bdmorin/ELLL20180530.ipynb

## ELLL20180530.ipynb
{
  "cells": [
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Lunch and Learn, May 30, Jupyter Basics"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Things I'd like to achieve\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* Show jupyter examples and offer insight on thow this might help you at EL\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* What the [REPL](https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop) is, and why it's interesting\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* [ipython](https://ipython.org/) vs [Jupyter](https://jupyter.org/) vs [Jupyter Labs](https://jupyterlab.readthedocs.io/en/stable/)\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* Jupyter [Kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* What is the [pandas](https://pandas.pydata.org/) (Python Data Analysis Library) library, and why it's important"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* So many plotting options \n  * [matplotlib](http://matplotlib.org/)\n  * [bokeh](http://bokeh.pydata.org/)\n  * [seaborn](http://stanford.edu/~mwaskom/software/seaborn/)\n  * [pygal](http://pygal.org/)\n  * [plotly](https://plot.ly/)\n  * [ggplot](http://ggplot.yhathq.com/)"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Things we won't cover unless someone really really wants to"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* Installation of Jupyter. It's either really easy, or you can make it complicated. \n  * Simple: [Anaconda Cloud](https://anaconda.org/) It's a large installation, but handles nearly everything.\n    * _brew cask install anaconda_\n  * Also Simple: Use cloud based JupyterHub\n    * https://jupyter.org/try\n    * https://mybinder.org/\n  * Moderate: Use your system's *_pip_* installer. (sudo hell)\n    * just use Anaconda _conda install jupyter_\n  * Temporary: *docker run -p 8888:8888 jupyter/scipy-notebook*\n  * Complicated: DIY dockerfile or kubernetes."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "  * Python2.x vs Python 3.x - If you don't know python at all, start with 3.6, and be aware 3.7 is gnarly. Python2.7 is standard default interpreter on most systems."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "* Why *x* is better than _y_\n  * BUT.. EXCEL!!\n  * I can do all this in Google Sheets.\n  * gawd... more luggage, just use VanillaJS!!\n  * You should use RStudio, or Tableau, or whateverelse.."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Features"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%lsmagic",
      "execution_count": 1,
      "outputs": [
        {
          "data": {
            "application/json": {
              "cell": {
                "!": "OSMagics",
                "HTML": "Other",
                "SVG": "Other",
                "bash": "Other",
                "capture": "ExecutionMagics",
                "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",
                "cat": "Other",
                "cd": "OSMagics",
                "clear": "KernelMagics",
                "colors": "BasicMagics",
                "config": "ConfigMagics",
                "connect_info": "KernelMagics",
                "cp": "Other",
                "debug": "ExecutionMagics",
                "dhist": "OSMagics",
                "dirs": "OSMagics",
                "doctest_mode": "BasicMagics",
                "ed": "Other",
                "edit": "KernelMagics",
                "env": "OSMagics",
                "gui": "BasicMagics",
                "hist": "Other",
                "history": "HistoryMagics",
                "killbgscripts": "ScriptMagics",
                "ldir": "Other",
                "less": "KernelMagics",
                "lf": "Other",
                "lk": "Other",
                "ll": "Other",
                "load": "CodeMagics",
                "load_ext": "ExtensionMagics",
                "loadpy": "CodeMagics",
                "logoff": "LoggingMagics",
                "logon": "LoggingMagics",
                "logstart": "LoggingMagics",
                "logstate": "LoggingMagics",
                "logstop": "LoggingMagics",
                "ls": "Other",
                "lsmagic": "BasicMagics",
                "lx": "Other",
                "macro": "ExecutionMagics",
                "magic": "BasicMagics",
                "man": "KernelMagics",
                "matplotlib": "PylabMagics",
                "mkdir": "Other",
                "more": "KernelMagics",
                "mv": "Other",
                "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",
                "rep": "Other",
                "rerun": "HistoryMagics",
                "reset": "NamespaceMagics",
                "reset_selective": "NamespaceMagics",
                "rm": "Other",
