All of 'em

Graphing

{
 "metadata": {
  "name": "Three new matplotlib plots"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "21 Interactive Plots from matplotlib, ggplot for Python, prettyplotlib, Stack Overflow, and seaborn"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Plotly is collaborative, makes beautiful interactive graphs with a URL for you, and stores your data and graphs together. This NB shows how to use Plotly to share plots from some awesome Python libraries. For best results, you can copy and paste this Notebook and key. Just run `$ pip install plotly` and start up a Notebook. \n\nLet's set up our environment and packages."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import plotly.plotly as py\nimport plotly.tools as tls\nfrom plotly.graph_objs import *\npy.sign_in(\"IPython.Demo\", \"1fw3zw2o13\")",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# tls.set_credentials_file(\"IPython.Demo\", \"1fw3zw2o13\")\n# tls.get_credentials_file()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import plotly\nplotly.__version__",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": "'1.0.0'"
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "I. matplotlib Gallery graphs"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "For matplotlib experts, you'll recognize these graphs from the [matplotlib gallery](matplotlib.org/gallery.html). \n\nIn addition to matplotlib and Plotly's own [Python API](https://plot.ly/python), You can also use Plotly's other [APIs](https://plot.ly/api) for MATLAB, R, Perl, Julia, and REST to write to graphs. That means you and I could edit the same graph with any language. We can even edit the graph and data from the GUI, so technical and non-technical teams can work together. And all the graphs go to your profile, like this: https://plot.ly/~IPython.Demo.\n\nYou control [the privacy](http://plot.ly/python/privacy) by setting `world_readable` to False or True, and can control your [sharing](http://plot.ly/python/file-sharing)."
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Let's get started with this [damped oscillation](http://matplotlib.org/examples/pylab_examples/legend_demo2.html) graph."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig1 = plt.figure()\n# Make a legend for specific lines.\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nt1 = np.arange(0.0, 2.0, 0.1)\nt2 = np.arange(0.0, 2.0, 0.01)\n\n# note that plot returns a list of lines.  The \"l1, = plot\" usage\n# extracts the first element of the list into l1 using tuple\n# unpacking.  So l1 is a Line2D instance, not a sequence of lines\nl1, = plt.plot(t2, np.exp(-t2))\nl2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')\nl4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')\n\nplt.xlabel('time')\nplt.ylabel('volts')\nplt.title('Damped oscillation')\n\nplt.show()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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11ajXqPU3pxk7BVJGmbzRCT4hNy+X4hPFMBT7lxlrT/cQcBkiPo+gfZv2XFpzibprLDtG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\nCB4jSkMQBEHwGFEagiAIgsf8f6/NeKkjAey4AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x11091ab90>"
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now, to convert it to a Plotly figure, this is all it takes:"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "py.iplot_mpl(fig1)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3486\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f07f190>"
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can hover, zoom, and pan on the figure. You can also strip out the matplotlib styling, and use Plotly's default styling."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = tls.mpl_to_plotly(fig1)\nfig['layout'].update(showlegend=True)\nfig.strip_style()\npy.iplot(fig)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3487\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1108e6390>"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Next up, an example from [pylab](http://matplotlib.org/examples/pylab_examples/arctest.html)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig2 = plt.figure()\n\nfrom pylab import *\n\ndef f(t):\n    'a damped exponential'\n    s1 = cos(2*pi*t)\n    e1 = exp(-t)\n    return multiply(s1,e1)\n\nt1 = arange(0.0, 5.0, .2)\n\n\nl = plot(t1, f(t1), 'ro')\nsetp(l, 'markersize', 30)\nsetp(l, 'markerfacecolor', 'b')\n\npy.iplot_mpl(fig2)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3488\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f144390>"
