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Author: 1StefanLive

Assignments/EN/Assignment_6.ipynb

@@ -39,29 +39,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# a)"
+    "# a)\n",
+    "import random\n",
+    "\n",
+    "def eight_sided_dice():\n",
+    "    sides_dice = [1,2,3,4,5,6,7,8]\n",
+    "    roll_dice = random.choices(sides_dice, weights = [1,2,3,4,5,6,7,8], k = 1)\n",
+    "    result = roll_dice\n",
+    "    return result\n",
+    "\n",
+    "eight_sided_dice()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[8], [4], [8], [4], [5], [8], [8], [7], [8], [7], [7], [6], [8], [5], [8], [7], [4], [7], [4], [6], [6], [8], [8], [8], [7], [5], [7], [8], [6], [5], [8], [3], [2], [5], [5], [8], [6], [7], [7], [5], [8], [8], [6], [7], [4], [6], [1], [7], [7], [8], [2], [8], [7], [8], [2], [6], [3], [5], [5], [4], [8], [7], [6], [7], [4], [5], [6], [1], [4], [4], [7], [6], [8], [6], [6], [1], [7], [5], [8], [5], [8], [6], [7], [6], [7], [2], [3], [5], [5], [4], [4], [8], [8], [1], [6], [4], [8], [2], [3], [6]]\n",
+      "[8, 4, 8, 4, 5, 8, 8, 7, 8, 7, 7, 6, 8, 5, 8, 7, 4, 7, 4, 6, 6, 8, 8, 8, 7, 5, 7, 8, 6, 5, 8, 3, 2, 5, 5, 8, 6, 7, 7, 5, 8, 8, 6, 7, 4, 6, 1, 7, 7, 8, 2, 8, 7, 8, 2, 6, 3, 5, 5, 4, 8, 7, 6, 7, 4, 5, 6, 1, 4, 4, 7, 6, 8, 6, 6, 1, 7, 5, 8, 5, 8, 6, 7, 6, 7, 2, 3, 5, 5, 4, 4, 8, 8, 1, 6, 4, 8, 2, 3, 6]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# b)"
+    "# b)\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "tenthousand_rolls = [eight_sided_dice() for roll_dice in range(1, 1001)]\n",
+    "print(tenthousand_rolls[0:100])\n",
+    "\n",
+    "tenthousand_convert = [i for sublist in tenthousand_rolls for i in sublist]\n",
+    "print(tenthousand_convert[0:100])\n",
+    "\n",
+    "plt.hist(tenthousand_convert, bins = 8)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0994415283203125e-05"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# c)"
+    "# c)\n",
+    "import time\n",
+    "\n",
+    "def modify_eight_sided_dice():\n",
+    "    result = []\n",
+    "    values = set()\n",
+    "    time_start = time.time()\n",
+    "    while len(values) < 8:\n",
+    "        value = random.randint(1,8)\n",
+    "        values.add(value)\n",
+    "    time_end = time.time()\n",
+    "    absolute_elapsed = time_end - time_start\n",
+    "    return absolute_elapsed\n",
+    "    \n",
+    "modify_eight_sided_dice()\n",
+    "    "
    ]
   },
   {
@@ -124,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.6"
+   "version": "3.9.13"
   }
  },
  "nbformat": 4,