                "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  %cat  %cd  %clear  %colors  %config  %connect_info  %cp  %debug  %dhist  %dirs  %doctest_mode  %ed  %edit  %env  %gui  %hist  %history  %killbgscripts  %ldir  %less  %lf  %lk  %ll  %load  %load_ext  %loadpy  %logoff  %logon  %logstart  %logstate  %logstop  %ls  %lsmagic  %lx  %macro  %magic  %man  %matplotlib  %mkdir  %more  %mv  %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  %rep  %rerun  %reset  %reset_selective  %rm  %rmdir  %run  %save  %sc  %set_env  %store  %sx  %system  %tb  %time  %timeit  %unalias  %unload_ext  %who  %who_ls  %whos  %xdel  %xmode\n\nAvailable cell magics:\n%%!  %%HTML  %%SVG  %%bash  %%capture  %%debug  %%file  %%html  %%javascript  %%js  %%latex  %%markdown  %%perl  %%prun  %%pypy  %%python  %%python2  %%python3  %%ruby  %%script  %%sh  %%svg  %%sx  %%system  %%time  %%timeit  %%writefile\n\nAutomagic is ON, % prefix IS NOT needed for line magics."
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "!pip install pandas\n",
      "execution_count": 45,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Requirement already satisfied: pandas in /usr/local/lib/python3.6/site-packages (0.23.0)\nRequirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/site-packages (from pandas) (1.14.2)\nRequirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/site-packages (from pandas) (2.6.0)\nRequirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/site-packages (from pandas) (2018.3)\nRequirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/site-packages (from python-dateutil>=2.5.0->pandas) (1.10.0)\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%latex\n\\begin{align*}\n f(x)  &= a x^2+b x +d   &   g(x)  &= d x^3 \\\\\n f'(x) &= 2 a x +b       &   g'(x) &= 3 d x^2\n\\end{align*}\n",
      "execution_count": 46,
      "outputs": [
        {
          "data": {
            "text/latex": "\\begin{align*}\n f(x)  &= a x^2+b x +d   &   g(x)  &= d x^3 \\\\\n f'(x) &= 2 a x +b       &   g'(x) &= 3 d x^2\n\\end{align*}",
            "text/plain": "<IPython.core.display.Latex object>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a = 103\na\ntaylor",
      "execution_count": 52,
      "outputs": [
        {
          "data": {
            "text/plain": "'wise'"
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print(a+130)\ntaylor=\"wise\"",
      "execution_count": 51,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "233\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%ruby\nputs \"Hello from Ruby #{RUBY_VERSION}\"",
      "execution_count": 54,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "Hello from Ruby 2.5.1\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%%bash\ncurl google.com",
      "execution_count": 55,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "<HTML><HEAD><meta http-equiv=\"content-type\" content=\"text/html;charset=utf-8\">\n<TITLE>301 Moved</TITLE></HEAD><BODY>\n<H1>301 Moved</H1>\nThe document has moved\n<A HREF=\"http://www.google.com/\">here</A>.\r\n</BODY></HTML>\r\n"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n                                 Dload  Upload   Total   Spent    Left  Speed\n\r  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\r100   219  100   219    0     0   1972      0 --:--:-- --:--:-- --:--:--  1972\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Embedded Video"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.display import YouTubeVideo\nYouTubeVideo('sjfsUzECqK0')",
      "execution_count": 8,
      "outputs": [
        {
          "data": {
            "image/jpeg": 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\n",
            "text/html": "\n        <iframe\n            width=\"400\"\n            height=\"300\"\n            src=\"https://www.youtube.com/embed/sjfsUzECqK0\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        ",
            "text/plain": "<IPython.lib.display.YouTubeVideo at 0x108067ac8>"
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Audio Waveform generation"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.display import Audio\nmax_time = 3\nf1 = 220\nf2 = 224\nrate = 8000\nL = 3\ntimes = np.linspace(0,L,rate*L)\nsignal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n\nAudio(data=signal, rate=rate)",
      "execution_count": 56,
      "outputs": [
        {
          "data": {
            "text/html": "\n                <audio controls=\"controls\" >\n                    <source 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\" type=\"audio/wav\" />\n                    Your browser does not support the audio element.\n                </audio>\n              ",
            "text/plain": "<IPython.lib.display.Audio object>"
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "## Embedded Streaming\n![podcast](https://ia802608.us.archive.org/15/items/OTRR_X_Minus_One_Singles/OTRR_X_Minus_One_Singles.jpg)\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nAudio(url=\"https://archive.org/download/OTRR_X_Minus_One_Singles/XMinusOne55-04-24001NoContact.mp3\")",
      "execution_count": 57,
      "outputs": [
        {
          "data": {
            "text/html": "\n                <audio controls=\"controls\" >\n                    <source src=\"https://archive.org/download/OTRR_X_Minus_One_Singles/XMinusOne55-04-24001NoContact.mp3\" type=\"audio/mpeg\" />\n                    Your browser does not support the audio element.\n                </audio>\n              ",