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Here's where this gets special. You can get the data from any Plotly graph. That means you can re-plot the graph or part of it, or use your favorite Python tools to wrangle and analyze your data. Check out our [getting started guide](http://nbviewer.ipython.org/github/etpinard/plotly-python-doc/blob/1.0/s0_getting-started/s0_getting-started.ipynb) for a full background on these features."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "tls.mpl_to_plotly(fig2).get_data()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": "[{'name': '_line0',\n  'x': [0.0,\n   0.20000000000000001,\n   0.40000000000000002,\n   0.60000000000000009,\n   0.80000000000000004,\n   1.0,\n   1.2000000000000002,\n   1.4000000000000001,\n   1.6000000000000001,\n   1.8,\n   2.0,\n   2.2000000000000002,\n   2.4000000000000004,\n   2.6000000000000001,\n   2.8000000000000003,\n   3.0,\n   3.2000000000000002,\n   3.4000000000000004,\n   3.6000000000000001,\n   3.8000000000000003,\n   4.0,\n   4.2000000000000002,\n   4.4000000000000004,\n   4.6000000000000005,\n   4.8000000000000007],\n  'y': [1.0,\n   0.25300171651849518,\n   -0.54230030891302927,\n   -0.44399794031078654,\n   0.13885028597711233,\n   0.36787944117144233,\n   0.09307413008823949,\n   -0.19950113459002566,\n   -0.16333771416280363,\n   0.051080165611754998,\n   0.1353352832366127,\n   0.034240058964379601,\n   -0.073392365906047419,\n   -0.060088587008433003,\n   0.018791342780197139,\n   0.049787068367863944,\n   0.012596213757493282,\n   -0.026999542555766767,\n   -0.022105355809443925,\n   0.0069129486808399343,\n   0.018315638888734179,\n   0.0046338880779826647,\n   -0.0099325766273000524,\n   -0.0081321059420741033,\n   0.0025431316975542792]}]"
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Or you can get the figure makeup. Here, we're using 'IPython.Demo', which is the username and '3357' which is the figure number. You can use this command on Plotly graphs to interact with them from the console. You can access  graphs via a URL. For example, for this plot, it's:\n\nhttps://plot.ly/~IPython.Demo/3357/\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pylab = py.get_figure('IPython.Demo', '3357')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "#print figure\nprint pylab.to_string()",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "Figure(\n    data=Data([\n        Scatter(\n            x=[0.0, 0.2, 0.4, 0.6000000000000001, 0.8, 1.0, 1.2000000000000...],\n            y=[1.0, 0.2530017165184952, -0.5423003089130293, -0.44399794031...],\n            name='_line0',\n            mode='markers',\n            marker=Marker(\n                symbol='dot',\n                line=Line(\n                    color='#000000',\n                    width=0.5\n                ),\n                size=30,\n                color='#0000FF',\n                opacity=1\n            )\n        )\n    ]),\n    layout=Layout(\n        xaxis=XAxis(\n            domain=[0.0, 1.0],\n            range=[0.0, 5.0],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='y',\n            mirror=True\n        ),\n        yaxis=YAxis(\n            domain=[0.0, 1.0],\n            range=[-0.6000000000000001, 1.2],\n            showline=True,\n            ticks='inside',\n            showgrid=False,\n            zeroline=False,\n            anchor='x',\n            mirror=True\n        ),\n        hovermode='closest',\n        showlegend=False\n    )\n)\n"
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Now let's suppose we wanted to add a fit to the graph (see our [fits post](http://blog.plot.ly/post/84402951992/contour-plots-error-bars-chocolate-beer-meat) to learn more), and re-style it a bit. We can go into the web app, fork a copy, and edit the image in our GUI. No coding required."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/RusH4k2.png?1')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<img src=\"https://i.imgur.com/RusH4k2.png?1\"/>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": "<IPython.core.display.Image at 0x10f098a90>"
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "I can now call that graph into the NB. And if I want to see the data for the fit or access the figure styling, I can  run the same commands, but now on this graph."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "tls.embed('MattSundquist', '1307')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~MattSundquist/1307\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f0985d0>"
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "And Plotly graphs are interactive. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import HTML\nHTML('<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<br><center><iframe class=\"vine-embed\" src=\"https://vine.co/v/Mvzin6HZzLB/embed/simple\" width=\"600\" height=\"600\" frameborder=\"0\"></iframe><script async src=\"//platform.vine.co/static/scripts/embed.js\" charset=\"utf-8\"></script></center><br>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": "<IPython.core.display.HTML at 0x10f098b10>"
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can also plot with Plotly with pandas, NumPy, datetime, and more of your favorite Python tools. We've already imported numpy and matplotlib; here we've kept them in so you can simply copy and paste these examples into your own NB. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig3 = plt.figure()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# make a little extra space between the subplots\nplt.subplots_adjust(wspace=0.5)\n\ndt = 0.01\nt = np.arange(0, 30, dt)\nnse1 = np.random.randn(len(t))                 # white noise 1\nnse2 = np.random.randn(len(t))                 # white noise 2\nr = np.exp(-t/0.05)\n\ncnse1 = np.convolve(nse1, r, mode='same')*dt   # colored noise 1\ncnse2 = np.convolve(nse2, r, mode='same')*dt   # colored noise 2\n\n# two signals with a coherent part and a random part\ns1 = 0.01*np.sin(2*np.pi*10*t) + cnse1\ns2 = 0.01*np.sin(2*np.pi*10*t) + cnse2\n\nplt.subplot(211)\nplt.plot(t, s1, 'b-', t, s2, 'g-')\nplt.xlim(0,5)\nplt.xlabel('time')\nplt.ylabel('s1 and s2')\nplt.grid(True)\n\nplt.subplot(212)\ncxy, f = plt.csd(s1, s2, 256, 1./dt)\nplt.ylabel('CSD (db)')\n\npy.iplot_mpl(fig3)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3489\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x110939e90>"
