{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center>\n",
    "<h1>Introduction to Data Analysis with Python</h1>\n",
    "<br>\n",
    "<h3>Dr. Thomas Wiecki</h3><br>\n",
    "<h3>Lead Data Scientist</h3>\n",
    "<img width=40% src=\"http://i2.wp.com/stuffled.com/wp-content/uploads/2014/09/Quantopian-Logo-EPS-vector-image.png?resize=1020%2C680\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"http://cdn.nutanix.com/wp-content/uploads/2013/09/5530553658_cf0a5dd64d_z.jpg\">\n",
    "\n",
    "Source: http://www.nutanix.com/2013/09/16/the-cup-has-been-flipped/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center>\n",
    "<h1><strike>Introduction to Data Analysis with Python</strike></h1>\n",
    "<h1>The Path of the PyData Ninja</h1>\n",
    "<br>\n",
    "<h3>Dr. Thomas Wiecki</h3><br>\n",
    "<h3>Lead Data Scientist</h3>\n",
    "<img width=40% src=\"http://i2.wp.com/stuffled.com/wp-content/uploads/2014/09/Quantopian-Logo-EPS-vector-image.png?resize=1020%2C680\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# About me\n",
    "\n",
    "* Lead Data Scientist at [Quantopian Inc](https://www.quantopian.com): Building a crowd sourced hedge fund.\n",
    "* PhD from Brown University -- research on computational neuroscience and machine learning using Bayesian modeling.\n",
    "* Twitter: [@twiecki](https://twitter.com/twiecki)\n",
    "* GitHub: [@twiecki](https://github.com/twiecki)\n",
    "* Blog: [http://twiecki.github.io](https://twiecki.github.io)\n",
    "* Developer of [PyMC3](https://github.com/pymc-devs/pymc3)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<a href=\"https://quantopian.com\"><img width=40% src=\"http://i2.wp.com/stuffled.com/wp-content/uploads/2014/09/Quantopian-Logo-EPS-vector-image.png?resize=1020%2C680\"></a>\n",
    "\n",
    "* We back the best investment algorithms with investor capital, trading operations, and technology.\n",
    "* Do your research in our hosted IPython environment using stock price history, corporate fundamental data, and other data sets.\n",
    "* Write your algorithm in your browser. Then backtest it, for free, over 13 years of minute-level data.\n",
    "* When you enter the contest, your algorithm will also be considered for our hedge fund.\n",
    "* [We're hiring in Düsseldorf: Operations Engineer!](https://www.quantopian.com/about#op-37090-operations-engineer-israel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why use Python for data analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Python is a **general purpose language** -> No hodge-podge of perl, bash, matlab, fortran.\n",
    "* Very easy to learn.\n",
    "* Quality and quantity of data analysis libraries is very high and growing at a rapid pace.\n",
    "* What are the alternatives?\n",
    "  - R: \"The best thing about R is that it was written by statisticians. The worst thing about R is that it was written by statisticians.\" Bow Cogwill\n",
    "  - Matlab: $$$, not open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Jobs!\n",
    "\n",
    "<img src=\"http://www.indeed.com/trendgraph/jobgraph.png?q=R++and+%28%22big+data%22+or+%22statistical+analysis%22+or+%22data+mining%22+or+%22data+analytics%22+or+%22machine+learning%22+or+%22quantitative+analysis%22+or+%22business+analytics%22+or+%22statistical+software%22+or+%22predictive+modeling%22%29+%21%22R+D%22+%21%22A+R%22+%21%22H+R%22+%21%22R+N%22++%21toys+%21kids+%21%22+R+Walgreen%22+%21walmart+%21%22HVAC+R%22+%21%22R+Bard%22++%2C+python+and+%28%22big+data%22+or+%22statistical+analysis%22+or+%22data+mining%22+or+%22data+analytics%22+or+%22machine+learning%22+or+%22quantitative+analysis%22+or+%22business+analytics%22+or+%22statistical+software%22+or+%22predictive+modeling%22%29\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center> <h2>The PyData Stack</h2>\n",
    "Source: [Jake VanderPlas: State of the Tools](https://www.youtube.com/watch?v=5GlNDD7qbP4)\n",
    "<center><img src='pydata_stack-0.jpg' width=50%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center> <h2>The PyData Stack</h2>\n",
    "\n",
    "<center><img src='pydata_stack-1.jpg' width=50%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center> <h2>The PyData Stack</h2>\n",
    "\n",
    "<center><img src='pydata_stack-2.jpg' width=50%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center> <h2>The PyData Stack</h2>\n",
    "\n",
    "<center><img src='pydata_stack-3.jpg' width=50%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center> <h2>The PyData Stack</h2>\n",
    "\n",
    "<center><img src='pydata_stack-4.jpg' width=50%></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Level 0: n00b\n",
    "\n",
    "<img src=\"beginner.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# How to get started\n",
    "\n",
    "* Start by installing the [Anaconda Python distribution](http://continuum.io/downloads#py34) (use Python 3.4)\n",
    "* Install the jupyter notebook (former IPython)\n",
