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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Save the PDF Locally\n",
      "[Japanese Qualifying](http://www.fia.com/sites/default/files/championship/event_report/documents/2014_15_JPN_F1_Q0_Timing_QualifyingSessionLapTimes_V01.pdf)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Perform Imports and Add Our Development Directory to the Python Path"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import sys\n",
      "import os\n",
      "import seaborn as sns\n",
      "%matplotlib inline\n",
      "\n",
      "# handle sphinx building docs from higher level directory\n",
      "path = os.path.realpath(os.path.dirname('__file__'))\n",
      "if 'notebooks' not in path:\n",
      "    rel_path = ''\n",
      "else:\n",
      "    rel_path = '../../'\n",
      "\n",
      "# get access to formulapy module\n",
      "sys.path.append(os.path.realpath(rel_path))  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Import the FIA PDF Parser"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from formulapy.data.fia.parsers import parse_laptimes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Call the Parser on our PDF File\n",
      "This demonstrates the primary data format that this project uses, which is [Pandas Dataframe](http://pandas.pydata.org/). This is a tabular format that works much like a in memory database. This can be more intuitive, compared to nested structures that are often used with Matlab.\n",
      "\n",
      "This example looks complicated due to having to work with different paths when building the docs, but when using it locally, you would just perform:\n",
      "\n",
      "```parse_laptimes('\\full\\path\\to\\thepdf.pdf')```\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = parse_laptimes(os.path.realpath(rel_path + 'data/fia_qualifying.pdf'))\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>driver_no</th>\n",
        "      <th>name</th>\n",
        "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  846.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   94.784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   95.517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  111.270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  112.372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  559.995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   95.726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   94.766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  109.571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  107.159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  977.535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  185.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   95.104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   94.432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  108.724</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  117.619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  348.486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  846.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   94.466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   95.613</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  122.818</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  119.784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  546.494</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   95.593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   94.503</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  107.671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  106.924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  987.803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  189.837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   95.180</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>258</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>259</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>260</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   94.301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>261</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  113.357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>262</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  113.219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>263</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  487.053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>264</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  103.937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>265</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>266</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td> 1188.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>267</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  110.096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>268</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>269</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  293.273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>270</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  115.373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>271</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>272</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  398.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>273</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  126.088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>274</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>275</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  123.845</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>276</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>   96.338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>277</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  397.805</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>278</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  116.811</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>279</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>   96.656</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>280</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>   96.653</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>281</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  113.326</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>282</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  113.668</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>283</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  460.364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>284</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  451.681</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>285</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>   95.736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>286</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>   95.364</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>287</th>\n",
        "      <td> 99</td>\n",
        "      <td>     A.SUTIL</td>\n",
        "      <td>  116.468</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>288 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "     driver_no         name      time\n",