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Another subplotting example using Plotly's defaults. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig4 = plt.figure()\n\nfrom pylab import figure, show\nfrom numpy import arange, sin, pi\n\nt = arange(0.0, 1.0, 0.01)\n\nfig = figure(1)\n\nax1 = fig.add_subplot(211)\nax1.plot(t, sin(2*pi*t))\nax1.grid(True)\nax1.set_ylim( (-2,2) )\nax1.set_ylabel('1 Hz')\nax1.set_title('A sine wave or two')\n\nfor label in ax1.get_xticklabels():\n    label.set_color('r')\n\n\nax2 = fig.add_subplot(212)\nax2.plot(t, sin(2*2*pi*t))\nax2.grid(True)\nax2.set_ylim( (-2,2) )\nl = ax2.set_xlabel('Hi mom')\nl.set_color('g')\nl.set_fontsize('large')\n\npy.iplot_mpl(fig4, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3490\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x110d22510>"
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "From the gallery here we're shwoing [Anscombe's quartet](http://matplotlib.org/examples/pylab_examples/anscombe.html). You  might also like Plotly's [blog post](blog.plot.ly/post/68951620673/why-graph-anscombes-quartet) on the subject."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig5 = plt.figure()\n\nfrom __future__ import print_function\n\"\"\"\nEdward Tufte uses this example from Anscombe to show 4 datasets of x\nand y that have the same mean, standard deviation, and regression\nline, but which are qualitatively different.\n\nmatplotlib fun for a rainy day\n\"\"\"\n\nfrom pylab import *\n\nx =  array([10, 8, 13, 9, 11, 14, 6, 4, 12, 7, 5])\ny1 = array([8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68])\ny2 = array([9.14, 8.14, 8.74, 8.77, 9.26, 8.10, 6.13, 3.10, 9.13, 7.26, 4.74])\ny3 = array([7.46, 6.77, 12.74, 7.11, 7.81, 8.84, 6.08, 5.39, 8.15, 6.42, 5.73])\nx4 = array([8,8,8,8,8,8,8,19,8,8,8])\ny4 = array([6.58,5.76,7.71,8.84,8.47,7.04,5.25,12.50,5.56,7.91,6.89])\n\ndef fit(x):\n    return 3+0.5*x\n\n\n\nxfit = array( [amin(x), amax(x) ] )\n\nsubplot(221)\nplot(x,y1,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'I', fontsize=20)\n\nsubplot(222)\nplot(x,y2,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), xticklabels=[], yticks=(4,8,12), yticklabels=[], xticks=(0,10,20))\ntext(3,12, 'II', fontsize=20)\n\nsubplot(223)\nplot(x,y3,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\ntext(3,12, 'III', fontsize=20)\nsetp(gca(), yticks=(4,8,12), xticks=(0,10,20))\n\nsubplot(224)\n\nxfit = array([amin(x4),amax(x4)])\nplot(x4,y4,'ks', xfit, fit(xfit), 'r-', lw=2)\naxis([2,20,2,14])\nsetp(gca(), yticklabels=[], yticks=(4,8,12), xticks=(0,10,20))\ntext(3,12, 'IV', fontsize=20)\n\n#verify the stats\npairs = (x,y1), (x,y2), (x,y3), (x4,y4)\nfor x,y in pairs:\n    print ('mean=%1.2f, std=%1.2f, r=%1.2f'%(mean(y), std(y), corrcoef(x,y)[0][1]))\n\npy.iplot_mpl(fig5, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "mean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\nmean=7.50, std=1.94, r=0.82\n"
      },
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3491\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f073250>"
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "And a final [histogram](http://matplotlib.org/examples/statistics/histogram_demo_features.html) from the matplotlib gallery. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig6 = plt.figure()\n\nimport numpy as np\nimport matplotlib.mlab as mlab\nimport matplotlib.pyplot as plt\n\n\n# example data\nmu = 100 # mean of distribution\nsigma = 15 # standard deviation of distribution\nx = mu + sigma * np.random.randn(10000)\n\nnum_bins = 50\n# the histogram of the data\nn, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)\n# add a 'best fit' line\ny = mlab.normpdf(bins, mu, sigma)\nplt.plot(bins, y, 'r--')\nplt.xlabel('Smarts')\nplt.ylabel('Probability')\n\n# Tweak spacing to prevent clipping of ylabel\nplt.subplots_adjust(left=0.15)\n\npy.iplot_mpl(fig6, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3492\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1108f6e50>"
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Want to see more matplotlylib graphs? Head over to our [API](https://plot.ly/python) and copy and paste away."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from IPython.display import Image\nImage(url='https://i.imgur.com/HEJEnjQ.png')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<img src=\"https://i.imgur.com/HEJEnjQ.png\"/>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": "<IPython.core.display.Image at 0x10f668090>"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "II. ggplot for Python"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "An exciting package by [Greg Lamp](https://github.com/glamp) and the team at [\u0177hat](https://yhathq.com/) is [ggplot for Python](https://github.com/yhat/ggplot). You can draw figures with ggplot's wonderful syntax and share them with Plotly. You'll want to run `$ pip install ggplot` to get started."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from ggplot import *",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We'll start out with a plot from the diamonds dataset. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "a = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + facet_wrap(\"cut\") ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Then share it to Plotly."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = a.draw() \npy.iplot_mpl(fig, strip_style=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3493\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1109693d0>"