    "* Do a basic Python tutorial to get a handle on the syntax, e.g. [Learn Python the Hard Way](http://learnpythonthehardway.org/)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Interpreted and interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 * 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "x = [1, 2, 3]\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    }
   ],
   "source": [
    "x.append(4)\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'height': [1.7, 1.8, 1.5], 'weight': [60, 120, 50]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements = {'height': [1.70, 1.80, 1.50], 'weight': [60, 120, 50]}\n",
    "measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.7, 1.8, 1.5]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measurements['height']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9, 16]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1, 2, 3, 4]\n",
    "[i**2 for i in x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[20.761245674740486, 37.03703703703704, 22.22222222222222]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_bmi(weight, height):\n",
    "    return weight / height**2\n",
    "\n",
    "[calc_bmi(w, h) for w, h in zip(measurements['weight'], measurements['height'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Level 1: \"The Pandas Wrangler\"\n",
    "\n",
    "<img src=\"amateur.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## How to become a \"Pandas Wrangler\"\n",
    "\n",
    "* Learn Pandas (data wrangling): http://pandas.pydata.org/pandas-docs/stable/tutorials.html\n",
    "* Learn Seaborn (data visualization): http://stanford.edu/~mwaskom/software/seaborn/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Why not start with NumPy and Matplotlib?\n",
    "* These libraries have become core libraries.\n",
    "* Better results can be achieved starting with Pandas and Seaborn.\n",
    "* For more motivation, see http://twiecki.github.io/blog/2014/11/18/python-for-data-science/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     3\n",
       "2     5\n",
       "3   NaN\n",
       "4     6\n",
       "5     8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-1.825304</td>\n",
       "      <td>0.016285</td>\n",
       "      <td>-0.306601</td>\n",
       "      <td>0.004272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.192045</td>\n",
       "      <td>-0.291267</td>\n",
       "      <td>1.951792</td>\n",
       "      <td>1.708918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.672820</td>\n",
       "      <td>-2.051800</td>\n",
       "      <td>0.113415</td>\n",
       "      <td>-0.594967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.453007</td>\n",
       "      <td>1.282767</td>\n",
       "      <td>-0.113163</td>\n",
       "      <td>-1.824503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.824425</td>\n",
       "      <td>2.156586</td>\n",
       "      <td>-0.047000</td>\n",
       "      <td>-1.042518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.359821</td>\n",
       "      <td>-0.896814</td>\n",
       "      <td>0.617897</td>\n",
       "      <td>-0.723848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -1.825304  0.016285 -0.306601  0.004272\n",
       "2013-01-02  0.192045 -0.291267  1.951792  1.708918\n",
       "2013-01-03 -0.672820 -2.051800  0.113415 -0.594967\n",
       "2013-01-04 -1.453007  1.282767 -0.113163 -1.824503\n",
       "2013-01-05  0.824425  2.156586 -0.047000 -1.042518\n",
       "2013-01-06  0.359821 -0.896814  0.617897 -0.723848"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "\n",
    "df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.192045</td>\n",
       "      <td>-0.291267</td>\n",
       "      <td>1.951792</td>\n",
       "      <td>1.708918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.824425</td>\n",
       "      <td>2.156586</td>\n",
       "      <td>-0.047000</td>\n",
       "      <td>-1.042518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.359821</td>\n",
       "      <td>-0.896814</td>\n",
       "      <td>0.617897</td>\n",
       "      <td>-0.723848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.192045 -0.291267  1.951792  1.708918\n",
       "2013-01-05  0.824425  2.156586 -0.047000 -1.042518\n",
       "2013-01-06  0.359821 -0.896814  0.617897 -0.723848"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.429140\n",
       "B    0.035960\n",
       "C    0.369390\n",
       "D   -0.412108\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.527837\n",
       "2013-01-02    0.890372\n",
       "2013-01-03   -0.801543\n",
       "2013-01-04   -0.526977\n",
       "2013-01-05    0.472873\n",
       "2013-01-06   -0.160736\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis='columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Mixed types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A          B  C  D      E    F\n",
       "0  1 2013-01-02  1  3   test  foo\n",
       "1  1 2013-01-02  1  3  train  foo\n",
       "2  1 2013-01-02  1  3   test  foo\n",
       "3  1 2013-01-02  1  3  train  foo"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({ 'A' : 1.,\n",