            "text/plain": "<IPython.lib.display.Audio object>"
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Inline graphviz"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from graphviz import Digraph\nf = Digraph('finite_state_machine', filename='fsm.gv')\nf.attr(rankdir='LR', size='8,5')\n\nf.attr('node', shape='doublecircle')\nf.node('LR_0')\nf.node('LR_3')\nf.node('LR_4')\nf.node('LR_8')\n\nf.attr('node', shape='circle')\nf.edge('LR_0', 'LR_2', label='SS(B)')\nf.edge('LR_0', 'LR_1', label='SS(S)')\nf.edge('LR_1', 'LR_3', label='S($end)')\nf.edge('LR_2', 'LR_6', label='SS(b)')\n\nf.edge('LR_2', 'LR_4', label='S(A)')\nf.edge('Taylor', 'LR_7', label='S(b)')\nf.edge('LR_5', 'LR_5', label='S(a)')\nf.edge('LR_6', 'LR_6', label='S(b)')\nf.edge('LR_6', 'LR_5', label='S(a)')\nf.edge('LR_7', 'LR_8', label='S(b)')\nf.edge('LR_7', 'LR_5', label='S(a)')\nf.edge('LR_8', 'LR_6', label='S(b)')\nf.edge('LR_8', 'LR_5', label='S(a)')\n\nf",
      "execution_count": 62,
      "outputs": [
        {
          "data": {
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          },
          "execution_count": 62,
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        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Let's do datastuff.\nSemi-livecoding, expect mistakes."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "skip"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fruits.count('apple')",
      "execution_count": 12,
      "outputs": [
        {
          "data": {
            "text/plain": "2"
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fruits.index('kiwi')",
      "execution_count": 13,
      "outputs": [
        {
          "data": {
            "text/plain": "4"
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fruits ",
      "execution_count": 14,
      "outputs": [
        {
          "data": {
            "text/plain": "['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']"
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pprint\npp = pprint.PrettyPrinter(indent=4)\nfruits.insert(0,fruits[:])\npp.pprint(fruits)",
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[   ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana'],\n    'orange',\n    'apple',\n    'pear',\n    'banana',\n    'kiwi',\n    'apple',\n    'banana']\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%timeit np.linalg.eigvals(np.random.rand(100,100))",
      "execution_count": 63,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "5.3 ms ± 109 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from IPython.display import Math\nMath(r'F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx')",
      "execution_count": 64,
      "outputs": [
        {
          "data": {
            "text/latex": "$$F(k) = \\int_{-\\infty}^{\\infty} f(x) e^{2\\pi i k} dx$$",
            "text/plain": "<IPython.core.display.Math object>"
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# http://matplotlib.org/gallery.html\n\n# Import a bunch of libraries.\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Set Matplotlib to display inline.\n%matplotlib inline",
      "execution_count": 18,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = np.linspace(0, 3*np.pi, 500)\nplt.plot(x, np.sin(x**2))\nplt.title('Basic wave plot') ",
      "execution_count": 19,
      "outputs": [
        {
          "data": {
            "text/plain": "Text(0.5,1,'Basic wave plot')"
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "x = np.linspace(0, 5, 10)\ny = x ** 2\n\nfig = plt.figure()\n\naxes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1\naxes.plot(x, y, 'r')\naxes.set_xlabel('x')\naxes.set_ylabel('y')\naxes.set_title('title');",
      "execution_count": 69,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "scrolled": true,
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig.savefig(\"plot1.png\")\nfrom IPython.display import Image\ni = Image(filename='plot1.png')\ni",
      "execution_count": 21,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<IPython.core.display.Image object>"
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Pandas"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Import Libraries\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sys, matplotlib\n%matplotlib inline",
      "execution_count": 70,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# The inital set of baby names and bith rates\nnames = ['Bob','Jessica','Mary','John','Mel']\nbirths = [968, 155, 77, 578, 973]\n",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pp.pprint([names,births])",
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": "[['Bob', 'Jessica', 'Mary', 'John', 'Mel'], [968, 155, 77, 578, 973]]\n"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "zip?",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "AgeDataSet = list(zip(names,births))\nAgeDataSet",
      "execution_count": 26,
      "outputs": [
        {
          "data": {
            "text/plain": "[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]"
