hello_world.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is a Hello world project in Jupyter Notebook\n",
    "Hi there 🙂 this is my getting started project in Jupyter notebooks and we will look into\n",
    "- list item \n",
    "- list item 2\n",
    "- list item 3\n",
    "\n",
    "also links like [Google](https://www.google.com)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "print('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is OS\n",
      " Volume Serial Number is 5E86-2AE3\n",
      "\n",
      " Directory of C:\\Users\\anish\\coursera\\ml-university-washington\\ml-foundations-case-study-approach\n",
      "\n",
      "07/20/2020  08:30 PM    <DIR>          .\n",
      "07/20/2020  08:30 PM    <DIR>          ..\n",
      "07/20/2020  08:12 PM                52 .gitignore\n",
      "07/20/2020  06:12 PM                 0 .gitmodules\n",
      "07/20/2020  05:25 PM    <DIR>          .idea\n",
      "07/20/2020  08:07 PM    <DIR>          .ipynb_checkpoints\n",
      "07/20/2020  08:30 PM             3,650 hello_world.ipynb\n",
      "07/20/2020  08:12 PM                 0 jupyter-hello-world.ipynb\n",
      "07/20/2020  05:22 PM             1,071 LICENSE\n",
      "07/20/2020  05:37 PM             1,026 README.md\n",
      "               6 File(s)          5,799 bytes\n",
      "               4 Dir(s)  47,335,411,712 bytes free\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "cell": {
        "!": "OSMagics",
        "HTML": "Other",
        "SVG": "Other",
        "bash": "Other",
        "capture": "ExecutionMagics",
        "cmd": "Other",
        "debug": "ExecutionMagics",
        "file": "Other",
        "html": "DisplayMagics",
        "javascript": "DisplayMagics",
        "js": "DisplayMagics",
        "latex": "DisplayMagics",
        "markdown": "DisplayMagics",
        "perl": "Other",
        "prun": "ExecutionMagics",
        "pypy": "Other",
        "python": "Other",
        "python2": "Other",
        "python3": "Other",
        "ruby": "Other",
        "script": "ScriptMagics",
        "sh": "Other",
        "svg": "DisplayMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "writefile": "OSMagics"
       },
       "line": {
        "alias": "OSMagics",
        "alias_magic": "BasicMagics",
        "autoawait": "AsyncMagics",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "cls": "KernelMagics",
        "colors": "BasicMagics",
        "conda": "PackagingMagics",
        "config": "ConfigMagics",
        "connect_info": "KernelMagics",
        "copy": "Other",
        "ddir": "Other",
        "debug": "ExecutionMagics",
        "dhist": "OSMagics",
        "dirs": "OSMagics",
        "doctest_mode": "BasicMagics",
        "echo": "Other",
        "ed": "Other",
        "edit": "KernelMagics",
        "env": "OSMagics",
        "gui": "BasicMagics",
        "hist": "Other",
        "history": "HistoryMagics",
        "killbgscripts": "ScriptMagics",
        "ldir": "Other",
        "less": "KernelMagics",
        "load": "CodeMagics",
        "load_ext": "ExtensionMagics",
        "loadpy": "CodeMagics",
        "logoff": "LoggingMagics",
        "logon": "LoggingMagics",
        "logstart": "LoggingMagics",
        "logstate": "LoggingMagics",
        "logstop": "LoggingMagics",
        "ls": "Other",
        "lsmagic": "BasicMagics",
        "macro": "ExecutionMagics",
        "magic": "BasicMagics",
        "matplotlib": "PylabMagics",
        "mkdir": "Other",
        "more": "KernelMagics",
        "notebook": "BasicMagics",
        "page": "BasicMagics",
        "pastebin": "CodeMagics",
        "pdb": "ExecutionMagics",
        "pdef": "NamespaceMagics",
        "pdoc": "NamespaceMagics",
        "pfile": "NamespaceMagics",
        "pinfo": "NamespaceMagics",
        "pinfo2": "NamespaceMagics",
        "pip": "PackagingMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "prun": "ExecutionMagics",
        "psearch": "NamespaceMagics",
        "psource": "NamespaceMagics",
        "pushd": "OSMagics",
        "pwd": "OSMagics",
        "pycat": "OSMagics",
        "pylab": "PylabMagics",
        "qtconsole": "KernelMagics",
        "quickref": "BasicMagics",
        "recall": "HistoryMagics",
        "rehashx": "OSMagics",
        "reload_ext": "ExtensionMagics",
        "ren": "Other",
        "rep": "Other",
        "rerun": "HistoryMagics",
        "reset": "NamespaceMagics",
        "reset_selective": "NamespaceMagics",
        "rmdir": "Other",
        "run": "ExecutionMagics",
        "save": "CodeMagics",
        "sc": "OSMagics",