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Line charts can be interactive (drag your mouse along the line to see the data on the hover)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "b = ggplot(aes(x='date', y='beef'), data=meat) + \\\n    geom_line() ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = b.draw() \npy.iplot_mpl(fig)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3494\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f0d9690>"
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Histograms are also fun to hover over to get the exact data. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "c = ggplot(aes(x='price'), data=diamonds) + geom_histogram() + ggtitle('My Diamond Histogram')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = c.draw() \npy.iplot_mpl(fig, strip_style=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3495\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1109b3750>"
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "d = ggplot(aes(x='x', y='y', color='z'), data=diamonds.head(1000)) +\\\n    geom_point() ",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = d.draw() \npy.iplot_mpl(fig, strip_style=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3496\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1109379d0>"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can also use more advanced plotting types in collaboration with pandas. You can add a geom."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "random_walk1 = pd.DataFrame({\n  \"x\": np.arange(100),\n  \"y\": np.cumsum(np.random.choice([-1, 1], 100))\n})\nrandom_walk2 = pd.DataFrame({\n  \"x\": np.arange(100),\n  \"y\": np.cumsum(np.random.choice([-1, 1], 100))\n})\ne = ggplot(aes(x='x', y='y'), data=random_walk1) + \\\n    geom_step() + \\\n    geom_step(aes(x='x', y='y'), data=random_walk2)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig = e.draw() \npy.iplot_mpl(fig, strip_style=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3497\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1061c8d10>"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "III. Prettyplotlib graphs in Plotly"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "The gallery of [examples](http://nbviewer.ipython.org/github/olgabot/prettyplotlib/blob/master/ipython_notebooks/Examples%20of%20everything%20pretty%20and%20plotted!.ipynb?create=1) from [prettyplotlib](https://github.com/olgabot/prettyplotlib), a library by [Olga Botvinnik](https://github.com/olgabot), can be a fun one to make interactive. Here's a scatter; let us know if you make others. You'll note that not all elements of the styling come through. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig12 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    x = np.random.normal(loc=i, size=800)\n    y = np.random.normal(loc=i, size=800)\n    ax = ppl.scatter(x, y, label=str(i))\n    \nppl.legend(ax)\nax.set_title('prettyplotlib `scatter`')\nax.legend().set_visible(False)\n\npy.iplot_mpl(fig12)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3498\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f1d7210>"
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "And another prettyplotlib example."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig13 = plt.figure()\n\nimport prettyplotlib as ppl\n\n# Set the random seed for consistency\nnp.random.seed(12)\n\n# Show the whole color range\nfor i in range(8):\n    y = np.random.normal(size=1000).cumsum()\n    x = np.arange(1000)\n\n    # Specify both x and y\n    ppl.plot(x, y, label=str(i), linewidth=0.75)\n        \npy.iplot_mpl(fig13)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3499\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10fd5d550>"
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "IV. Plotting with seaborn"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Another library we really dig is [seaborn](http://stanford.edu/~mwaskom/software/seaborn/index.html), by Michael Waskom. You may need to [import six](http://stackoverflow.com/questions/13967428/importerror-no-module-named-six), which you can do from pip. The styling isn't yet translated to Plotly, so we'll go to Plotly's default settings. "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import seaborn as sns\nfrom matplotlylib import fig_to_plotly",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "def sinplot(flip=1):\n    x = np.linspace(0, 14, 100)\n    for i in range(1, 7):\n        plt.plot(x, np.sin(x + i * .5) * (7 - i) * flip)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig14 = plt.figure()\n\nsns.set_style(\"dark\")\nsinplot()\n\npy.iplot_mpl(fig14, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3500\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x111e96650>"