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#make dataframe\ndf = pd.DataFrame(data = AgeDataSet, columns=['Name','Birth'])\ndf\n",
      "execution_count": 27,
      "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>Name</th>\n      <th>Birth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Bob</td>\n      <td>968</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Jessica</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mary</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>John</td>\n      <td>578</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Mel</td>\n      <td>973</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      Name  Birth\n0      Bob    968\n1  Jessica    155\n2     Mary     77\n3     John    578\n4      Mel    973"
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# outputs\n#df.to_csv?\n#df.to_json?",
      "execution_count": 71,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.dtypes",
      "execution_count": 29,
      "outputs": [
        {
          "data": {
            "text/plain": "Name     object\nBirth     int64\ndtype: object"
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "Sorted = df.sort_values(['Birth'], ascending=False)\nSorted",
      "execution_count": 30,
      "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>Name</th>\n      <th>Birth</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Mel</td>\n      <td>973</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Bob</td>\n      <td>968</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>John</td>\n      <td>578</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Jessica</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mary</td>\n      <td>77</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "      Name  Birth\n4      Mel    973\n0      Bob    968\n3     John    578\n1  Jessica    155\n2     Mary     77"
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df['Birth'].plot()\n\n\n",
      "execution_count": 31,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10c7687b8>"
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df=pd.read_csv('datasets/zoo.tab', sep='\\t',skiprows=[1,2])\ndf.head() ",
      "execution_count": 32,
      "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>name</th>\n      <th>hair</th>\n      <th>feathers</th>\n      <th>eggs</th>\n      <th>milk</th>\n      <th>airborne</th>\n      <th>aquatic</th>\n      <th>predator</th>\n      <th>toothed</th>\n      <th>backbone</th>\n      <th>breathes</th>\n      <th>venomous</th>\n      <th>fins</th>\n      <th>legs</th>\n      <th>tail</th>\n      <th>domestic</th>\n      <th>catsize</th>\n      <th>type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>aardvark</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>mammal</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>antelope</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>mammal</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>bass</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>fish</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>bear</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>mammal</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>boar</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>mammal</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       name  hair  feathers  eggs  milk  airborne  aquatic  predator  toothed  \\\n0  aardvark     1         0     0     1         0        0         1        1   \n1  antelope     1         0     0     1         0        0         0        1   \n2      bass     0         0     1     0         0        1         1        1   \n3      bear     1         0     0     1         0        0         1        1   \n4      boar     1         0     0     1         0        0         1        1   \n\n   backbone  breathes  venomous  fins  legs  tail  domestic  catsize    type  \n0         1         1         0     0     4     0         0        1  mammal  \n1         1         1         0     0     4     1         0        1  mammal  \n2         1         0         0     1     0     1         0        0    fish  \n3         1         1         0     0     4     0         0        1  mammal  \n4         1         1         0     0     4     1         0        1  mammal  "
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.describe()",
      "execution_count": 33,
