diff --git a/Numpy (26.02)/Numpy_Task.ipynb b/Numpy (26.02)/Numpy_Task.ipynb
index 593ba20..c105af2 100644
--- a/Numpy (26.02)/Numpy_Task.ipynb	
+++ b/Numpy (26.02)/Numpy_Task.ipynb	
@@ -1,470 +1,323 @@
+
 {
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "medieval-detail"
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "abstract-istanbul"
-   },
-   "source": [
-    "1. Задание\n",
-    "- проверьте тип объекта python_list\n",
-    "- если это не numpy ndarray, то преобразуйте к нему\n",
-    "- проверьте, что на выходе вы получили объект python_list желаемого типа"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "entertaining-automation"
-   },
-   "outputs": [],
-   "source": [
-    "python_list = [1, 12, 13, 45, 76, 45, 98, 0]\n",
-    "print()\n",
-    "python_list = \n",
-    "print()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "loose-tobago"
-   },
-   "source": [
-    "2. Задание\n",
-    "- создайте одномерный массив numpy из 10 элементов заполненный числом 1.5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "included-polymer"
-   },
-   "outputs": [],
-   "source": [
-    "z = \n",
-    "print(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "threatened-theme"
-   },
-   "source": [
-    "3. Задание\n",
-    "- создайте двумерный массив numpy из 25 элементов заполненный числом 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "alert-endorsement"
-   },
-   "outputs": [],
-   "source": [
-    "z = \n",
-    "print(z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "federal-blackberry"
-   },
-   "source": [
-    "4. Задание\n",
-    "- создайте одномерный массив numpy из 12 элементов заполненный числом 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "static-filing"
-   },
-   "outputs": [],
-   "source": [
-    "ones = \n",
-    "print(ones)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "whole-chassis"
-   },
-   "source": [
-    "5. Задание\n",
-    "- преобразуйте массив из предыдущего задания к матрице 3 на 4\n",
-    "- выведите размерность полученного массива"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "outstanding-deviation"
-   },
-   "outputs": [],
-   "source": [
-    "ones = \n",
-    "ones.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cubic-noise"
-   },
-   "source": [
-    "6. Задание\n",
-    "- создайте матрицу 4 на 5 заполненную числами от 1 до 20\n",
-    "- замените элемент в 3-й строке 4-м столбце на -99"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "foster-memory"
-   },
-   "outputs": [],
-   "source": [
-    "Z = \n",
-    "print(Z)\n",
-    "\n",
-    "print(Z)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "helpful-table"
-   },
-   "source": [
-    "7. Задание\n",
-    "- создайте вектор длинной 15 со случайными целыми числами в диапазоне от -10 до 10\n",
-    "- сделайте реверс вектора"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "magnetic-leone"
-   },
-   "outputs": [],
-   "source": [
-    "first = \n",
-    "print(first)\n",
-    "second = \n",
-    "print(second)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "executed-september"
-   },
-   "source": [
-    "8. Задание\n",
-    "- создайте матрицу 5 на 5 со случайными целыми числами в диапазоне от -15 до 15\n",
-    "- замените отрицательные элементы на их квадраты"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "pharmaceutical-sigma"
-   },
-   "outputs": [],
-   "source": [
-    "first = \n",
-    "print(first)\n",
-    "\n",
-    "print(first)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "floral-difference"
-   },
-   "source": [
-    "9. Задание\n",
-    "- создайте матрицу 3 на 5 со случайными целыми числами в диапазоне от -15 до 15\n",
-    "- найдите максимум и минимум\n",
-    "- найдите среднее по столбцам\n",
-    "- найдите среднее по строкам"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "saving-conference"
-   },
-   "outputs": [],
-   "source": [
-    "first = \n",
-    "print(first)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "diagnostic-departure"
-   },
-   "source": [
-    "10. Задание\n",
-    "- даны две матрицы\n",
-    "- убедитесь, что их можно пермножить\n",
-    "- произведите операцию умножения матриц, если это возможно, иначе выведите ошибку"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "olympic-qatar"
-   },
-   "outputs": [],
-   "source": [
-    "a = np.random.randint(-10, 10, (2, 5))\n",
-    "first_axis = np.random.randint(4, 6)\n",
-    "b = np.random.randint(-10, 10, (first_axis, 3))\n",
