Simplilearn Example 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4a7e51c1",
   "metadata": {},
   "source": [
    "# Below is a graph\n",
    "\n",
    "* **This is bold** *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "3b8f9d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import random as rnd\n",
    "import math as mt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "df876e22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coordinate Data is as follows\n",
      "\n",
      "X axix = [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0, 24.5]\n",
      "\n",
      "Y axix = [1.0, 0.5, 1.3, 1.0, 0.2, 0.2, 0.1, 0.5, 0.4, 0.4, 0.1, 0.3, 0.3, 0.3, 0.3, 0.1, 0.1, 0.1, 0.1, 0.1, 0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.1, 0.1, 0.0, 0.1, 0.0, 0.1, 0.0, 0.1, 0.0, 0.1, 0.1, 0.0, 0.0, 0.1, 0.1, 0.0, 0.1, 0.0, 0.1, 0.0, 0.0, 0.1]\n",
      "\n",
      "Generated graph is below:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [(xx/rnd.randrange(2,3)) for xx in range(1,50,1) ]\n",
    "y = [round(rnd.randrange(1,5,3) / yy, 1) for yy in range(1,50,1)]\n",
    "print(\"Coordinate Data is as follows\\n\\nX axix = {}\\n\\nY axix = {}\\n\\nGenerated graph is below:\".format(x,y))\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "234d8292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Windows\n",
      "Intel64 Family 6 Model 94 Stepping 3, GenuineIntel\n",
      "AMD64\n",
      "('64bit', 'WindowsPE')\n",
      "<module 'sys' (built-in)>\n",
      "uname_result(system='Windows', node='DESK-SMK', release='10', version='10.0.19044', machine='AMD64')\n"
     ]
    }
   ],
   "source": [
    "import platform\n",
    "\n",
    "x = platform.system()\n",
    "print(x, platform.processor(), platform.machine(), platform.architecture(), platform.sys, platform.uname(), sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "36b64ad6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Jun  1 00:00:00 2018\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "x = datetime.datetime(2018, 6, 1)\n",
    "\n",
    "print(x.strftime(\"%c\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "2fdb647f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6457513110645907"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math as m\n",
    "m.sqrt(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "3a08c269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"name\": \"John\", \"age\": 30, \"married\": true, \"divorced\": false, \"children\": [\"Ann\", \"Billy\"], \"pets\": null, \"cars\": [{\"model\": \"BMW 230\", \"mpg\": 27.5}, {\"model\": \"Ford Edge\", \"mpg\": 24.1}]}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "x = {\n",
    "  \"name\": \"John\",\n",
    "  \"age\": 30,\n",
    "  \"married\": True,\n",
    "  \"divorced\": False,\n",
    "  \"children\": (\"Ann\",\"Billy\"),\n",
    "  \"pets\": None,\n",
    "  \"cars\": [\n",
    "    {\"model\": \"BMW 230\", \"mpg\": 27.5},\n",
    "    {\"model\": \"Ford Edge\", \"mpg\": 24.1}\n",
    "  ]\n",
    "}\n",
    "\n",
    "print(json.dumps(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "23d196de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\\n    \"name\": \"John\",\\n    \"age\": 30,\\n    \"married\": true,\\n    \"divorced\": false,\\n    \"children\": [\\n        \"Ann\",\\n        \"Billy\"\\n    ],\\n    \"pets\": null,\\n    \"cars\": [\\n        {\\n            \"model\": \"BMW 230\",\\n            \"mpg\": 27.5\\n        },\\n        {\\n            \"model\": \"Ford Edge\",\\n            \"mpg\": 24.1\\n        }\\n    ]\\n}'"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(x, indent=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "ee7286f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\\n    \"name\" = \"John\". \\n    \"age\" = 30. \\n    \"married\" = true. \\n    \"divorced\" = false. \\n    \"children\" = [\\n        \"Ann\". \\n        \"Billy\"\\n    ]. \\n    \"pets\" = null. \\n    \"cars\" = [\\n        {\\n            \"model\" = \"BMW 230\". \\n            \"mpg\" = 27.5\\n        }. \\n        {\\n            \"model\" = \"Ford Edge\". \\n            \"mpg\" = 24.1\\n        }\\n    ]\\n}'"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(x, indent=4, separators=(\". \", \" = \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "d7a4e8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\\n    \"age\": 30,\\n    \"cars\": [\\n        {\\n            \"model\": \"BMW 230\",\\n            \"mpg\": 27.5\\n        },\\n        {\\n            \"model\": \"Ford Edge\",\\n            \"mpg\": 24.1\\n        }\\n    ],\\n    \"children\": [\\n        \"Ann\",\\n        \"Billy\"\\n    ],\\n    \"divorced\": false,\\n    \"married\": true,\\n    \"name\": \"John\",\\n    \"pets\": null\\n}'"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.dumps(x, indent=4, sort_keys=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "256a07cd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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