scipy.stats import f
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# data = pd.DataFrame({1:[3,1,2],2:[5,3,4],3:[7,6,5]}) # Here 3 groups and we are going to compare them
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# def odno_disp(data):
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# first_group = [i for i in data[1]] # Выделяем группы для операции над данными
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# second_group = [i for i in data[2]]
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# third_group = [i for i in data[3]]
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# number_of_groups = len([first_group,second_group,third_group])
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# all_groups = first_group+second_group+third_group # Все группы тут
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# mean_of_all_groups = np.mean(all_groups) # среднее значение всей группы
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# sum_of_squared_total = sum([(i-mean_of_all_groups)**2 for i in all_groups]) # Обьщая изменчивость наших данных, здесь мы расчитали сумму всех квадратов отклонение от среднего
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# df_of_sst = len(all_groups) - 1 # Число степеней свободы в SST
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# ssw1 = sum([(i-np.mean(first_group))**2 for i in first_group]) # для расчета суммы квадратов
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# ssw2 = sum([(i-np.mean(second_group))**2 for i in second_group]) # расчитаем сумму кв всех групп
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# ssw3 = sum([(i-np.mean(third_group))**2 for i in third_group])
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# sum_of_squared_within = ssw1+ssw2+ssw3 # сумма квадратов внутри групповая
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# df_of_ssw = len(all_groups) - number_of_groups # Число степеней свободы во внутри групповой
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# # Теперь узнаем на сколько наши групповые отклоняются от общегрупповых средних
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# for_minus_from_each_group = [first_group, second_group, third_group] # для минуса из каждых групп
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# sum_of_squared_between = sum([number_of_groups*(np.mean(i)-mean_of_all_groups)**2 for i in for_minus_from_each_group])
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# df_of_ssb = number_of_groups - 1
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# F = (sum_of_squared_between / df_of_ssb) / (sum_of_squared_within / df_of_ssw)
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# P_value = f.sf(F, df_of_ssb, df_of_ssw)
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# if P_value >= 0.05:
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# return f"Мы не отклоняем нулевую гипотезу так как P_value = {P_value}"
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# else:
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# return f"Мы отклоняем нулевую гипотезу то есть P value = {P_value}, H1 верна то есть минимум 2 данные различаются между собой в Генеральной совокупонсти"
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# p = odno_disp(data)
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#dd
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