    "                     'B' : pd.Timestamp('20130102'),\n",
    "                     'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "                     'D' : np.array([3] * 4,dtype='int32'),\n",
    "                     'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "                     'F' : 'foo' })\n",
    "\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Grouping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "                          'foo', 'bar', 'foo', 'foo'],\n",
    "                   'B' : ['one', 'one', 'two', 'three',\n",
    "                          'two', 'two', 'one', 'three'],\n",
    "                   'C' : np.random.randn(8),\n",
    "                   'D' : np.random.randn(8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.343733</td>\n",
       "      <td>-0.387430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>2.061690</td>\n",
       "      <td>2.454234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>0.626621</td>\n",
       "      <td>-1.106039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>1.511600</td>\n",
       "      <td>0.963582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.187094</td>\n",
       "      <td>-1.310974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.065222</td>\n",
       "      <td>-0.983320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.924636</td>\n",
       "      <td>0.349164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>2.093810</td>\n",
       "      <td>-0.913885</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one  0.343733 -0.387430\n",
       "1  bar    one  2.061690  2.454234\n",
       "2  foo    two  0.626621 -1.106039\n",
       "3  bar  three  1.511600  0.963582\n",
       "4  foo    two -1.187094 -1.310974\n",
       "5  bar    two -1.065222 -0.983320\n",
       "6  foo    one -0.924636  0.349164\n",
       "7  foo  three  2.093810 -0.913885"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>2.508068</td>\n",
       "      <td>2.434496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>0.952434</td>\n",
       "      <td>-3.369164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  2.508068  2.434496\n",
       "foo  0.952434 -3.369164"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>2.061690</td>\n",
       "      <td>2.454234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>1.511600</td>\n",
       "      <td>0.963582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.065222</td>\n",
       "      <td>-0.983320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.580903</td>\n",
       "      <td>-0.038266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>2.093810</td>\n",
       "      <td>-0.913885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.560473</td>\n",
       "      <td>-2.417013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one    2.061690  2.454234\n",
       "    three  1.511600  0.963582\n",
       "    two   -1.065222 -0.983320\n",
       "foo one   -0.580903 -0.038266\n",
       "    three  2.093810 -0.913885\n",
       "    two   -0.560473 -2.417013"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seaborn: Generating statistical plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lowAkfP3uBZiVMfUVbYjoUhq1Cn9/1wKkJujx531n8eHJHtGRSDCW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\nQKHrGuwnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7e82c65c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.normal(size=100)\n",
    "sns.distplot(x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 2D distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.086599</td>\n",
       "      <td>0.232294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.443143</td>\n",
       "      <td>-0.354076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.652826</td>\n",
       "      <td>1.165967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.903281</td>\n",
       "      <td>0.170521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.148413</td>\n",
       "      <td>2.050707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.176674</td>\n",
       "      <td>2.375160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.845552</td>\n",
       "      <td>1.879006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-0.962104</td>\n",
       "      <td>0.340105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.038327</td>\n",
       "      <td>0.011257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.704808</td>\n",
       "      <td>-0.132214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.265136</td>\n",
       "      <td>0.426827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.327651</td>\n",
       "      <td>3.319972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.044100</td>\n",
       "      <td>0.249657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.451112</td>\n",
       "      <td>0.740770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.596272</td>\n",
       "      <td>1.360725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.137805</td>\n",