        "0            1     S.VETTEL   846.000\n",
        "1            1     S.VETTEL    94.784\n",
        "2            1     S.VETTEL    95.517\n",
        "3            1     S.VETTEL   111.270\n",
        "4            1     S.VETTEL   112.372\n",
        "5            1     S.VETTEL   559.995\n",
        "6            1     S.VETTEL    95.726\n",
        "7            1     S.VETTEL    94.766\n",
        "8            1     S.VETTEL   109.571\n",
        "9            1     S.VETTEL   107.159\n",
        "10           1     S.VETTEL   977.535\n",
        "11           1     S.VETTEL   185.605\n",
        "12           1     S.VETTEL    95.104\n",
        "13           1     S.VETTEL    94.432\n",
        "14           1     S.VETTEL   108.724\n",
        "15           1     S.VETTEL   117.619\n",
        "16           1     S.VETTEL   348.486\n",
        "17           3  D.RICCIARDO   846.000\n",
        "18           3  D.RICCIARDO    94.466\n",
        "19           3  D.RICCIARDO    95.613\n",
        "20           3  D.RICCIARDO   122.818\n",
        "21           3  D.RICCIARDO   119.784\n",
        "22           3  D.RICCIARDO   546.494\n",
        "23           3  D.RICCIARDO    95.593\n",
        "24           3  D.RICCIARDO    94.503\n",
        "25           3  D.RICCIARDO   107.671\n",
        "26           3  D.RICCIARDO   106.924\n",
        "27           3  D.RICCIARDO   987.803\n",
        "28           3  D.RICCIARDO   189.837\n",
        "29           3  D.RICCIARDO    95.180\n",
        "..         ...          ...       ...\n",
        "258         77     V.BOTTAS   843.000\n",
        "259         77     V.BOTTAS    93.443\n",
        "260         77     V.BOTTAS    94.301\n",
        "261         77     V.BOTTAS   113.357\n",
        "262         77     V.BOTTAS   113.219\n",
        "263         77     V.BOTTAS   487.053\n",
        "264         77     V.BOTTAS   103.937\n",
        "265         77     V.BOTTAS    93.329\n",
        "266         77     V.BOTTAS  1188.277\n",
        "267         77     V.BOTTAS   110.096\n",
        "268         77     V.BOTTAS    93.801\n",
        "269         77     V.BOTTAS   293.273\n",
        "270         77     V.BOTTAS   115.373\n",
        "271         77     V.BOTTAS    93.128\n",
        "272         77     V.BOTTAS   398.983\n",
        "273         77     V.BOTTAS   126.088\n",
        "274         99      A.SUTIL   842.000\n",
        "275         99      A.SUTIL   123.845\n",
        "276         99      A.SUTIL    96.338\n",
        "277         99      A.SUTIL   397.805\n",
        "278         99      A.SUTIL   116.811\n",
        "279         99      A.SUTIL    96.656\n",
        "280         99      A.SUTIL    96.653\n",
        "281         99      A.SUTIL   113.326\n",
        "282         99      A.SUTIL   113.668\n",
        "283         99      A.SUTIL   460.364\n",
        "284         99      A.SUTIL   451.681\n",
        "285         99      A.SUTIL    95.736\n",
        "286         99      A.SUTIL    95.364\n",
        "287         99      A.SUTIL   116.468\n",
        "\n",
        "[288 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "## Example of Using DataFrames on Parsed Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Select a Driver by Number"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.driver_no == 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>driver_no</th>\n",
        "      <th>name</th>\n",
        "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 846.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  94.784</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  95.517</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 111.270</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 112.372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 559.995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  95.726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  94.766</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 109.571</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 107.159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 977.535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 185.605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  95.104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td>  94.432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 108.724</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 117.619</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 1</td>\n",
        "      <td> S.VETTEL</td>\n",
        "      <td> 348.486</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "    driver_no      name     time\n",
        "0           1  S.VETTEL  846.000\n",
        "1           1  S.VETTEL   94.784\n",
        "2           1  S.VETTEL   95.517\n",
        "3           1  S.VETTEL  111.270\n",
        "4           1  S.VETTEL  112.372\n",
        "5           1  S.VETTEL  559.995\n",
        "6           1  S.VETTEL   95.726\n",
        "7           1  S.VETTEL   94.766\n",
        "8           1  S.VETTEL  109.571\n",
        "9           1  S.VETTEL  107.159\n",
        "10          1  S.VETTEL  977.535\n",
        "11          1  S.VETTEL  185.605\n",
        "12          1  S.VETTEL   95.104\n",
        "13          1  S.VETTEL   94.432\n",
        "14          1  S.VETTEL  108.724\n",
        "15          1  S.VETTEL  117.619\n",
        "16          1  S.VETTEL  348.486"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Or, Select by Name"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.name == 'V.BOTTAS']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>driver_no</th>\n",
        "      <th>name</th>\n",
        "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>258</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>259</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>   93.443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>260</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>   94.301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>261</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  113.357</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>262</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  113.219</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>263</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  487.053</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>264</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  103.937</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>265</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>   93.329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>266</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td> 1188.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>267</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  110.096</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>268</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>   93.801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>269</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  293.273</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>270</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  115.373</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>271</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>   93.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>272</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  398.983</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>273</th>\n",
