Diss/02_code/00_RawData_Python/Testing Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Jupyter\n",
    "*italicized XXX*\n",
    "\n",
    "## Headline Two\n",
    "\n",
    "This is normal paragraph with **blod** letters\n",
    "\n",
    "1. Hallo\n",
    "2. Second\n",
    "3. Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Hello World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
        "autocall": "AutoMagics",
        "automagic": "AutoMagics",
        "autosave": "KernelMagics",
        "bookmark": "OSMagics",
        "cd": "OSMagics",
        "clear": "KernelMagics",
        "cls": "KernelMagics",
        "colors": "BasicMagics",
        "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": "BasicMagics",
        "popd": "OSMagics",
        "pprint": "BasicMagics",
        "precision": "BasicMagics",
        "profile": "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  %autocall  %automagic  %autosave  %bookmark  %cd  %clear  %cls  %colors  %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  %popd  %pprint  %precision  %profile  %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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%lsmagic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Simple demo of a scatter plot.\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "N=50\n",
    "x=np.random.rand(N)\n",
    "y=np.random.rand(N)\n",
    "colors=np.random.rand(N)\n",
    "area=np.pi*(18*np.random.rand(N))**2 #0 to 15 point radiuses\n",
    "plt.scatter(x,y,s=area,c=colors,alpha=0.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/YJC6ldI3hWk\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%HTML\n",
    "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/YJC6ldI3hWk\" frameborder=\"0\" allowfullscreen></iframe>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.9 µs ± 9.78 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "square_evens = [n*n for n in range(1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>0.809287</td>\n",
       "      <td>0.699242</td>\n",
       "      <td>0.561213</td>\n",
       "      <td>0.709627</td>\n",
       "      <td>0.986164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.086052</td>\n",
       "      <td>0.886943</td>\n",
       "      <td>0.533827</td>\n",
       "      <td>0.012499</td>\n",
       "      <td>0.139520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.371663</td>\n",
       "      <td>0.211376</td>\n",
       "      <td>0.937434</td>\n",
       "      <td>0.011237</td>\n",
       "      <td>0.106434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.081288</td>\n",
       "      <td>0.801140</td>\n",
       "      <td>0.649774</td>\n",
       "      <td>0.954318</td>\n",
       "      <td>0.219239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.842870</td>\n",
       "      <td>0.087737</td>\n",
       "      <td>0.815415</td>\n",
       "      <td>0.708544</td>\n",
       "      <td>0.654585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.809287  0.699242  0.561213  0.709627  0.986164\n",
       "1  0.086052  0.886943  0.533827  0.012499  0.139520\n",
       "2  0.371663  0.211376  0.937434  0.011237  0.106434\n",
       "3  0.081288  0.801140  0.649774  0.954318  0.219239\n",
       "4  0.842870  0.087737  0.815415  0.708544  0.654585"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(10,5))\n",
    "df.head()"
   ]
  }
 ],
 "metadata": {
  "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}