       "      <td>1.041698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.168201</td>\n",
       "      <td>2.713771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-1.319453</td>\n",
       "      <td>-0.503621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.461970</td>\n",
       "      <td>2.389124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.334954</td>\n",
       "      <td>1.328563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.649478</td>\n",
       "      <td>1.537904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.310419</td>\n",
       "      <td>1.523426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.710832</td>\n",
       "      <td>0.943226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-0.823799</td>\n",
       "      <td>1.775166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.992667</td>\n",
       "      <td>1.022176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.568753</td>\n",
       "      <td>0.557493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.301274</td>\n",
       "      <td>1.079018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-1.733951</td>\n",
       "      <td>0.567187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.952436</td>\n",
       "      <td>1.852690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-1.174882</td>\n",
       "      <td>0.873883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>0.563523</td>\n",
       "      <td>2.138955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>1.033686</td>\n",
       "      <td>4.141070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>0.292181</td>\n",
       "      <td>1.408222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>-2.301801</td>\n",
       "      <td>-0.042128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>0.104059</td>\n",
       "      <td>0.542621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>0.588103</td>\n",
       "      <td>0.567352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>-1.073328</td>\n",
       "      <td>-0.120820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>1.853767</td>\n",
       "      <td>0.099758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>1.075379</td>\n",
       "      <td>2.611514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.559673</td>\n",
       "      <td>3.366675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>0.747945</td>\n",
       "      <td>2.053162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>-2.927040</td>\n",
       "      <td>-0.621391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>0.134816</td>\n",
       "      <td>0.727113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>0.015388</td>\n",
       "      <td>2.829295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>0.742623</td>\n",
       "      <td>2.058462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>-0.229863</td>\n",
       "      <td>1.139025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>-1.033777</td>\n",
       "      <td>-0.642099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>0.428791</td>\n",
       "      <td>0.762164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>-0.596019</td>\n",
       "      <td>2.079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>1.819033</td>\n",
       "      <td>1.434940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>0.254380</td>\n",
       "      <td>1.494345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>2.276169</td>\n",
       "      <td>0.408099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>-0.076959</td>\n",
       "      <td>-1.321194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.496929</td>\n",
       "      <td>-0.174779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>0.723700</td>\n",
       "      <td>1.561495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>1.097657</td>\n",
       "      <td>2.438591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.460952</td>\n",
       "      <td>2.506192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0.458422</td>\n",
       "      <td>1.522599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>1.411305</td>\n",
       "      <td>1.632619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-1.533196</td>\n",
       "      <td>2.016695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            x         y\n",
       "0    1.086599  0.232294\n",
       "1   -0.443143 -0.354076\n",
       "2    0.652826  1.165967\n",
       "3   -0.903281  0.170521\n",
       "4   -1.148413  2.050707\n",
       "5   -0.176674  2.375160\n",
       "6    0.845552  1.879006\n",
       "7   -0.962104  0.340105\n",
       "8    0.038327  0.011257\n",
       "9   -0.704808 -0.132214\n",
       "10  -0.265136  0.426827\n",
       "11   1.327651  3.319972\n",
       "12  -0.044100  0.249657\n",
       "13   0.451112  0.740770\n",
       "14   0.596272  1.360725\n",
       "15  -0.137805  1.041698\n",
       "16   1.168201  2.713771\n",
       "17  -1.319453 -0.503621\n",
       "18   0.461970  2.389124\n",