      "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>hair</th>\n      <th>feathers</th>\n      <th>eggs</th>\n      <th>milk</th>\n      <th>airborne</th>\n      <th>aquatic</th>\n      <th>predator</th>\n      <th>toothed</th>\n      <th>backbone</th>\n      <th>breathes</th>\n      <th>venomous</th>\n      <th>fins</th>\n      <th>legs</th>\n      <th>tail</th>\n      <th>domestic</th>\n      <th>catsize</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n      <td>101.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.425743</td>\n      <td>0.198020</td>\n      <td>0.584158</td>\n      <td>0.405941</td>\n      <td>0.237624</td>\n      <td>0.356436</td>\n      <td>0.554455</td>\n      <td>0.603960</td>\n      <td>0.821782</td>\n      <td>0.792079</td>\n      <td>0.079208</td>\n      <td>0.168317</td>\n      <td>2.841584</td>\n      <td>0.742574</td>\n      <td>0.128713</td>\n      <td>0.435644</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.496921</td>\n      <td>0.400495</td>\n      <td>0.495325</td>\n      <td>0.493522</td>\n      <td>0.427750</td>\n      <td>0.481335</td>\n      <td>0.499505</td>\n      <td>0.491512</td>\n      <td>0.384605</td>\n      <td>0.407844</td>\n      <td>0.271410</td>\n      <td>0.376013</td>\n      <td>2.033385</td>\n      <td>0.439397</td>\n      <td>0.336552</td>\n      <td>0.498314</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>8.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "             hair    feathers        eggs        milk    airborne     aquatic  \\\ncount  101.000000  101.000000  101.000000  101.000000  101.000000  101.000000   \nmean     0.425743    0.198020    0.584158    0.405941    0.237624    0.356436   \nstd      0.496921    0.400495    0.495325    0.493522    0.427750    0.481335   \nmin      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n25%      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n50%      0.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n75%      1.000000    0.000000    1.000000    1.000000    0.000000    1.000000   \nmax      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n\n         predator     toothed    backbone    breathes    venomous        fins  \\\ncount  101.000000  101.000000  101.000000  101.000000  101.000000  101.000000   \nmean     0.554455    0.603960    0.821782    0.792079    0.079208    0.168317   \nstd      0.499505    0.491512    0.384605    0.407844    0.271410    0.376013   \nmin      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n25%      0.000000    0.000000    1.000000    1.000000    0.000000    0.000000   \n50%      1.000000    1.000000    1.000000    1.000000    0.000000    0.000000   \n75%      1.000000    1.000000    1.000000    1.000000    0.000000    0.000000   \nmax      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n\n             legs        tail    domestic     catsize  \ncount  101.000000  101.000000  101.000000  101.000000  \nmean     2.841584    0.742574    0.128713    0.435644  \nstd      2.033385    0.439397    0.336552    0.498314  \nmin      0.000000    0.000000    0.000000    0.000000  \n25%      2.000000    0.000000    0.000000    0.000000  \n50%      4.000000    1.000000    0.000000    0.000000  \n75%      4.000000    1.000000    0.000000    1.000000  \nmax      8.000000    1.000000    1.000000    1.000000  "
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "table = pd.pivot_table(df, index= ['type','name'], columns=['venomous'],aggfunc={'venomous':sum}, fill_value=0)\ntable",
      "execution_count": 34,
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th></th>\n      <th colspan=\"2\" halign=\"left\">venomous</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>venomous</th>\n      <th>0</th>\n      <th>1</th>\n    </tr>\n    <tr>\n      <th>type</th>\n      <th>name</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">amphibian</th>\n      <th>frog</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>newt</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>toad</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"20\" valign=\"top\">bird</th>\n      <th>chicken</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>crow</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>dove</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>duck</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>flamingo</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>gull</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>hawk</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>kiwi</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>lark</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>ostrich</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>parakeet</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>penguin</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>pheasant</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>rhea</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>skimmer</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>skua</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>sparrow</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>swan</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>vulture</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>wren</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"7\" valign=\"top\">fish</th>\n      <th>bass</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>carp</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>catfish</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>chub</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>dogfish</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>haddock</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>herring</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"25\" valign=\"top\">mammal</th>\n      <th>hamster</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>hare</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>leopard</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>lion</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>lynx</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>mink</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>mole</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>mongoose</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>opossum</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>oryx</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>platypus</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>polecat</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>pony</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>porpoise</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>puma</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>pussycat</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>raccoon</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>reindeer</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>seal</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>sealion</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>squirrel</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>vampire</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>vole</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>wallaby</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>wolf</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th rowspan=\"5\" valign=\"top\">reptile</th>\n      <th>pitviper</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>seasnake</th>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>slowworm</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>tortoise</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>tuatara</th>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 2 columns</p>\n</div>",
            "text/plain": "                   venomous   \nvenomous                  0  1\ntype      name                \namphibian frog            0  1\n          newt            0  0\n          toad            0  0\nbird      chicken         0  0\n          crow            0  0\n          dove            0  0\n          duck            0  0\n          flamingo        0  0\n          gull            0  0\n          hawk            0  0\n          kiwi            0  0\n          lark            0  0\n          ostrich         0  0\n          parakeet        0  0\n          penguin         0  0\n          pheasant        0  0\n          rhea            0  0\n          skimmer         0  0\n          skua            0  0\n          sparrow         0  0\n          swan            0  0\n          vulture         0  0\n          wren            0  0\nfish      bass            0  0\n          carp            0  0\n          catfish         0  0\n          chub            0  0\n          dogfish         0  0\n          haddock         0  0\n          herring         0  0\n...                     ... ..\nmammal    hamster         0  0\n          hare            0  0\n          leopard         0  0\n          lion            0  0\n          lynx            0  0\n          mink            0  0\n          mole            0  0\n          mongoose        0  0\n          opossum         0  0\n          oryx            0  0\n          platypus        0  0\n          polecat         0  0\n          pony            0  0\n          porpoise        0  0\n          puma            0  0\n          pussycat        0  0\n          raccoon         0  0\n          reindeer        0  0\n          seal            0  0\n          sealion         0  0\n          squirrel        0  0\n          vampire         0  0\n          vole            0  0\n          wallaby         0  0\n          wolf            0  0\nreptile   pitviper        0  1\n          seasnake        0  1\n          slowworm        0  0\n          tortoise        0  0\n          tuatara         0  0\n\n[100 rows x 2 columns]"
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "table.plot()\n",
      "execution_count": 35,
      "outputs": [
        {
          "data": {
            "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x10ccadf60>"
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 1 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Easier Data Pivots\nLet's use a GUI pivtotable maker.."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# GUI Pivottables\ndf3 = pd.read_csv(\"datasets/titanic.tab\", sep='\\t', skiprows=[1,2])\n\ndf3.describe()",
      "execution_count": 36,
      "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>status</th>\n      <th>age</th>\n      <th>sex</th>\n      <th>survived</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2201</td>\n      <td>2201</td>\n      <td>2201</td>\n      <td>2201</td>\n    </tr>\n    <tr>\n      <th>unique</th>\n      <td>4</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>top</th>\n      <td>crew</td>\n      <td>adult</td>\n      <td>male</td>\n      <td>no</td>\n    </tr>\n    <tr>\n      <th>freq</th>\n      <td>885</td>\n      <td>2092</td>\n      <td>1731</td>\n      <td>1490</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "       status    age   sex survived\ncount    2201   2201  2201     2201\nunique      4      2     2        2\ntop      crew  adult  male       no\nfreq      885   2092  1731     1490"
