6-Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=images/ucsc_banner.png width=500>\n",
    "# Visualization\n",
    "\n",
    "Visualization is one of the best parts of data science. Visualizing data isn't just for aesthetics, it can profoundly change the way a person understands the data they're working with, in a way that is much harder to achieve by just working with numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>body {\n",
       "    margin: 0;\n",
       "    font-family: Helvetica;\n",
       "}\n",
       "table.dataframe {\n",
       "    border-collapse: collapse;\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe tr {\n",
       "    border: none;\n",
       "}\n",
       "table.dataframe td, table.dataframe th {\n",
       "    margin: 0;\n",
       "    border: 1px solid white;\n",
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       "    padding-right: 0.25em;\n",
       "}\n",
       "table.dataframe th:not(:empty) {\n",
       "    background-color: #fec;\n",
       "    text-align: left;\n",
       "    font-weight: normal;\n",
       "}\n",
       "table.dataframe tr:nth-child(2) th:empty {\n",
       "    border-left: none;\n",
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       "}\n",
       "table.dataframe td {\n",
       "    border: 2px solid #ccf;\n",
       "    background-color: #f4f4ff;\n",
       "}\n",
       "h3 {\n",
       "    color: white;\n",
       "    background-color: black;\n",
       "    padding: 0.5em;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "css = open('style-table.css').read() + open('style-notebook.css').read()\n",
    "HTML('<style>{}</style>'.format(css))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have a personal preference for [Seaborn](https://stanford.edu/~mwaskom/software/seaborn/), a statistical modeling package from Stanford for Python that is designed around working with dataframes.\n",
    "\n",
    "In the previous module, you computed some statistics from the *E. coli* data.  Now, we'll go ahead and create visual representations of those answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### What is the distribution of genome sizes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/ecoli_cit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>generation</th>\n",
       "      <th>clade</th>\n",
       "      <th>strain</th>\n",
       "      <th>cit</th>\n",
       "      <th>run</th>\n",
       "      <th>genome_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>REL606</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>REL606</td>\n",
       "      <td>unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>REL1166A</td>\n",
       "      <td>2000</td>\n",
       "      <td>unknown</td>\n",
       "      <td>REL606</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SRR098028</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZDB409</td>\n",
       "      <td>5000</td>\n",
       "      <td>unknown</td>\n",
       "      <td>REL606</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SRR098281</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZDB429</td>\n",
       "      <td>10000</td>\n",
       "      <td>UC</td>\n",
       "      <td>REL606</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SRR098282</td>\n",
       "      <td>4.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZDB446</td>\n",
       "      <td>15000</td>\n",
       "      <td>UC</td>\n",
       "      <td>REL606</td>\n",
       "      <td>unknown</td>\n",
       "      <td>SRR098283</td>\n",
       "      <td>4.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sample  generation    clade  strain      cit        run  genome_size\n",
       "0    REL606           0      NaN  REL606  unknown        NaN         4.62\n",
       "1  REL1166A        2000  unknown  REL606  unknown  SRR098028         4.63\n",
       "2    ZDB409        5000  unknown  REL606  unknown  SRR098281         4.60\n",
       "3    ZDB429       10000       UC  REL606  unknown  SRR098282         4.59\n",