       "19  -0.334954  1.328563\n",
       "20  -0.649478  1.537904\n",
       "21   0.310419  1.523426\n",
       "22   0.710832  0.943226\n",
       "23  -0.823799  1.775166\n",
       "24   0.992667  1.022176\n",
       "25   0.568753  0.557493\n",
       "26  -0.301274  1.079018\n",
       "27  -1.733951  0.567187\n",
       "28   0.952436  1.852690\n",
       "29  -1.174882  0.873883\n",
       "..        ...       ...\n",
       "170  0.563523  2.138955\n",
       "171  1.033686  4.141070\n",
       "172  0.292181  1.408222\n",
       "173 -2.301801 -0.042128\n",
       "174  0.104059  0.542621\n",
       "175  0.588103  0.567352\n",
       "176 -1.073328 -0.120820\n",
       "177  1.853767  0.099758\n",
       "178  1.075379  2.611514\n",
       "179  0.559673  3.366675\n",
       "180  0.747945  2.053162\n",
       "181 -2.927040 -0.621391\n",
       "182  0.134816  0.727113\n",
       "183  0.015388  2.829295\n",
       "184  0.742623  2.058462\n",
       "185 -0.229863  1.139025\n",
       "186 -1.033777 -0.642099\n",
       "187  0.428791  0.762164\n",
       "188 -0.596019  2.079460\n",
       "189  1.819033  1.434940\n",
       "190  0.254380  1.494345\n",
       "191  2.276169  0.408099\n",
       "192 -0.076959 -1.321194\n",
       "193  0.496929 -0.174779\n",
       "194  0.723700  1.561495\n",
       "195  1.097657  2.438591\n",
       "196  0.460952  2.506192\n",
       "197  0.458422  1.522599\n",
       "198  1.411305  1.632619\n",
       "199 -1.533196  2.016695\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean, cov = [0, 1], [(1, .5), (.5, 1)]\n",
    "data = np.random.multivariate_normal(mean, cov, 200)\n",
    "df = pd.DataFrame(data, columns=[\"x\", \"y\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Q3NFWO0NK3VLiQpEYHYK9eRUoKK1Hz4QwqUvyKdEnRNjtdixfvhw33ngjbrrp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de619940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=\"x\", y=\"y\", data=df, kind=\"kde\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### All pairwise combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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thi5oo+D2O6RkD3T8jGO7jlCpvA6pF0g+h6p79tlnMXDgQAwdOhS7du3CpEmT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T+/rjwsVra9WEgH832/tAQXeSZzvWv6Ga2G/bPozFWMGdwN63eeBABMz0tzOiYX5jtP5yClf+To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\nqiqrBW1XWanQmEtTt6kAUFRUpte6umI3ZTygcc67vszeeR49ejQuXryI6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RBCCCGEEI3M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\nJDpbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de4a0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "sns.pairplot(iris);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Seaborn: Regressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wAAc4ET3GlbU7JkQIzn1FRt1XhKs3NZQ8Yhm9HstUeO3MeXqOH6ejJ0OsSJId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\nJFSEEEJkjISKEEKIjJFQEUIIkTH/C5po0tfNOm0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de0b0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"total_bill\", y=\"tip\", hue=\"smoker\", data=tips);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UU1Og36+PlN9fmJdjWIFLZpedd7vP47KxZK6f+VXFLJnrT6W1Lpnrx2qaaeOF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de0ca518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"total_bill\", y=\"tip\", col=\"day\", data=tips,\n",
    "           col_wrap=2, size=3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RUZHuvvtu9ezZU5MnT1Z8fLyysrK0YcOGJk1O5ws5OdkqLDzl9vbV29Y1H1Z9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\nAAAshPAGAABgIYQ3AAAAC/n/nkP/KtJmCc0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7ddfdb1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.factorplot(x=\"time\", y=\"total_bill\", hue=\"smoker\",\n",
    "               col=\"day\", data=tips, kind=\"box\", size=4, aspect=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Level 2: \"The Kaggle top scorer\"\n",
    "\n",
    "<img src=\"semi-pro.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lots of machine learning and stats libraries\n",
    "\n",
    "* SciPy: comprehensive library of numerical routines like optimizers, integrators, FFT.\n",
    "* scikit-learn: **The** ML library out there\n",
    "* statsmodels: Frequentist statistics\n",
    "* SymPy: Symbolic Math\n",
    "* PyMC3: Probabilistic programming in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## scikit-learn\n",
    "\n",
    "Taken from http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "X = [[0, 0], [1, 1]]\n",
    "y = [0, 1]\n",
    "clf = svm.SVC()\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[0, .5]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Advanced example: Grid Search with Cross-Validation to find hyper parameters\n",
    "\n",
    "Taken from http://scikit-learn.org/stable/auto_examples/grid_search_digits.html and http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAADFCAYAAADt05aKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niccSkHgsAYnHEpB4LAGJ9xfRvcT6cis2zwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de274f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#Display the first digit\n",