        "set_env": "OSMagics",
        "store": "StoreMagics",
        "sx": "OSMagics",
        "system": "OSMagics",
        "tb": "ExecutionMagics",
        "time": "ExecutionMagics",
        "timeit": "ExecutionMagics",
        "unalias": "OSMagics",
        "unload_ext": "ExtensionMagics",
        "who": "NamespaceMagics",
        "who_ls": "NamespaceMagics",
        "whos": "NamespaceMagics",
        "xdel": "NamespaceMagics",
        "xmode": "BasicMagics"
       }
      },
      "text/plain": [
       "Available line magics:\n",
       "%alias  %alias_magic  %autoawait  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %conda  %config  %connect_info  %copy  %ddir  %debug  %dhist  %dirs  %doctest_mode  %echo  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %macro  %magic  %matplotlib  %mkdir  %more  %notebook  %page  %pastebin  %pdb  %pdef  %pdoc  %pfile  %pinfo  %pinfo2  %pip  %popd  %pprint  %precision  %prun  %psearch  %psource  %pushd  %pwd  %pycat  %pylab  %qtconsole  %quickref  %recall  %rehashx  %reload_ext  %ren  %rep  %rerun  %reset  %reset_selective  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n",
       "\n",
       "Available cell magics:\n",
       "%%!  %%HTML  %%SVG  %%bash  %%capture  %%cmd  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n",
       "\n",
       "Automagic is ON, % prefix IS NOT needed for line magics."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\anish\\\\coursera\\\\ml-university-washington\\\\ml-foundations-case-study-approach'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Data for plotting\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2 * np.pi * t)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(t, s)\n",
    "\n",
    "ax.set(xlabel='time (s)', ylabel='voltage (mV)',\n",
    "       title='About as simple as it gets, folks')\n",
    "ax.grid()\n",
    "\n",
    "fig.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<code>this is bold</code>\n",
       "<pre>\n",
       "class Hello {\n",
       "    private static void main(String[] args) {\n",
       "        Sysytem.out.println(\"Hello world\");\n",
       "    }\n",
       "}\n",
       "</pre>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<code>this is bold</code>\n",
    "<pre>\n",
    "class Hello {\n",
    "    private static void main(String[] args) {\n",
    "        Sysytem.out.println(\"Hello world\");\n",
    "    }\n",
    "}\n",
    "\n",
    "</pre>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "'use strict';\n",
       "console.log('hello');\n",
       "let i = 10;\n",
       "console.log(i);\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "'use strict';\n",
    "console.log('hello');\n",
    "let i = 10;\n",
    "console.log(i);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\sin{\\theta} - \\cos{\\theta}$\n"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%latex\n",
    "$\\sin{\\theta} - \\cos{\\theta}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.38 µs ± 30.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "squares = [i * i in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.425664</td>\n",
       "      <td>0.142662</td>\n",
       "      <td>0.556905</td>\n",
       "      <td>-0.518221</td>\n",
       "      <td>-0.588153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.410602</td>\n",
       "      <td>0.558197</td>\n",
       "      <td>0.673382</td>\n",
       "      <td>0.087137</td>\n",
       "      <td>0.968940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.386032</td>\n",
       "      <td>1.670070</td>\n",
       "      <td>0.598469</td>\n",
       "      <td>0.019596</td>\n",
       "      <td>0.512543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.476449</td>\n",
       "      <td>-1.036184</td>\n",
       "      <td>1.754525</td>\n",
       "      <td>-0.125509</td>\n",
       "      <td>0.408721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.498019</td>\n",
       "      <td>-0.400901</td>\n",
       "      <td>-0.279030</td>\n",
       "      <td>-0.755939</td>\n",
       "      <td>1.134731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  1.425664  0.142662  0.556905 -0.518221 -0.588153\n",
       "1  1.410602  0.558197  0.673382  0.087137  0.968940\n",
       "2 -1.386032  1.670070  0.598469  0.019596  0.512543\n",
       "3 -1.476449 -1.036184  1.754525 -0.125509  0.408721\n",
       "4 -0.498019 -0.400901 -0.279030 -0.755939  1.134731"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(10, 5))\n",
    "df.head()"
   ]
  }
 ],
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