A quick tour of Jupyter Notebook.ipynb

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   "source": [
    "# A quick tour of Jupyter Notebook"
   ]
  },
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   "source": [
    "This tour will work a little better in interactive mode, so it'll be better if you get IPython notebook installed and running. You can start it from a terminal by running\n",
    "\n",
    "`jupyter notebook`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "### Running a Cell\n",
    "\n",
    "First, we need to explain how to run cells. Try to run the cell below!"
   ]
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   "source": [
    "import pandas as pd\n",
    "\n",
    "print(\"Hi! This is a cell. Press the ▶ button above to run it\")"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "You can also run a cell with Ctrl+Enter or Shift+Enter. Experiment a bit with that."
   ]
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   "source": [
    "### Help\n",
    "\n",
    "One of the most useful things about Jupyter notebook is its tab completion. \n",
    "\n",
    "Try this: click just after read_csv( in the cell below and press Shift+Tab 4 times, slowly"
   ]
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   "source": [
    "pd.read_csv("
   ]
  },
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     "slide_type": "subslide"
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   "source": [
    "After the first time, you should see this:\n",
    "<div align=\"center\">\n",
    "<img src=\"files/images/tab-once.png\" width=\"75%\">\n",
    "</div>\n",
    "\n",
    "After the second time:\n",
    "<div align=\"center\">\n",
    "<img src=\"files/images/tab-twice.png\" width=\"75%\">\n",
    "</div>\n",
    "\n",
    "After the fourth time, a big help box should pop up at the bottom of the screen, with the full documentation for the `read_csv` function:\n",
    "<div align=\"center\">\n",
    "<img src=\"files/images/tab-4-times.png\" width=\"90%\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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   },
   "source": [
    "I find this amazingly useful. I think of this as \"the more confused I am, the more times I should press Shift+Tab\". Nothing bad will happen if you tab complete 12 times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Tab Completion\n",
    "\n",
    "Okay, let's try tab completion for function names!"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "pd.r"
   ]
  },
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   "source": [
    "You should see this:\n",
    "\n",
    "<div align=\"center\">\n",
    "<img src=\"files/images/function-completion.png\" width=\"30%\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Writing code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "Writing code in the notebook is pretty normal."
   ]
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   "source": [
    "def print_10_nums():\n",
    "    for i in range(10):\n",
    "        print(i, end=' ')"
   ]
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   "source": [
    "print_10_nums()"
   ]
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   "source": [
    "# Saving"
   ]
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   "source": [
    "As of the latest stable version, the notebook autosaves. You should use the latest stable version. Really."
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Magic functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "Jupyter has all kinds of magic functions. Here's an example of comparing `sum()` with a list comprehension to a generator comprehension using the `%time` magic."
   ]
  },
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   "source": [
    "%time sum([x for x in range(100000)])"
   ]
  },
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   "source": [
    "%time sum(x for x in range(100000))"
   ]
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   "source": [
    "# Magic Reference\n",
    "\n",
    "The magics I use most are `%time` and `%prun` for profiling. You can run `%magic` to get a list of all of them, and `%quickref` for a reference sheet."
   ]
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   "source": [
    "%quickref"
   ]
  },
  {
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   "source": [
    "### Other Languages\n",
    "\n",
    "You can also do nutty things like run Perl code in the notebook with cell magics. This is especially cool for things like Cython code, where you can try out Cython really fast with the `%%cython` magic (you'll need to install it)."
   ]
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   "source": [
    "%%perl\n",
    "\n",
    "$_ = \"whoa, python!\";\n",
    "s/python/perl/;\n",
    "print"
   ]
  },
  {
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   },
   "source": [
    "### Auto Reload Modules\n",
    "\n",
    "When prototyping modules we get in the this sort of workflow:\n",
    "\n",
    "- make the module\n",
    "- import it in a notebook\n",
    "- test it\n",
    "- fix it\n",
    "- run it again in a notebook\n",
    "\n",
    "With special intervention, the module will not reload."
   ]
  },
  {
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   "source": [
    "### Example\n",
    "\n",
    "There is a file called `foo.py` in this directory and we want to work on it as a module."
   ]
  },
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   "metadata": {
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   "outputs": [],
   "source": [
    "!cat foo.py"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": false,
    "slideshow": {
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   "outputs": [],
   "source": [
    "import foo\n",
    "\n",
    "foo.some_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "source": [
    "### Edit Module\n",
    "\n",
    "Open foo.py in an editor and change some_function to return 43"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
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   "outputs": [],
   "source": [
    "foo.some_function()"
   ]
  },
  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   },
   "source": [
    "### Didn't Work!!!\n",
    "\n",
    "Jupyter didn't see that we changed the module.  The hard way to handle this is to restart the kernel.  The easier way is to use the `%autoreload` magic function.\n",
    "\n",
    "For this to work, we will have to restart the kernel."
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [],
   "source": [
    "import foo\n",
    "\n",
    "foo.some_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Edit Module\n",
    "\n",
    "Open foo.py in an editor and change some_function to return 43."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "foo.some_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot in Notebook\n",
    "\n",
    "By default if you create a matplot lib plot in the notebook, the plot will not render.  You have to use `%matplotlib inline` in order to have the plot render in the output cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pylab as plt\n",
    "%matplotlib inline\n",
    "plt.plot(range(10),range(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
    }
   },
   "source": [
    "That's it for now!"
   ]
  }
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