    "plt.figure(1, figsize=(3, 3))\n",
    "plt.imshow(digits.images[-1], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "plt.grid('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "n_samples = len(digits.images)\n",
    "X = digits.images.reshape((n_samples, -1))\n",
    "y = digits.target\n",
    "\n",
    "# Split the dataset in two equal parts\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.5, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, loss_func=None, n_jobs=1,\n",
       "       param_grid=[{'C': [1, 10, 100, 1000], 'kernel': ['rbf'], 'gamma': [0.001, 0.0001]}, {'C': [1, 10, 100, 1000], 'kernel': ['linear']}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n",
       "       verbose=0)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the parameters by cross-validation\n",
    "tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n",
    "                     'C': [1, 10, 100, 1000]},\n",
    "                    {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n",
    "\n",
    "clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5)\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "print(clf.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VFxfLsiw9/PDD6t69u7F6JOnw4cMaNmyYXn755SbZTHA2hg4dGrgX7cc//rEW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\ncAIAYMP/AU2DXRBjGKJuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7de2d6ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_true, y_pred = y_test, clf.predict(X_test)\n",
    "ax = sns.heatmap(confusion_matrix(y_true, y_pred))\n",
    "ax.set(xlabel='true label', ylabel='predicted label');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Level 3: \"Lord of Speed\"\n",
    "\n",
    "<img src=\"pro.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Python is slow!\n",
    "\n",
    "* The interpreted language is indeed quite slow (just like matlab and R are slow)\n",
    "* Vectorize computations (i.e. the matlab way): leads to unreadable code.\n",
    "\n",
    "## Great tools to generate C-code\n",
    "\n",
    "* Cython: Write Python-like syntax that can be translated to fast C-code and called from Python.\n",
    "* Numba: Directly write Python and auto-translate to LLVM.\n",
    "* Theano: Write numerical expressions in a NumPy-like syntax to build up compute-graph that can be compiled.\n",
    "* PyCUDA: GPU programming."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Comparing Python, Cython and Numba\n",
    "\n",
    "Taken from https://jakevdp.github.io/blog/2013/06/15/numba-vs-cython-take-2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.random.random((1000, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 loops, best of 3: 6.34 s per loop\n"
     ]
    }
   ],
   "source": [
    "def pairwise_python(X):\n",
    "    M = X.shape[0]\n",
    "    N = X.shape[1]\n",
    "    D = np.empty((M, M), dtype=np.float)\n",
    "    for i in range(M):\n",
    "        for j in range(M):\n",
    "            d = 0.0\n",
    "            for k in range(N):\n",
    "                tmp = X[i, k] - X[j, k]\n",
    "                d += tmp * tmp\n",
    "            D[i, j] = np.sqrt(d)\n",
    "    return D\n",
    "%timeit pairwise_python(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "import numpy as np\n",
    "cimport cython\n",
    "from libc.math cimport sqrt\n",
    "\n",
    "@cython.boundscheck(False)\n",
    "@cython.wraparound(False)\n",
    "def pairwise_cython(double[:, ::1] X):\n",
    "    cdef int M = X.shape[0]\n",
    "    cdef int N = X.shape[1]\n",
    "    cdef double tmp, d\n",
    "    cdef double[:, ::1] D = np.empty((M, M), dtype=np.float64)\n",
    "    for i in range(M):\n",
    "        for j in range(M):\n",
    "            d = 0.0\n",
    "            for k in range(N):\n",
    "                tmp = X[i, k] - X[j, k]\n",
    "                d += tmp * tmp\n",
    "            D[i, j] = sqrt(d)\n",
    "    return np.asarray(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 7.11 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit pairwise_cython(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Numba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 7.31 ms per loop\n"
     ]
    }
   ],
   "source": [
    "from numba.decorators import jit\n",
    "\n",
    "pairwise_numba = jit(pairwise_python)\n",
    "\n",
    "# Run once to compile before timing\n",
    "pairwise_numba(X)\n",
    "\n",
    "%timeit pairwise_numba(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Level 4: \"High Priest of Big Data\"\n",
    "\n",
    "<img src=\"master.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Lots of things happening!\n",
    "\n",
    "## Big Data\n",
    "* [Blaze](http://blaze.pydata.org/en/latest/) + [Dask](http://dask.pydata.org/en/latest/)\n",
    "* [Ibis](https://github.com/cloudera/ibis)\n",
    "* [PySpark](https://spark.apache.org/docs/0.9.0/python-programming-guide.html)\n",
