magic-keywords.ipynb

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    "# Magic Keywords\n",
    "\n",
    "Magic keywords are special commands you can run in cells that let you control the notebook itself or perform system calls such as changing directories. \n",
    "Magic commands are preceded with one or two percent signs `(% or %%)` for line magics and cell magics, respectively. Line magics apply only to the line the magic command is written on, while cell magics apply to the whole cell.\n",
    "\n",
    "## Timing Code"
   ]
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   "source": [
    "from math import sqrt\n",
    "\n",
    "def fibo_recursive(n):\n",
    "    if n == 0:\n",
    "        return 0\n",
    "    elif n == 1:\n",
    "        return 1\n",
    "    return fibo_recursive(n-1) + fibo_recursive(n - 2)\n",
    "\n",
    "def fibo(n):\n",
    "    return ((1+sqrt(5))**n-(1 - sqrt(5))**n)/(2**n*sqrt(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "%timeit fibo_recursive(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "%timeit fibo(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
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   "outputs": [],
   "source": [
    "%%timeit\n",
    "rolls= [random.randint(1,6) for _ in range(100)]\n",
    "prize = sum(roll if roll%2 == 0 else -1 for roll in rolls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Embedding Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [],
   "source": [
    "x = np.linspace(0,1,300)\n",
    "for w in range(2,6,2):\n",
    "    plt.plot(x, np.sin(np.pi*x)*np.sin(2*w*np.pi*x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Debugging in the Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pdb on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = 'hello'\n",
    "sum(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pdb off"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List the Environment Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to the set an env variable\n",
    "%set_env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Call Pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: run shell commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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