-    "if :\n",
-    "    print(a @ b)\n",
-    "else:\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "governmental-austin"
-   },
-   "source": [
-    "11. Задание\n",
-    "- создайте матрицу 5 на 5 заполненную равномерным распределением с диапазоно 2-12\n",
-    "- заполните все элементы ниже главной диагонали нулями"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "suffering-mauritius"
-   },
-   "outputs": [],
-   "source": [
-    "mask = \n",
-    "matrix = \n",
-    "\n",
-    "print(matrix)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "altered-baghdad"
-   },
-   "source": [
-    "12. Задание\n",
-    "- создайте матрицу 5 на 5 заполненную нормальным распределением с парметрами среднее 10 и отклонение 2\n",
-    "- заполните все элементы на главной диагонали нулями"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "refined-stuff"
-   },
-   "outputs": [],
-   "source": [
-    "mask = \n",
-    "matrix = \n",
-    "\n",
-    "print(matrix)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "quiet-complement"
-   },
-   "source": [
-    "13. Задание\n",
-    "- дано два массива, проверить одинаковы ли они"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "french-fighter"
-   },
-   "outputs": [],
-   "source": [
-    "a = np.random.randint(0,2,5)\n",
-    "print(a)\n",
-    "b = np.random.randint(0,2,5)\n",
-    "print(b)\n",
-    "equal = \n",
-    "equal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "color-amplifier"
-   },
-   "source": [
-    "14. Задание\n",
-    "- сгенерируйте матрицу случайного размера:\n",
-    " - строк от 3 до 7\n",
-    " - столбцов от 2 до 12\n",
-    "- сгенерируйте число N - размер выборки так, чтобы оно не превышало половину количества элементов сгенерированной матрицы, но было больше 0\n",
-    "- сделайте случайную выборку N элементов"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "close-daisy"
-   },
-   "outputs": [],
-   "source": [
-    "r, c = \n",
-    "a = \n",
-    "print(a)\n",
-    "N = \n",
-    "print(N)\n",
-    "sample = \n",
-    "print(sample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "patent-african"
-   },
-   "source": [
-    "15. Задание\n",
-    "- дан вектор, проверить есть в нем Nan\n",
-    "- проверить есть в нем знак бесконечности\n",
-    "- заполнить их нулями"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "taken-fabric"
-   },
-   "outputs": [],
-   "source": [
-    "a = np.array([1, np.NaN, np.Inf], float)\n",
-    "\n",
-    "\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "analyzed-ireland"
-   },
-   "source": [
-    "16. Задание\n",
-    "- сгенерировать массив с произвольным числом осей\n",
-    "- определить сколько у него осей"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "imposed-digest"
-   },
-   "outputs": [],
-   "source": [
-    "axis = \n",
-    "print(axis)\n",
-    "matrix = \n",
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "regulation-colleague"
-   },
-   "source": [
-    "17. Задание\n",
-    "- сгенерируйте матрицу 10 на 3 с нормальным распределением, среднее 50, отклонение 10\n",
-    "- найдите индексы максимальных элементов в каждой строке\n",
-    "- найдите сами элементы"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "concerned-anthropology"
-   },
-   "outputs": [],
-   "source": [
-    "matrix = np.random.normal(50, 10, (10,3))\n",
-    "print(matrix)\n",
-    "indexes = \n",
-    "print(indexes)\n",
-    "print(...)"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "01_task.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
+  "metadata": {
+    "kernelspec": {
+      "name": "python",
+      "display_name": "Pyolite",
+      "language": "python"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "python",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8"
+    }
+  },
+  "nbformat_minor": 5,
+  "nbformat": 4,
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": "import numpy as np\nimport random",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 1,
+      "outputs": [],
+      "id": "b77bbab2-39a5-4a4b-967d-689fed59a792"
+    },
+    {
+      "cell_type": "code",
+      "source": "python_list = [1, 12, 13, 45, 76, 45, 98, 0]\nprint(type(python_list))\nif type(python_list) is not 'numpy.ndarray':\n    python_list = np.array(python_list)\nprint(type(python_list))",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "name": "stderr",
