Numpy work with library.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy  کار با کتابخانه  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.random.seed(0) # seed -> تولید مجدد دیتا\n",
    "x1 = np.random.randint(10, size=6) # یک بعدی array\n",
    "x2 = np.random.randint(10, size=(3, 4)) # دو بعدی array\n",
    "x3 = np.random.randint(10, size=(3, 4, 5)) # سه بعدی array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 3, 3, 7, 9])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 2, 4],\n",
       "       [7, 6, 8, 8],\n",
       "       [1, 6, 7, 7]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[8, 1, 5, 9, 8],\n",
       "        [9, 4, 3, 0, 3],\n",
       "        [5, 0, 2, 3, 8],\n",
       "        [1, 3, 3, 3, 7]],\n",
       "\n",
       "       [[0, 1, 9, 9, 0],\n",
       "        [4, 7, 3, 2, 7],\n",
       "        [2, 0, 0, 4, 5],\n",
       "        [5, 6, 8, 4, 1]],\n",
       "\n",
       "       [[4, 9, 8, 1, 1],\n",
       "        [7, 9, 9, 3, 6],\n",
       "        [7, 2, 0, 3, 5],\n",
       "        [9, 4, 4, 6, 4]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x3 ndim:  3\n",
      "x3 shape: (3, 4, 5)\n",
      "x3 size:  60\n"
     ]
    }
   ],
   "source": [
    "print(\"x3 ndim: \", x3.ndim)\n",
    "print(\"x3 shape:\", x3.shape)\n",
    "print(\"x3 size: \", x3.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x2 ndim:  2\n",
      "x2 shape: (3, 4)\n",
      "x2 size:  12\n"
     ]
    }
   ],
   "source": [
    "print(\"x2 ndim: \", x2.ndim)\n",
    "print(\"x2 shape:\", x2.shape)\n",
    "print(\"x2 size: \", x2.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x1 ndim:  1\n",
      "x1 shape: (6,)\n",
      "x1 size:  6\n"
     ]
    }
   ],
   "source": [
    "print(\"x1 ndim: \", x1.ndim)\n",
    "print(\"x1 shape:\", x1.shape)\n",
    "print(\"x1 size: \", x1.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "print(\"dtype:\", x3.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "itemsize: 4 bytes\n",
      "nbytes: 240 bytes\n"
     ]
    }
   ],
   "source": [
    "print(\"itemsize:\", x3.itemsize, \"bytes\")\n",
    "print(\"nbytes:\", x3.nbytes, \"bytes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 3, 3, 7, 9])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 2, 4],\n",
       "       [7, 6, 8, 8],\n",
       "       [1, 6, 7, 7]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  5,  2,  4],\n",
       "       [ 7,  6,  8,  8],\n",
       "       [ 1,  6,  7,  7]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[0, 0] = 12\n",
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 3, 3, 7, 9])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1[0] = 3.14159\n",
    "x1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subarray دسترسی به"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# یک بعدی"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[4:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[::-1]# تمام مقادیر برعکس می شوند"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# چند بعدی"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 2, 4],\n",
       "       [7, 6, 8, 8],\n",
       "       [1, 6, 7, 7]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 2],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[:2, :3]# دو سطر، سه ستون"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  2],\n",
       "       [ 7,  8],\n",
       "       [ 1,  7]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[:3, ::2]# تمامی سطرها، ستون ها یکی در میان"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 7, 6, 1],\n",
       "       [8, 8, 6, 7],\n",
       "       [4, 2, 5, 3]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x2[::-1, ::-1] #  همه با هم برعکس"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 7 1]\n"
     ]
    }
   ],
   "source": [
    "print(x2[:, 0]) # اولین ستون x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12  5  2  4]\n"
     ]
    }
   ],
   "source": [
    "print(x2[0]) #  x2[0, :]  برابر با"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshaping of Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "grid = np.arange(1, 10).reshape((3, 3))\n",