       "4    ZDB446       15000       UC  REL606  unknown  SRR098283         4.66"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with just a basic scatter plot of the the genome_sizes by sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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6bwiSpCHrZw5/N7AHeHjR9lcCjwLvj4jvAKdlZgIXAbcBZOa9wPmVpW2wubk5pqYOMjV1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c306590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [sample for sample in xrange(len(df))] # this gets us an array of numbers from 0 to 29 for the x-axis\n",
    "plt.scatter(x, df.genome_size);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see there's some separation to the data, let's bin these genome sizes into a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAECCAYAAAAFL5eMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIdSZm80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c6c7bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the semi-colon at the end hides some unnecessary matplotlib output\n",
    "plt.hist(df.genome_size); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Is there a relationship between genome size and Cit status?\n",
    "We can use Seaborn to look at these relationships by taking advantage that the data is in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hMEiSKqbNcNWhoSEamzd03XDKbtPY/BpDQ+2/aPKKKy5j3bqX277fOg0PF/exuvjiD3a4\nktYtXPgGbrzxU50uQx02bYJB3W3Dhg3FB+3rXqs5dQ03uuRGh43i7yxNm2Do7e1l49YeL3Cr2fqn\nv0Rv77y277e3t5dNMzbTd+r+bd+3CoP3P0fvXK/Sln0MkqRRDAZJUoXBIEmqMBgkSRUGgySpwmCQ\nJFVMm+Gq6n7Dr21h8P7nOl1Gy4Y3bQVgxpyZHa6kNcOvbYHuuY+VamQwqCt02+1NAQY3lLdmnbtH\nhytp0dzu/Dur/aZVMDQ2v9ZVU2I0tm4CoGfmnA5X0rri1p7tv8CtW+4h3cw7uKlbTZtg6MZvQoOD\nxfQEfQvb/0Fbn3ld+beWtM20CQa/cUpSaxyVJEmqqL3FEBF7A48DJ2Xmmqbli4EbKObL/BFwPrAF\n+GPgMGAjcHFmPlN3jZKkbWptMUTEbOBWYGjU8h7gNuCCzHw78BBwIHAmsFtmHgdcRREckqRJVPep\npOXALcDzo5YfArwEXBERDwN7ZGYCxwP3AWTmY8DRNdcnSRqltlNJEXEBMJCZD0bE1VRvsbIIOA64\nDHgG+EpE/C9gIbCuabutETEjM7vkTifqNitW3MPq1Y/Vsu/BweI6hpFBBO20ePESli49t+37laDe\nPoZlQCMiTgYOB+6OiNMz8wWK1sLTZSuBiLifonWwDljQtI9xQ6Gvbx6zZnXHlaUTNXNm0aDr718w\nzpbaUXPnzvn3v3O77b777gC17H/u3Dn+f6Ha1BYMmXnCyOOIWAlcUoYCwLPA/Ig4qOxcfjtwB0Xr\n4TTgCxFxDPCd8Y4zOPhq22ufKrZuLTJxYOCVDley6zrttLM57bSzO13GDvH/C+2M7X2xmMzrGHoi\n4hxgfmbeHhEXAX9RdkSvysz7ysenRMSq8neWTWJ9kiQmKRgy88SRh03LVgJLRm3XAC6djJokSWPz\nAjdJUoXBINXkySef4Mknn+h0GdKETZu5kqTJ9jd/cy8Ahx761g5XIk2MLQapBk8++QSZ3yfz+7Ya\n1HVsMbRBXRdJeYFU9xppLYw8ttWgbmIwTGFz5uzW6RIkTUM9jUaj0zXslIGBV7r7BWiX9OSTT3D9\n9b8LwG/+5m/ZYtCU09+/oOf11tlikGpw6KFvJeI//PtjqZsYDFJNzjjjrE6XIO0QTyVJ0jS0vVNJ\nDleVJFUYDJKkCoNBklRhMEiSKgwGSVKFwSBJqjAYJEkVBoMkqcJgkCRVGAySpAqDQZJUYTBIkioM\nBklShcEgSaowGCRJFQaDJKnCYJAkVRgMkqQKg0GSVGEwSJIqDAZJUoXBIEmqMBgkSRUGgySpwmCQ\nJFUYDJKkill1HyAi9gYeB07KzDVNyz8KXAQMlIt+JTOfiohvAS+Xy57NzIvqrlGStE2twRARs4Fb\ngaExVh8JnJeZ327afneAzDyxzrokSa+v7lNJy4FbgOfHWHcUcE1EfCMiriqXvQ2YFxEPRMRDEbGk\n5vokSaPUFgwRcQEwkJkPlot6Rm3yWeAS4J3AL0bEuylaFssz813Ah4F7IsJ+EEmaRHV+6C4DTomI\nlcDhwN1lf8OImzJzbWZuBr4KHAGsAe4ByMyngJeAfWusUZI0Sk+j0aj9IGU4XDLS+RwRbwC+A7wV\neBVYAdwJ/AxwWGZeFhH7AQ8BP5eZw7UXKUkCJmFUUpOeiDgHmJ+Zt5f9CiuBjcDXMvP+iJgFfDoi\nHil/Z5mhIEmTa1JaDJKk7mHHriSpwmCQJFUYDJKkCoNBklRhMHRQRBwQEd/sdB1qj4i4t9M1aOdE\nxA8jYk6n6+g0g0Fqk8w8q9M1aKc5TJPJvY5h2iinA4nMvLqcGPBJ4AfAPwM/DywEzm7afgZwN/Bd\n4PPA54DngIOAf8rMX42IPYDPAAso3rffKh+fkpkfKa8LOTYzz4iIc4H9gUMorhM5gOIK8guaJy3U\nxJTv62nA7hR/z5uAMyje0yuBP8nMfSPiYeDbVN/rGcBnM/PYcl+PAu8H3gzcAGyiuNjzfZm5fvJe\n1fRVvp+nAovKn/9eruqJiD+leL8eiIhTgfdn5rKI+DTFv8u5FLM3fGbyK6+fLYZ6jP7WMfL8scw8\nBfg74Jxy+WyKaUBWZeb1FHNKHQxcCPxH4L9ExBspguCBzDyB4oPmTuBB4B3lvt8B7BcRMyk+vP6q\nXP7DzDwV+CPgV9r9Qqeh3sx8N/D7wKWZ+UsUf9dlbHufG4z9XjcbeX4GxReBEygmnOyrt3w1aQAz\nMvNkioD4JDCzaV3z+0lEzAfeDry33H7rpFY7iQyG+vWUPw2Kb5EA/5fiWyfAYUA/xbf/EU9n5lB5\n1ffz5baHAo8AZOa/Ausovo2uiYijKb5xPkrxAbN/Zma5r5Fj/r+mY2rHNChafVDcM+T75eN/46f/\ntmO9181G/p+4DngTxfQv7wM2t7Feje8hgMz8McX7uGiMbWaU26wHfgO4naJlv9sk1TjpDIZ6bGDb\n5H9HUnwAjJ5ddsTjwHuA8yLiF8plY53n/D5l6yAi3kTxzfJF4IvAHwB/T9GCuI7iW6rq0eo56JHt\nRt73DcDeETGjPC14YLnug8CfZuY7gSewVTfZFgOUrfJ5bLtx2AZgv/LxkeU2+wBHla3E9wDX76qz\nP++SL2oKuB84ICK+QXHaZx3VpilNjxuZuQG4FPgzYA5jn3a4DnhnRHydIgw+VLYovgocQxEKD1PM\nUvtXo3535L92rO28sf6e2/vbNije459QBPZq4DbgqXLdPwF3RMTXgP9E0dekyXNw+bf/EsW/wa0U\n78sdwEcj4u8oAqJRtir2iYhVFP/elu+qc7k5V5KkaSkifhlYlJk3dLqWqcYWg6TpzG/GY7DFIEmq\nsMUgSaowGCRJFQaDJKnCYJAkVThXklSTiLidYpqLp4G7M/O9HS5JaomjkqSaRcQBwMrMPLDTtUit\nMBikNomI3wfOBLZQXN18JnAt8DHgXcBXnJpb3cBTSVIbRMTZwHEUU23PAf6BYpK1BvAR4GFDQd3C\nYJDa4x3A5zNzM8UMqUdExMpy3etNoChNSY5KktpjM00BUPYr9HasGmkn2GKQ2uMR4Ncj4haKU0kP\nAPPLdVvw35q6iC0GqQ0y86+BVcC3KKbSvhEYuVnSj4HnIuKhDpUnTYijkiRJFbYYJEkVBoMkqcJg\nkCRVGAySpAqDQZJUYTBIkioMBklShcEgSar4/2Q4me7YncZDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10deb8cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='cit', y='genome_size', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of