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "You can also run subplots like this."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig15 = plt.figure()\n\nwith sns.axes_style(\"darkgrid\"):\n    plt.subplot(211)\n    sinplot()\nplt.subplot(212)\nsinplot(-1)\n\npy.iplot_mpl(fig15, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3501\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10e81f890>"
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "And a final example, [combining plot types](http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distributions.html#basic-visualization-with-histograms)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import numpy as np\nfrom numpy.random import randn\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig16 = plt.figure()\n\nsns.set_palette(\"hls\")\nmpl.rc(\"figure\", figsize=(8, 4))\ndata = randn(200)\nsns.distplot(data);\n\npy.iplot_mpl(fig16, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3502\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10fd75490>"
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "V. Stack Overflow Answers"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "We love Stack Overflow, so wanted answer a few questions from there, in Plotly. If you want to plot data you already have as a [histogram](http://stackoverflow.com/questions/5328556/histogram-matplotlib) and make it interactive, try this one out."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig17 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nmu, sigma = 100, 15\nx = mu + sigma * np.random.randn(10000)\nhist, bins = np.histogram(x, bins=50)\nwidth = 0.7 * (bins[1] - bins[0])\ncenter = (bins[:-1] + bins[1:]) / 2\nplt.bar(center, hist, align='center', width=width)\n\npy.iplot_mpl(fig17, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3503\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f5c0250>"
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Here is how to create a [density plot](http://stackoverflow.com/questions/4150171/how-to-create-a-density-plot-in-matplotlib/4152016#4152016) like you might in R, but in matplotlib."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig18 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.stats import gaussian_kde\ndata = [1.5]*7 + [2.5]*2 + [3.5]*8 + [4.5]*3 + [5.5]*1 + [6.5]*8\ndensity = gaussian_kde(data)\nxs = np.linspace(0,8,200)\ndensity.covariance_factor = lambda : .25\ndensity._compute_covariance()\nplt.plot(xs,density(xs))\n\npy.iplot_mpl(fig18, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3504\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f55cd10>"
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Drawing a simple example of [different lines for different plots](http://stackoverflow.com/questions/4805048/how-to-get-different-lines-for-different-plots-in-a-single-figure/4805456#4805456) looks like this..."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig19 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = np.arange(10)\n\nplt.plot(x, x)\nplt.plot(x, 2 * x)\nplt.plot(x, 3 * x)\nplt.plot(x, 4 * x)\n\npy.iplot_mpl(fig19, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3505\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10fd63190>"
      }
     ],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "...and can get more exciting like this."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig20 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nnum_plots = 10\n\n# Have a look at the colormaps here and decide which one you'd like:\n# http://matplotlib.org/1.2.1/examples/pylab_examples/show_colormaps.html\ncolormap = plt.cm.gist_ncar\nplt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, num_plots)])\n\n# Plot several different functions...\nx = np.arange(10)\nlabels = []\nfor i in range(1, num_plots + 1):\n    plt.plot(x, i * x + 5 * i)\n    labels.append(r'$y = %ix + %i$' % (i, 5*i))\n\npy.iplot_mpl(fig20, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3506\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x1108bde90>"
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Plotly also lets you draw [variables as subscripts in math mode](http://stackoverflow.com/questions/23276918/writing-variables-as-subscripts-in-math-mode)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig21 = plt.figure()\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport matplotlib.mlab as mlab\n\nmean = [10,12,16,22,25]\nvariance = [3,6,8,10,12]\n\nx = np.linspace(0,40,1000)\n\nfor i in range(4):\n    sigma = np.sqrt(variance[i])\n    y = mlab.normpdf(x,mean[i],sigma)\n    plt.plot(x,y, label=r'$v_{}$'.format(i+1))\n\nplt.xlabel(\"X\")\nplt.ylabel(\"P(X)\")        \n\npy.iplot_mpl(fig21, strip_style = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/3507\" height=\"525\" width=\"100%\"></iframe>",
       "metadata": {},
       "output_type": "display_data",
       "text": "<IPython.core.display.HTML at 0x10f488510>"
      }
     ],
     "prompt_number": 78
    }
   ],
   "metadata": {}
  }
 ]
}

What about adding a sidebar it? So it would be easier to navigate through the notebook.
Mine is like this:
https://nbviewer.jupyter.org/github/cqcn1991/Wind-Speed-Analysis/blob/master/GMM.ipynb