+          "text": "<>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n<>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "<class 'list'>\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stderr",
+          "text": "<ipython-input-2-b708454e0338>:3: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n  if type(python_list) is not 'numpy.ndarray':\n",
+          "output_type": "stream"
+        },
+        {
+          "name": "stdout",
+          "text": "<class 'numpy.ndarray'>\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "9e139854-ef2a-4f80-b6ce-569f68896efa"
+    },
+    {
+      "cell_type": "code",
+      "source": "z = np.full(10,1.5)\nprint(z)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.5]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "b5bbcb29-031a-45ee-bcc9-12a194e21d39"
+    },
+    {
+      "cell_type": "code",
+      "source": "z = np.zeros((2, 25))\nprint(z)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n  0.]\n [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n  0.]]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "4526b219-8b1c-4ac2-8b1f-ee127f68e5f5"
+    },
+    {
+      "cell_type": "code",
+      "source": "ones = np.full(12,1)\nprint(ones)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[1 1 1 1 1 1 1 1 1 1 1 1]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "bbcb3047-4ec9-4c12-bde9-d3ee229b5fa6"
+    },
+    {
+      "cell_type": "code",
+      "source": "ones = ones.reshape(3,4)\nprint(ones.shape)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "(3, 4)\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "a2818e91-f4dd-468d-8c85-9db855abec8a"
+    },
+    {
+      "cell_type": "code",
+      "source": "Z = np.arange(1,21,1).reshape(4,5)\nprint(Z)\nZ[2][3]=-99\nprint(Z)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[ 1  2  3  4  5]\n [ 6  7  8  9 10]\n [11 12 13 14 15]\n [16 17 18 19 20]]\n[[  1   2   3   4   5]\n [  6   7   8   9  10]\n [ 11  12  13 -99  15]\n [ 16  17  18  19  20]]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "155cd343-d009-4446-8765-02908b88039e"
+    },
+    {
+      "cell_type": "code",
+      "source": "first = np.random.randint(-10, 10, 15)\nprint(first)\nsecond = first[::-1]\nprint(second)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[ 5 -3 -9  8 -8 -7 -2 -1  4  3  2  1  0  8 -9]\n[-9  8  0  1  2  3  4 -1 -2 -7 -8  8 -9 -3  5]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "76fdc4ef-55c1-46da-a4fa-96950f02125c"
+    },
+    {
+      "cell_type": "code",
+      "source": "first = np.random.randint(-15, 15, 25).reshape(5,5)\nprint(first)\nfirst=np.array([i*i if i<0 else i for i in first.ravel()]).reshape(5,5)\nprint(first)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[ -8   8  -8 -13   3]\n [-11  12 -10  12  -7]\n [-15  -7   6 -15 -12]\n [ -9  11   7 -15 -13]\n [ 14   7  -2  -8  -4]]\n[[ 64   8  64 169   3]\n [121  12 100  12  49]\n [225  49   6 225 144]\n [ 81  11   7 225 169]\n [ 14   7   4  64  16]]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "d1b07a8b-6fab-495e-9276-9d8179d39169"
+    },
+    {
+      "cell_type": "code",
+      "source": "first = np.random.randint(-15, 15, 15).reshape(3,5)\nprint(first)\nprint(first.max(),first.min(),first.mean(axis=0),first.mean(axis=1))",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[  0   2 -13  -7  -8]\n [  4   5  10 -15  -1]\n [ 14 -12  -9 -12  -3]]\n14 -15 [  6.          -1.66666667  -4.         -11.33333333  -4.        ] [-5.2  0.6 -4.4]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "c5dbc0ea-e2ec-4f09-b8aa-ea4d296faacd"
+    },
+    {
+      "cell_type": "code",
+      "source": "a = np.random.randint(-10, 10, (2, 5))\nfirst_axis = np.random.randint(4, 6)\nb = np.random.randint(-10, 10, (first_axis, 3))\nprint(a)\nprint(b)\nprint(a.shape,b.shape)\nif a.shape[1]==b.shape[0] :\n    print(a.dot(b))\nelse:\n    print(\"Ошибка!\")",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[  1   0   3  -7 -10]\n [  5  -4  -1   1  -9]]\n[[ -6   8   4]\n [ -5   9  -3]\n [  4   7   9]\n [-10   8   4]]\n(2, 5) (4, 3)\nОшибка!\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "1e04cc55-79a3-46cc-a3e2-b6579e914c26"
+    },
+    {
+      "cell_type": "code",
