Python Course Lecture 3.ipynb

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   "metadata": {
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   "outputs": [],
   "source": [
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Introduction to Scientific Programming in Python\n",
    "\n",
    "## Lecture 3: Python Modules and Packages, NumPy Arrays, and Plotting with Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Modules and Packages\n",
    "\n",
    "If someone saves a text file with Python code in it and gives it the \".py\" file extension, you can run that file in your own session using the **import** statement. If that file contained functions or new classes, then we get to use them!\n",
    "  - A **Module** is one Python file.\n",
    "  - A **Package** is a collection of Python files.\n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## The \"Import\" Statement, Dot Notation, and Namespaces\n",
    "To keep from overwriting other functions that may have the same name, Python lets us use the Dot to tell it which packages' function we want to use.  What comes before the dot (or whether a dot is even needed) is referred to as an object's **Namespace**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<built-in function sqrt>\n",
      "<ufunc 'sqrt'>\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy\n",
    "print(math.sqrt)\n",
    "print(numpy.sqrt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## \"From\" lets you bring modules and functions into other namespaces. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ufunc 'sqrt'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import sqrt\n",
    "from numpy import sqrt\n",
    "sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## The \"as\" operater lets you assign a different variable name in the import statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'math' (built-in)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math as m\n",
    "import numpy as np\n",
    "my_math_packate = m\n",
    "my_math_packate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Even Packages are objects; They can be renamed the same as anything else once imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<module 'math' (built-in)>\n"
     ]
    }
   ],
   "source": [
    "the_math_package = math\n",
    "print(the_math_package)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Import Submodules with the Dot notation and \"as\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy.linalg.norm() as linalg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Script vs Package\n",
    "If you have code that you want to run **only** if being called as a script (not being imported as a package): use the following code snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code runs only in scripts and interactive sessions.\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    print('This code runs only in scripts and interactive sessions.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# The NumPy Package\n",
    "\n",
    "NumPy is a package for creating, transforming, and calculation with **Arrays** and **Matrices**.  It is the most essential package of the entire Python scientific library. \n",
    "\n",
    "The more familiar you are with NumPy, the more effective you will be during data analysis.  So bear with me as I share with you..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy Array Objects\n",
    "\n",
    "To construct a **numpy.array**, you should give it an **Iterable**.  It will guess what data type you want, but this can be set with the **dtype** **keyword argument**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "a_array = np.array([1, 2, 3, 4])\n",
    "a_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_float_array = np.array([1, 2, 3, 4], dtype=float)\n",
    "a_float_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Array Arithmetic: Now Easy!\n",
    "\n",
    "By default, all operations are performed **Element-Wise**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(range(10), dtype=float)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   1.,   4.,   9.,  16.,  25.,  36.,  49.,  64.,  81.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data * data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Array Arithmetic: Now Fast (on large datasets)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 16.31 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 1.18 µs per loop\n"
     ]
    }
   ],
   "source": [
    "data = np.array(range(3))\n",
    "%timeit [el ** 2 for el in data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 78.70 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 367 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit data ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Arrays Can Be Indexed and Sliced, Just like Tuples and Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(range(10), dtype=float)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Logical Indexing: Using Bool arrays to filter an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 10,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.array(range(4, 20))\n",
    "# mask = data > 3\n",
    "dd = data[:5]\n",
    "dd[1] = 10\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Fancy Indexing: Indexing with a list of indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  9, 13])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[3, 5, 9]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy comes with Lots of useful math functions!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.8125"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"http://docs.scipy.org/doc/numpy/reference/routines.math.html\" width=900 height=300></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iFrame src=\"http://docs.scipy.org/doc/numpy/reference/routines.math.html\" width=900 height=300></iFrame>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Limitations Of NumPy Arrays\n",
    "NumPy Arrays are:\n",
    "  - Fixed-Length (appending them means making a copy)\n",