    "print(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "x.reshape((3, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Array Concatenation and Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1.Concatenation of arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 3, 2, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "y = np.array([3, 2, 1])\n",
    "np.concatenate([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  3  2  1 99 99 99]\n"
     ]
    }
   ],
   "source": [
    "z = [99, 99, 99]\n",
    "print(np.concatenate([x, y, z]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "grid = np.array([[9, 8, 7],\n",
    "                 [6, 5, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [9, 8, 7],\n",
       "       [6, 5, 4]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([x, grid])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([[99],\n",
    "              [99]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9,  8,  7, 99],\n",
       "       [ 6,  5,  4, 99]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack([grid, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting of arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3] [99 99  3] [2 1]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3, 99, 99, 3, 2, 1]\n",
    "x1, x2, x3 = np.split(x, [3, 6])\n",
    "print(x1, x2, x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = np.arange(16).reshape((4, 4))\n",
    "grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3]\n",
      " [4 5 6 7]]\n",
      "[[ 8  9 10 11]\n",
      " [12 13 14 15]]\n"
     ]
    }
   ],
   "source": [
    "upper, lower = np.vsplit(grid, [2])\n",
    "print(upper)\n",
    "print(lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1]\n",
      " [ 4  5]\n",
      " [ 8  9]\n",
      " [12 13]]\n",
      "[[ 2  3]\n",
      " [ 6  7]\n",
      " [10 11]\n",
      " [14 15]]\n"
     ]
    }
   ],
   "source": [
    "left, right = np.hsplit(grid, [2])\n",
    "print(left)\n",
    "print(right)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy استفاده از عملگرها در   \n",
    "Operator                       ufunc Description\n",
    "+ np.add                        (e.g., 1 + 1 = 2)\n",
    "- np.subtract                   (e.g., 3 - 2 = 1)\n",
    "- np.negative                      (e.g., -2)\n",
    "* np.multiply                   (e.g., 2 * 3 = 6)\n",
    "/ np.divide                     (e.g., 3 / 2 = 1.5)\n",
    "// np.floor_divide              (e.g., 3 // 2 = 1)\n",
    "** np.power                     (e.g., 2 ** 3 = 8)\n",
    "% np.mod                        (e.g., 9 % 4 = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [0 1 2 3]\n",
      "x + 5 = [5 6 7 8]\n",
      "x - 5 = [-5 -4 -3 -2]\n",
      "x * 2 = [0 2 4 6]\n",
      "x / 2 = [0.  0.5 1.  1.5]\n",
      "x // 2 = [0 0 1 1]\n",
      "-x =  [ 0 -1 -2 -3]\n",
      "x ** 2 =  [0 1 4 9]\n",
      "x % 2 =  [0 1 0 1]\n",
      "[-1.   -2.25 -4.   -6.25]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(4)\n",
    "print(\"x =\", x)\n",
    "print(\"x + 5 =\", x + 5)\n",
    "print(\"x - 5 =\", x - 5)\n",
    "print(\"x * 2 =\", x * 2)\n",
    "print(\"x / 2 =\", x / 2)\n",
    "print(\"x // 2 =\", x // 2)\n",
    "print(\"-x = \", -x)\n",
    "print(\"x ** 2 = \", x ** 2)\n",
    "print(\"x % 2 = \", x % 2)\n",
    "print(-(0.5*x + 1) ** 2)\n",
    "np.add(x, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [1, 2, 3]\n",
      "e^x = [ 2.71828183  7.3890561  20.08553692]\n",
      "2^x = [2. 4. 8.]\n",
      "3^x = [ 3  9 27]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3]\n",
    "print(\"x =\", x)\n",
    "print(\"e^x =\", np.exp(x))\n",
    "print(\"2^x =\", np.exp2(x))\n",
    "print(\"3^x =\", np.power(3, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x = [1, 2, 4, 10]\n",
      "ln(x) = [0.         0.69314718 1.38629436 2.30258509]\n",
      "log2(x) = [0.         1.         2.         3.32192809]\n",
      "log10(x) = [0.         0.30103    0.60205999 1.        ]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 4, 10]\n",
    "print(\"x =\", x)\n",
    "print(\"ln(x) =\", np.log(x))\n",
    "print(\"log2(x) =\", np.log2(x))\n",
    "print(\"log10(x) =\", np.log10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [0, 0.001, 0.01, 0.1]\n",
    "print(\"exp(x) - 1 =\", np.expm1(x))\n",