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "\nfrom pivottablejs import pivot_ui\n\npivot_ui(df3)",
      "execution_count": 72,
      "outputs": [
        {
          "data": {
            "text/html": "\n        <iframe\n            width=\"100%\"\n            height=\"500\"\n            src=\"pivottablejs.html\"\n            frameborder=\"0\"\n            allowfullscreen\n        ></iframe>\n        ",
            "text/plain": "<IPython.lib.display.IFrame at 0x10d426898>"
          },
          "execution_count": 72,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "## Advanced Inline Plot"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "-"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from __future__ import print_function, division\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\n%matplotlib inline\n# Silly example data\nX = np.arange(-5, 5,3.25)\nY = np.arange(-5, 5, 1.25)\nX, Y = np.meshgrid(X, Y)\nR = np.sqrt(X**2 + Y**2)\nZ = np.sin(R)\n\n# Make the plot\nfig = plt.figure()\nax = fig.gca(projection=\"3d\")\nsurf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=cm.coolwarm,\n                       linewidth=0, antialiased=False)\nax.set_zlim(-1.01, 1.01)\nfig.colorbar(surf, shrink=0.5, aspect=5)\nplt.show()",
      "execution_count": 74,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": "<Figure size 432x288 with 2 Axes>"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# THEMES\n\n* https://github.com/dunovank/jupyter-themes\n* https://github.com/transcranial/jupyter-themer"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%pycat /Users/bdmorin/.jupyter/custom/custom.css\n",
      "execution_count": 76,
      "outputs": []
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Tools to be aware of\n\n* [Orange3](https://orange.biolab.si/)\n* [BeakerX](http://beakerx.com/)\n* [scikit-learn](http://scikit-learn.org/stable/)\n* [tensorflow](https://learningtensorflow.com/index.html)\n\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "## Orange3\n[Orange](https://orange.biolab.si/) is a gui tool that works like Jupyter, offering a different way to manage data inputs, outputs, and filters."
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "### Data mapping Visualization\n\n![Orange3 screenshot](https://orange.biolab.si/static/homepage/screenshots/images/concatenate.png)"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "markdown",
      "source": "### Example \n![Orange3 Twitter example](https://orange.biolab.si/static/homepage/screenshots/images/text-topics.png)"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## Beakerx\n\nBeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, interactive plots, tables, forms, publishing, and more. BeakerX supports:\n\n* Groovy, Scala, Clojure, Kotlin, Java, and SQL, including many magics;\n* Widgets for time-series plotting, tables, forms, and more (there are Python and JavaScript APIs in addition to the JVM languages);\n* One-click publication with interactive plots and tables, and\n* Jupyter Lab.\n"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "### Interactive Plotting\n![Interactive Plotting](http://beakerx.com/static/img/time-series.png)"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "markdown",
      "source": "### Interactive Tables\n![Interactive Tables](http://beakerx.com/static/img/table-with-menu.png)"
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "notes"
        }
      },
      "cell_type": "markdown",
      "source": "Available on [Github](https://gist.github.com/bdmorin/fb72dc55b878957e29490b91075a4a3f)\n\nOther Resources:\n* [Awesome Jupyter](https://github.com/markusschanta/awesome-jupyter)\n* [Jupyter Notebook Tutorial: The Definitive Guide](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook) (2016)\n* [Pandas Fundamentals](https://www.datacamp.com/courses/pandas-foundations)"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "_draft": {
      "nbviewer_url": "https://gist.github.com/fb72dc55b878957e29490b91075a4a3f"
    },
    "celltoolbar": "Slideshow",
    "gist": {
      "id": "fb72dc55b878957e29490b91075a4a3f",
      "data": {
        "description": "elll/ELLL20180530.ipynb",
        "public": true
      }
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "livereveal": {
      "theme": "sky",
      "transition": "zoom"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": false,
      "sideBar": true,
      "skip_h1_title": false,
      "base_numbering": 1,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {
        "height": "calc(100% - 180px)",
        "left": "10px",
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      "kernels_config": {
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}