boxplots, we can visualize the *densities* of the cit plus and minus distributions using our Pandas skills"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X8Gh2m8Hr1rcQjSXY/Hy32eWUJAn4tPbxQ3gdHgLO8tpUbC6e9Jm4sl1B+QlP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\nQghhURLwQghhUf8fx8o9J7UoMlMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c306850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df[df.cit == 'plus'].genome_size, shade=True, label='plus')\n",
    "sns.kdeplot(df[df.cit == 'minus'].genome_size, shade=True, label='minus');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Is there a relationship between genome size and generation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dq6jRH5KohBBiGqLxFOd6ogzGkqAUd5vvfNxOOxtbGwn1xzlyZqDU4eSNJCoh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\nSKYpUYmISKYpUYmISKb9P3uA5WsGKnzTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a88ee50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='generation', y='genome_size', data=df, kind='reg');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Pearson correlation of 0.4 isn't very strong, and we can see from the data that the disparity in the genome size happens between generations 30,000 and 40,000.  Based on the results of the above boxplots, we know that the genome size is mostly related to Cit status."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Dimensions\n",
    "\n",
    "All of these above plots show 1 or 2 dimensions worth of data.  How would you visualize 3 dimensions? 3-dimensional plots are generally not used as ultimately they're projected onto a 2-dimensional surface which obfuscates the data its trying to represent.\n",
    "\n",
    "*color*, *size*, and *shape* are just a few ways we can visualize additional dimesions of data without needing a 3-dimensional plot.\n",
    "\n",
    "This additional dimension is usually for *categorical* and not *continuous* data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h6PH0wcPs3jvK60eW9WlrzXDDtk3cuL2frgj7tJU85682y9ieCmZltSzWeBuA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\nWRI2xpgI/f+ym1fjbAhOlwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad86250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='generation', y='genome_size', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us a regression of generation vs. genome_size.  But how would we visualize this data with regards to the samples Cit status?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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unmGC1KeAG+NuiLhcvPf4gwN89UgQSgKTPzg42wEzmTgAJYEo/noVXVlFFZdv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\nkaRCCCFUViSpEEIIlRVJKoQQQmX9B3EcFYYC0sy2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dfd1890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='generation', y='genome_size', data=df, hue='cit');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get 3 regression lines, one showing that *unknown* and *minus* are almost identical and that cit+ is distinct. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tips Dataset\n",
    "**Tips** is a built in dataset from seaborn, what are some interesting relationships you can discover through visualization?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tips = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "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": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Have fun! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NIH BD2K Center for Big Data in Translational Genomics, UCSC Genomics Institute"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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