diff --git a/Planets b/Planets
new file mode 100644
index 0000000..e30ecb4
--- /dev/null
+++ b/Planets
@@ -0,0 +1,72 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+
+
+file = pd.read_csv("Objects.csv")
+file.head()
+
+
+class CosmicBody:
+    def init(self, m=1, r=np.array([0, 0, 0]), v=np.array([0, 0, 0])):
+        self.m = m
+        self.r = r
+        self.v = v
+
+    def step(self, a, dt):
+        self.r = self.r + dt * self.v + a * dt * dt / 2
+        self.v = self.v + a
+
+
+frame = np.array([])
+for i in file.index:
+    body = CosmicBody()
+    body.r = np.array([file["x"][i], file["y"][i], file["z"][i]])
+    body.v = np.array([file["v_x"][i], file["v_y"][i], file["v_z"][i]])
+    body.m = file["mass"][i]
+    frame = np.append(frame, body)
+print(frame)
+
+
+def acceleration(frame):
+    accel = np.array([])
+    for i in frame:
+        frame1 = frame[frame != i]
+        a = np.array([])
+        for j in frame1:
+            rvec = j.r - i.r
+            a = np.append(
+                a, j.m * rvec / ((rvec[0]  2 + rvec[1]  2 + rvec[2]  2)  (3/2)))
+        a = a.reshape(-1, 3)
+        a = np.sum(a, axis=0)
+        accel = np.append(accel, a)
+    return accel.reshape(-1, 3)
+
+
+def show(frame, t, dt):
+    fig = plt.figure(figsize=(7, 7))
+    ax = fig.add_subplot(111, projection='3d')
+    p = 0.1
+    for t0 in tqdm(np.arange(0., t, dt)):
+        accel = acceleration(frame)
+        p += 0.1
+        angle = 60 + 60 * p / t
+        ax.clear()
+        ax.axes.set_xlabel("X")
+        ax.axes.set_ylabel("Y")
+        ax.axes.set_zlabel("Z")
+        ax.axes.set_xlim3d(-30, 30)
+        ax.axes.set_ylim3d(-30, 30)
+        ax.axes.set_zlim3d(-30, 30)
+        for elem in frame:
+            ax.scatter(elem.r[0], elem.r[1], elem.r[2], s=20)
+            elem.step(accel[frame == elem][0], dt)
+        ax.view_init(angle-60, angle)
+        fig.canvas.draw()
+
+
+if name == 'main':
+    t = 10
+    dt = 0.1
+    show(frame, t, dt)
diff --git a/numpy b/numpy
new file mode 100644
index 0000000..38d7c23
--- /dev/null
+++ b/numpy
@@ -0,0 +1,54 @@
+ import matplotlib.pyplot as plt
+ import numpy as np
+ import scipy.special as sci
+
+
+ def Puasson(lambda, N):
+     if lambda < 0:
+         raise ValueError("Lambda must be positive")
+     numpy_arr = np.arange(N)
+     return lambda ** numpy_arr * np.exp(-lambda) / sci.factorial(numpy_arr)
+
+
+ def Moment(arr, k=1):
+     if k < 0:
+         raise ValueError('"k" must be positive')
+     elif k % 1:
+         raise ValueError('"k" must be integer')
+     elif not isinstance(arr, np.ndarray):
+         raise ValueError('Wrong array format')
+     return np.sum(arr * np.arange(len(arr)) ** k)
+
+
+ def Mean(arr):
+     return Moment(arr, 1)
+
+
+ def Diviation(arr):
+     return Moment(arr, 2) - Moment(arr, 1) ** 2
+
+
+ def Plotter(arr):
+     plt.plot(arr)
+     plt.show()
+
+
+ def Test(x, answer, error=0.1):
+     if abs(x - answer) <= error:
+         return "cовпадает в пределах погрешности " + str(error)
+     return "не cовпадает в пределах погрешности " + str(error)
+
+
+ if __name__ == '__main__':
+     lambda = int(input())
+     N = int(input())
+     k = int(input())
+     error = float(input())
+     arr = Puasson(lambda, N)
+     Plotter(arr)
+     print("Начальный момент случайной великичны для k =",
+           k, "равен", Moment(arr, k))
+     print("Среднее значение равно", Mean(arr),
+           Test(Mean(arr), lambda), "с реальным значением", lambda)
+     print("Дисперсия случайной величины равна", Diviation(Puasson(lambda, N)),
+           "она", Test(Diviation(arr), lambda), "с реальным значением", lambda)
diff --git a/numpy_dec b/numpy_dec
new file mode 100644
index 0000000..389bb0a
--- /dev/null
+++ b/numpy_dec
@@ -0,0 +1,68 @@
+-*- coding: utf-8 -*-
+
+ import matplotlib.pyplot as plt
+ import numpy as np
+ from decimal import Decimal, getcontext
+
+
+ getcontext().prec = 5
+
+
+ def Factorial(arr):
+     n = Decimal('1')
+     factorial = np.array([Decimal(1)])
+     for x in arr[1:]:
+         n *= x
+         factorial = np.append(factorial, n)
+     return factorial
+
+
+ def Puasson(lambda, N):
+     if lambda < 0:
+         raise ValueError("Lambda must be positive")
+     numpy_arrD = np.asarray([Decimal(i) for i in range(N)])
+     return lambda ** numpy_arrD * Decimal(-la).exp() / Factorial(numpy_arrD)
+
+
+ def Moment(arr, k=1):
+     if k < 0:
+         raise ValueError('"k" must be positive')
+     elif k % 1:
+         raise ValueError('"k" must be integer')
+     elif not isinstance(arr, np.ndarray):
+         raise ValueError('Wrong array format')
+     return np.sum(arr * np.arange(len(arr)) ** k)
+
+
+ def Mean(arr):
+     return Moment(arr, 1)
+
+
+ def Diviation(arr):
+     return Moment(arr, 2) - Moment(arr, 1) ** 2
+
+
+ def Plotter(arr):
+     plt.plot(arr)
+     plt.show()
+
+
+ def Test(x, answer, error=0.1):
+     if abs(x - answer) <= error:
+         return "cовпадает в пределах погрешности " + str(error)
+     return "не cовпадает в пределах погрешности " + str(error)
+
+
+ if __name__ == '__main__':
+     lambda = int(input())
+     N = int(input())
+     k = int(input())
+     error = float(input())
+     arr = Puasson(la, N)
+     Plotter(arr)
+     print("Начальный момент случайной великичны для k =",
+           k, "равен", Moment(arr, k))
+     print("Среднее значение равно", Mean(arr),
+           Test(Mean(arr), lambda), "с реальным значением", lambda)
+     print("Дисперсия случайной величины равна", Diviation(arr),
+           Test(Diviation(arr), lambda), "с реальным значением", lambda)