    "print(\"log(1 + x) =\", np.log1p(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.66847513e-01, 7.79051020e-01, 8.64933296e-01, 4.11396724e-01,\n",
       "       1.39972585e-01, 3.32223853e-02, 9.82574960e-01, 3.73290751e-01,\n",
       "       4.20075370e-01, 5.05881151e-02, 3.65496106e-01, 1.66279737e-02,\n",
       "       2.30742335e-01, 7.64911699e-01, 9.44123519e-01, 7.49999249e-01,\n",
       "       3.39403819e-01, 4.89548937e-01, 3.38985117e-01, 1.79490261e-01,\n",
       "       1.70986599e-01, 4.63450977e-01, 8.74572958e-01, 9.44119751e-01,\n",
       "       6.08252866e-01, 5.96655406e-01, 7.83644245e-01, 5.00026298e-01,\n",
       "       5.03700568e-02, 6.99098075e-01, 9.92396399e-01, 2.67262538e-01,\n",
       "       6.79090616e-01, 8.64281443e-01, 7.50844246e-01, 9.64489768e-01,\n",
       "       5.54242435e-01, 2.12390499e-01, 2.22443224e-01, 2.18749374e-01,\n",
       "       5.69573535e-01, 4.52109035e-01, 9.70236683e-01, 6.80544691e-01,\n",
       "       8.52955659e-02, 5.64183327e-02, 4.87837704e-01, 8.81004562e-01,\n",
       "       9.76404387e-01, 6.17657916e-01, 5.42498775e-01, 8.54613580e-01,\n",
       "       7.43834545e-01, 4.78596326e-01, 6.77081574e-01, 6.07045061e-01,\n",
       "       7.14696936e-01, 4.69497183e-01, 4.56014623e-01, 9.06418087e-01,\n",
       "       1.37220420e-01, 2.29219323e-01, 8.81585399e-01, 9.04424976e-01,\n",
       "       6.45784599e-01, 3.24682972e-01, 5.19711194e-01, 5.53568650e-05,\n",
       "       3.11860221e-01, 4.25451538e-01, 8.85337660e-01, 6.79879456e-01,\n",
       "       4.56129772e-01, 4.83408617e-01, 7.88739428e-01, 2.29441834e-01,\n",
       "       8.80297603e-01, 3.13692393e-01, 9.57450856e-01, 4.71751571e-01,\n",
       "       7.11583817e-01, 1.53694305e-01, 7.30442177e-01, 6.46264437e-01,\n",
       "       2.14880737e-01, 1.86458219e-01, 8.07580269e-01, 7.47079470e-01,\n",
       "       6.74847346e-01, 2.76893751e-01, 1.74908874e-01, 7.04474258e-01,\n",
       "       4.63150200e-01, 8.40428533e-01, 2.04865762e-01, 1.64958868e-01,\n",
       "       1.24833057e-01, 7.22080662e-01, 3.04529649e-02, 7.46994240e-01])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num= np.random.random(100)\n",
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5.535686500857118e-05, 0.9923963988886326)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(num), np.max(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# نمونه Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.45516882 0.87886974 0.49226824 0.71556116]\n",
      " [0.48615956 0.70854815 0.49814023 0.84455008]\n",
      " [0.19434222 0.77332601 0.97425866 0.86230978]]\n"
     ]
    }
   ],
   "source": [
    "M = np.random.random((3, 4))\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.88350264500669"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19434222, 0.70854815, 0.49226824, 0.71556116])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74925442, 0.67726331, 0.8330381 ])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "def selection_sort(x):\n",
    "    for i in range(len(x)):      \n",
    "        swap = i + np.argmin(x[i:])\n",
    "        (x[i], x[swap]) = (x[swap], x[i])\n",
    "    return x\n",
    "\n",
    "\n",
    "x = np.array([2, 1, 4, 3, 5])\n",
    "selection_sort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([2, 1, 4, 3, 5])\n",
    "np.sort(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "print(np.sort(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 3, 6, 6, 7, 9],\n",
       "       [1, 9, 6, 0, 3, 8],\n",
       "       [4, 1, 4, 5, 0, 3],\n",
       "       [1, 4, 4, 4, 0, 0]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.randint(0, 10, (4, 6))\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 4, 0, 0, 0],\n",
       "       [1, 3, 4, 4, 0, 3],\n",
       "       [4, 4, 6, 5, 3, 8],\n",
       "       [7, 9, 6, 6, 7, 9]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort each column of X\n",
    "np.sort(X, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 6, 6, 7, 7, 9],\n",
       "       [0, 1, 3, 6, 8, 9],\n",
       "       [0, 1, 3, 4, 4, 5],\n",
       "       [0, 0, 1, 4, 4, 4]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sort each row of X\n",
    "np.sort(X, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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