+      "source": "mask = np.random.uniform(2, 12, (5, 5)) \nprint(mask)\nmask[np.tril_indices(5, -1)] = 0\nmatrix = mask\nprint(matrix)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[ 7.06701872  3.59655659  6.41418417 10.22936981  4.55008583]\n [ 7.96916149  9.97171939  4.79134822  7.02051113  8.97131958]\n [ 7.55660222  7.16789098 10.2498419   8.71048664  6.4692685 ]\n [11.06650925  8.86514045  3.69571205  9.16825325  5.01901031]\n [11.8748883   9.44007247  8.22611336 10.66717289  6.14917873]]\n[[ 7.06701872  3.59655659  6.41418417 10.22936981  4.55008583]\n [ 0.          9.97171939  4.79134822  7.02051113  8.97131958]\n [ 0.          0.         10.2498419   8.71048664  6.4692685 ]\n [ 0.          0.          0.          9.16825325  5.01901031]\n [ 0.          0.          0.          0.          6.14917873]]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "151f05ca-5d10-4300-9467-21072ac9fadb"
+    },
+    {
+      "cell_type": "code",
+      "source": "mask =np.random.normal(10, 2, 25).reshape(5,5)\nprint(mask)\nmask[np.diag_indices(5)] = 0\nmatrix = mask\nprint(matrix)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[ 9.91316714  6.34550372  7.76155774 10.54823626 11.31871242]\n [ 5.31480719  6.91699947 10.31226178  8.64243223 10.23317928]\n [ 8.69067595 10.44768691  9.0068715   7.82252434  9.8078059 ]\n [12.21729204 10.13425224  9.94251486 12.56975161  7.51103498]\n [ 9.41256464  9.18163338 14.53528573 10.53827169  9.12362772]]\n[[ 0.          6.34550372  7.76155774 10.54823626 11.31871242]\n [ 5.31480719  0.         10.31226178  8.64243223 10.23317928]\n [ 8.69067595 10.44768691  0.          7.82252434  9.8078059 ]\n [12.21729204 10.13425224  9.94251486  0.          7.51103498]\n [ 9.41256464  9.18163338 14.53528573 10.53827169  0.        ]]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "ee145cb7-5e41-4135-98d2-6c4bd343606a"
+    },
+    {
+      "cell_type": "code",
+      "source": "a = np.random.randint(0,2,5)\nprint(a)\nb = np.random.randint(0,2,5)\nprint(b)\nequal = np.array_equal(a,b)\nprint(equal)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[0 1 1 1 0]\n[1 1 1 1 0]\nFalse\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "c712f0c2-0d12-49a0-a23a-0052c718bff0"
+    },
+    {
+      "cell_type": "code",
+      "source": "r, c = random.randint(3,7),random.randint(2,12)\na = np.random.randint(-15, 15, r*c).reshape(r,c)\nprint(a)\nN = r*c//2\nprint(N)\ns=set()\ncount=0\nsample=[]\nwhile True:\n    i, j = random.randint(0,r-1),random.randint(0,c-1)\n    if (i, j) not in s:\n        s.add((i, j))\n        count+=1\n        sample.append(a[i][j])\n    if N == count:\n        break\nprint(sample)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[-13   8   2  13  -5   8   1  11   3  -5]\n [-12  12   0   4  -3 -12  -6  14   0   1]\n [ -3   4 -10   9   4   0   8 -11 -11   5]\n [ -7  -4  -7  -7   3  -7  13 -14   1  13]]\n20\n[-11, -7, 8, 2, 13, 4, 13, 4, 0, -4, 1, -5, 9, -11, 1, 14, -5, -14, 0, 8]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "bf6e125e-7cf9-4194-a3b5-5cc1cc5b15ab"
+    },
+    {
+      "cell_type": "code",
+      "source": "a = np.array([1, np.NaN, np.Inf], float)\nprint(a)\na[np.isnan(a)]=0\na[np.isinf(a)]=0\nprint(a)\n",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[ 1. nan inf]\n[1. 0. 0.]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "e9501ed6-753c-42ff-ba77-3a5db9e58324"
+    },
+    {
+      "cell_type": "code",
+      "source": "axis = random.randint(1,5)\nprint(axis)\nmatrix = np.arange(np.math.factorial(axis)).reshape([i for i in range(1,axis+1)])\nprint(matrix)\nprint(matrix.ndim)",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 55,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "2\n[[0 1]]\n2\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "32710f81-d093-4bc3-8fba-5ebb4557e237"
+    },
+    {
+      "cell_type": "code",
+      "source": "matrix = np.random.normal(50, 10, (10,3))\nprint(matrix)\nindexes =np.argmax(matrix,axis=1) \nprint(indexes)\nprint(np.amax(matrix,axis=1))",
+      "metadata": {
+        "trusted": true
+      },
+      "execution_count": 34,
+      "outputs": [
+        {
+          "name": "stdout",
+          "text": "[[60.31706356 56.91231409 56.07774197]\n [41.01603076 51.89106166 56.70132702]\n [63.08147555 48.39991647 43.32159037]\n [38.3121983  62.57399301 57.61303084]\n [40.70319707 50.69338586 60.46488882]\n [56.54690773 40.43941707 47.10681669]\n [57.85355808 64.56475082 62.27495569]\n [55.56369408 19.60753686 51.00817838]\n [71.97141348 47.91661657 45.45307281]\n [42.54217472 36.90909217 70.41712757]]\n[0 2 0 1 2 0 1 0 0 2]\n[60.31706356 56.70132702 63.08147555 62.57399301 60.46488882 56.54690773\n 64.56475082 55.56369408 71.97141348 70.41712757]\n",
+          "output_type": "stream"
+        }
+      ],
+      "id": "ee570d7c-a7a6-44b4-aef1-0fa0456c251d"
+    }
+  ]
 }