    "* [bcolz](https://github.com/Blosc/bcolz)\n",
    "\n",
    "## Interactive data visualization\n",
    "* [Bokeh](http://bokeh.pydata.org/en/latest/)\n",
    "* [Plotly](https://plot.ly)\n",
    "* [pyxley](https://github.com/stitchfix/pyxley)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Work interactively on Big Data with Dask\n",
    "\n",
    "Taken from https://jakevdp.github.io/blog/2015/08/14/out-of-core-dataframes-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 wiecki wiecki 774M Sep  8 14:43 POIWorld.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls -lahL POIWorld.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dd.DataFrame<read-csv-POIWorld.csv-74f130b10caf7d8ca68886970e302ba2, divisions=(None, None, None, ..., None, None)>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import dataframe as dd\n",
    "columns = [\"name\", \"amenity\", \"Longitude\", \"Latitude\"]\n",
    "data = dd.read_csv('POIWorld.csv', usecols=columns)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "with_name = data[data.name.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "is_starbucks = with_name.name.str.contains('[Ss]tarbucks')\n",
    "is_dunkin = with_name.name.str.contains('[Dd]unkin')\n",
    "\n",
    "starbucks = with_name[is_starbucks]\n",
    "dunkin = with_name[is_dunkin]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from dask.diagnostics import ProgressBar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[########################################] | 100% Completed |  1min  5.8s\n"
     ]
    }
   ],
   "source": [
    "with ProgressBar():\n",
    "    starbucks_count, dunkin_count = dd.compute(starbucks.name.count(), dunkin.name.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5416, 1320)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starbucks_count, dunkin_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "locs = dd.compute(starbucks.Longitude,\n",
    "                  starbucks.Latitude,\n",
    "                  dunkin.Longitude,\n",
    "                  dunkin.Latitude)\n",
    "\n",
    "# extract arrays of values fro the series:\n",
    "lon_s, lat_s, lon_d, lat_d = [loc.values for loc in locs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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nw09R++Kj0WgEJ/hwOAxBEAKBGYvFUC6Xz7ymQUTTNOJERegJoVAI+XweqqpC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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7a9b15a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "\n",
    "def draw_USA():\n",
    "    \"\"\"initialize a basemap centered on the continental USA\"\"\"\n",
    "    plt.figure(figsize=(14, 10))\n",
    "    return Basemap(projection='lcc', resolution='l',\n",
    "                   llcrnrlon=-119, urcrnrlon=-64,\n",
    "                   llcrnrlat=22, urcrnrlat=49,\n",
    "                   lat_1=33, lat_2=45, lon_0=-95,\n",
    "                   area_thresh=10000)\n",
    "\n",
    "m = draw_USA()\n",
    "    \n",
    "# Draw map background\n",
    "m.fillcontinents(color='white', lake_color='#eeeeee')\n",
    "m.drawstates(color='lightgray')\n",
    "m.drawcoastlines(color='lightgray')\n",
    "m.drawcountries(color='lightgray')\n",
    "m.drawmapboundary(fill_color='#eeeeee')\n",
    "\n",
    "# Plot the values in Starbucks Green and Dunkin Donuts Orange\n",
    "style = dict(s=5, marker='o', alpha=0.5, zorder=2)\n",
    "m.scatter(lon_s, lat_s, latlon=True,\n",
    "          label=\"Starbucks\", color='#00592D', **style)\n",
    "m.scatter(lon_d, lat_d, latlon=True,\n",
    "          label=\"Dunkin' Donuts\", color='#FC772A', **style)\n",
    "plt.legend(loc='lower left', frameon=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Interactive data visualization with Bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "    \n",
       "        <link rel=\"stylesheet\" href=\"http://cdn.pydata.org/bokeh/release/bokeh-0.9.3.min.css\" type=\"text/css\" />\n",
       "        <script type=\"text/javascript\" src=\"http://cdn.pydata.org/bokeh/release/bokeh-0.9.3.min.js\"></script>\n",
       "        <script type=\"text/javascript\">\n",
       "            Bokeh.set_log_level(\"info\");\n",
       "        </script>\n",
       "    <div>\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span>BokehJS successfully loaded.</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.io import output_notebook\n",
    "from bokeh.resources import CDN\n",
    "from bokeh.plotting import figure, show\n",
    "output_notebook(resources=CDN)\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from math import pi\n",
    "\n",
    "from bokeh.browserlib import view\n",
    "from bokeh.document import Document\n",
    "from bokeh.embed import file_html\n",
    "from bokeh.models.glyphs import Circle, Text\n",
    "from bokeh.models import (\n",
    "    BasicTicker, ColumnDataSource, Grid, GridPlot, LinearAxis,\n",
    "    DataRange1d, PanTool, Plot, WheelZoomTool\n",
    ")\n",
    "from bokeh.resources import INLINE\n",