    "  - Fixed-Data Type (only one data type in an array)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Some Array-Creation Shortcut Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 20, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e+03,   5.62341325e+04,   3.16227766e+06,\n",
       "         1.77827941e+08,   1.00000000e+10])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(3, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(4); np.ones(4)\n",
    "np.zeros_like(data); np.ones_like(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Matlab - Python - R Equivalents\n",
    "\n",
    "For a fairly comprehensive lists of equivalents between languages, see:\n",
    "http://mathesaurus.sourceforge.net/matlab-python-xref.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"http://mathesaurus.sourceforge.net/matlab-python-xref.pdf\" width=900 height=400></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iFrame src=\"http://mathesaurus.sourceforge.net/matlab-python-xref.pdf\" width=900 height=400></iFrame>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Plotting in Matplotlib\n",
    "\n",
    "Matplotlib is the main plotting package in Python.  Its most important module is the **pyplot** module.  As such, you will almost always see it imported as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## To make plots visible in your Jupyter Notebook: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot and Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2f680b7048>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fO8tSRhYRadFKGUVzpFlmWFnYu20brFmT/nlNMaTcB0qph0TkCeBvgJdF5AGg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\nQCz+P5p5h0CookTiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f680e58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10*np.pi, 200)\n",
    "sinx = np.sin(x)\n",
    "\n",
    "plt.plot(x, sinx)\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('The Sine Function')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib Figures are composed of many sub-objects...\n",
    "\n",
    "  - **Figures** are the actual window that the graphics are inside.\n",
    "  - **Axes** is what we usually think of as a figure.  It contains the title, labels, lines, markers, tick marks, etc.\n",
    "\n",
    "To modify your figures, you will inevitably need to figure out which object needs to be modified.\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Making Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2f622a36d8>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PlCPkImWfqtTh89iaPx9On7avuY4qjbVokn/IQklTcPpGkOQfspopak+77BPq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\nSP4RERERcxD/H98Io/GQt7DeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f6807c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cosx = np.cos(x)\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax = plt.subplot(1, 2, 1)\n",
    "ax.plot(x, sinx)\n",
    "ax.set_title('Sine of X')\n",
    "\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "ax.plot(x, cosx)\n",
    "plt.title('Cosine of X')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PlCPkImWfqtTh89iaPx9On7avuY4qjbVokn/IQklTcPpGkOQfspopak+77BPq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\nSP4RERERcxD/H98Io/GQt7DeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb67df87dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
    "for ax, data, title in zip(axes, [sinx, cosx], ['Sine', 'Cosine']):\n",
    "    ax.plot(x, data)\n",
    "    ax.set_title('{} of X'.format(title))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2f621011d0>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aRpB/Fbo3JAKAnM5jXSCiZq///4cx9kfG2BAi8rs7uTf5+4X28qhK/hKaf9DO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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f62152a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, sinx, 'ko', label='Sine')\n",
    "ax.plot(x, cosx, 'r', label='Cosine')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: While shorter, this is still kind of ugly code. We will solve this problem in later sessions with Pandas and Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Gallery of PyPlot Plotting Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"http://matplotlib.org/gallery.html\" height=500 width=900></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iFrame src=\"http://matplotlib.org/gallery.html\" height=500 width=900></iFrame>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# PyPlot Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iFrame src=\"http://matplotlib.org/users/pyplot_tutorial.html\" height=400 width=900></iFrame>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML('<iFrame src=\"http://matplotlib.org/users/pyplot_tutorial.html\" height=400 width=900></iFrame>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Matplotlib Styles: Templates for making your plot look nice!\n",
    "**Caution:** The style implementation assumes that you are starting with default, so modifying it multiple times may create a weird hybrid-style.  Reset the Python Kernel to confirm that it looks right!\n",
    "\n",
    "Alternatively, you can use the following code snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['seaborn-white', 'seaborn-darkgrid', 'seaborn-talk', 'dark_background', 'classic', 'seaborn-ticks', 'seaborn-dark-palette', 'seaborn-pastel', 'seaborn-muted', 'seaborn-whitegrid']\n"
     ]
    }
   ],
   "source": [
    "print(plt.style.available[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f2f61eff6a0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AaHrcx45Kx476O0bNWrRUWYuW/k5RK+5DAGc7SigA3x07quO/Gu3vFDUKm7dI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\nFACAnYQCALCTUAAAdhIKAMAup5pY8ulhetAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2f61e1c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_data = np.random.randn(200)\n",
    "\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "plt.style.use('ggplot')\n",
    "plt.hist(random_data, bins=13)\n",
    "plt.title('My Completely Normal Histogram')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Questions and Discussion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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