    "from bokeh.sampledata.iris import flowers\n",
    "from bokeh.plotting import show\n",
    "\n",
    "colormap = {'setosa': 'red', 'versicolor': 'green', 'virginica': 'blue'}\n",
    "\n",
    "flowers['color'] = flowers['species'].map(lambda x: colormap[x])\n",
    "\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data=dict(\n",
    "        petal_length=flowers['petal_length'],\n",
    "        petal_width=flowers['petal_width'],\n",
    "        sepal_length=flowers['sepal_length'],\n",
    "        sepal_width=flowers['sepal_width'],\n",
    "        color=flowers['color']\n",
    "    )\n",
    ")\n",
    "\n",
    "text_source = ColumnDataSource(\n",
    "    data=dict(xcenter=[125], ycenter=[135])\n",
    ")\n",
    "\n",
    "xdr = DataRange1d()\n",
    "ydr = DataRange1d()\n",
    "\n",
    "def make_plot(xname, yname, xax=False, yax=False, text=None):\n",
    "    plot = Plot(\n",
    "        x_range=xdr, y_range=ydr, background_fill=\"#efe8e2\",\n",
    "        border_fill='white', title=\"\", min_border=2, h_symmetry=False, v_symmetry=False,\n",
    "        plot_width=150, plot_height=150)\n",
    "\n",
    "    circle = Circle(x=xname, y=yname, fill_color=\"color\", fill_alpha=0.2, size=4, line_color=\"color\")\n",
    "    r = plot.add_glyph(source, circle)\n",
    "\n",
    "    xdr.renderers.append(r)\n",
    "    ydr.renderers.append(r)\n",
    "\n",
    "    xticker = BasicTicker()\n",
    "    if xax:\n",
    "        xaxis = LinearAxis()\n",
    "        plot.add_layout(xaxis, 'below')\n",
    "        xticker = xaxis.ticker\n",
    "    plot.add_layout(Grid(dimension=0, ticker=xticker))\n",
    "\n",
    "    yticker = BasicTicker()\n",
    "    if yax:\n",
    "        yaxis = LinearAxis()\n",
    "        plot.add_layout(yaxis, 'left')\n",
    "        yticker = yaxis.ticker\n",
    "    plot.add_layout(Grid(dimension=1, ticker=yticker))\n",
    "\n",
    "    plot.add_tools(PanTool(), WheelZoomTool())\n",
    "\n",
    "    if text:\n",
    "        text = \" \".join(text.split('_'))\n",
    "        text = Text(\n",
    "            x={'field':'xcenter', 'units':'screen'},\n",
    "            y={'field':'ycenter', 'units':'screen'},\n",
    "            text=[text], angle=pi/4, text_font_style=\"bold\", text_baseline=\"top\",\n",
    "            text_color=\"#ffaaaa\", text_alpha=0.7, text_align=\"center\", text_font_size=\"28pt\"\n",
    "        )\n",
    "        plot.add_glyph(text_source, text)\n",
    "\n",
    "    return plot\n",
    "\n",
    "xattrs = [\"petal_length\", \"petal_width\", \"sepal_width\", \"sepal_length\"]\n",
    "yattrs = list(reversed(xattrs))\n",
    "plots = []\n",
    "\n",
    "for y in yattrs:\n",
    "    row = []\n",
    "    for x in xattrs:\n",
    "        xax = (y == yattrs[-1])\n",
    "        yax = (x == xattrs[0])\n",
    "        text = x if (x==y) else None\n",
    "        plot = make_plot(x, y, xax, yax, text)\n",
    "        row.append(plot)\n",
    "    plots.append(row)\n",
    "\n",
    "grid = GridPlot(children=plots, title=\"iris_splom\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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\"above\": [], \"extra_y_ranges\": {}, \"title\": \"iris_splom\", \"id\": \"55a4aa2f-2886-4469-aea8-8aa337f14e3b\", \"tool_events\": {\"id\": \"31714cdc-2882-40df-ba1b-58f75734f44b\", \"type\": \"ToolEvents\"}, \"tools\": [], \"right\": []}, \"type\": \"GridPlot\"}];\n",
       "        Bokeh.load_models(all_models);\n",
       "        var plots = [{'modeltype': 'GridPlot', 'elementid': '660e58f8-d16e-47cb-953f-ffbd0173778c', 'modelid': '55a4aa2f-2886-4469-aea8-8aa337f14e3b'}];\n",
       "        for (idx in plots) {\n",
       "        \tvar plot = plots[idx];\n",
       "        \tvar model = Bokeh.Collections(plot.modeltype).get(plot.modelid);\n",
       "        \tBokeh.logger.info('Realizing plot:')\n",
       "        \tBokeh.logger.info(' - modeltype: ' + plot.modeltype);\n",
       "        \tBokeh.logger.info(' - modelid: ' + plot.modelid);\n",
       "        \tBokeh.logger.info(' - elementid: ' + plot.elementid);\n",
       "        \tvar view = new model.default_view({\n",
       "        \t\tmodel: model,\n",
       "        \t\tel: '#' + plot.elementid\n",
       "        \t});\n",
       "        \tBokeh.index[plot.modelid] = view;\n",
       "        }\n",
       "    });\n",
       "    </script>\n",
       "<div class=\"plotdiv\" id=\"660e58f8-d16e-47cb-953f-ffbd0173778c\"></div>\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(grid)"
   ]
  },
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   "metadata": {
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   "source": [
    "# Staying up-to-date\n",
    "* Get on [Twitter](https://twitter.com/twiecki)\n",
    "* Frequent [HackerNews](https://news.ycombinator.com)\n",
    "* Frequent [DataTau](https://datatau.com)\n",
    "* Visit [PyData conferences](http://pydata.org/) and the [SciPy conference](http://conference.scipy.org/)\n"
   ]
  }
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