        "      <td> 77</td>\n",
        "      <td> V.BOTTAS</td>\n",
        "      <td>  126.088</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "     driver_no      name      time\n",
        "258         77  V.BOTTAS   843.000\n",
        "259         77  V.BOTTAS    93.443\n",
        "260         77  V.BOTTAS    94.301\n",
        "261         77  V.BOTTAS   113.357\n",
        "262         77  V.BOTTAS   113.219\n",
        "263         77  V.BOTTAS   487.053\n",
        "264         77  V.BOTTAS   103.937\n",
        "265         77  V.BOTTAS    93.329\n",
        "266         77  V.BOTTAS  1188.277\n",
        "267         77  V.BOTTAS   110.096\n",
        "268         77  V.BOTTAS    93.801\n",
        "269         77  V.BOTTAS   293.273\n",
        "270         77  V.BOTTAS   115.373\n",
        "271         77  V.BOTTAS    93.128\n",
        "272         77  V.BOTTAS   398.983\n",
        "273         77  V.BOTTAS   126.088"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Select the Lowest Time"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.time == min(df.time)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>driver_no</th>\n",
        "      <th>name</th>\n",
        "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> 6</td>\n",
        "      <td> N.ROSBERG</td>\n",
        "      <td> 92.506</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "    driver_no       name    time\n",
        "51          6  N.ROSBERG  92.506"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Sort Rows by a Given Column"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort(columns='time')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>driver_no</th>\n",
        "      <th>name</th>\n",
        "      <th>time</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>51 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td>   92.506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td>   92.629</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>254</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td>   92.703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>248</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td>   92.946</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td>   92.950</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>255</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td>   92.982</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>271</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>265</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.329</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>259</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>149</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>   93.527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>155</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>   93.527</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>143</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>   93.551</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>247</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td>   93.611</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td>   93.671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>119</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>   93.675</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>132</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>   93.740</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>268</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   93.801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>125</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>   93.858</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>128</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>   94.005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>152</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>   94.059</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   94.075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>159</th>\n",
        "      <td> 20</td>\n",
        "      <td> K.MAGNUSSEN</td>\n",
        "      <td>   94.229</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>171</th>\n",
        "      <td> 20</td>\n",
        "      <td> K.MAGNUSSEN</td>\n",
        "      <td>   94.242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>260</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>   94.301</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>201</th>\n",
        "      <td> 22</td>\n",
        "      <td>    J.BUTTON</td>\n",
        "      <td>   94.317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>   94.432</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>165</th>\n",
        "      <td> 20</td>\n",
        "      <td> K.MAGNUSSEN</td>\n",
        "      <td>   94.437</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>   94.466</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>144</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>   94.483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>120</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>   94.497</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>134</th>\n",
        "      <td> 17</td>\n",
        "      <td>   J.BIANCHI</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34 </th>\n",
        "      <td>  4</td>\n",
        "      <td>   M.CHILTON</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>205</th>\n",
        "      <td> 25</td>\n",
        "      <td>    J.VERGNE</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>219</th>\n",
        "      <td> 26</td>\n",
        "      <td>     D.KVYAT</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>109</th>\n",
        "      <td> 13</td>\n",
        "      <td> P.MALDONADO</td>\n",
        "      <td>  842.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>258</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55 </th>\n",
        "      <td>  7</td>\n",
        "      <td> K.RAIKKONEN</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>245</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>142</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td>  843.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td>  844.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>188</th>\n",
        "      <td> 22</td>\n",
        "      <td>    J.BUTTON</td>\n",
        "      <td>  845.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>158</th>\n",
        "      <td> 20</td>\n",
        "      <td> K.MAGNUSSEN</td>\n",
        "      <td>  845.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  846.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0  </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  846.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>81 </th>\n",
        "      <td>  9</td>\n",
        "      <td>  M.ERICSSON</td>\n",
        "      <td>  847.000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>220</th>\n",
        "      <td> 26</td>\n",
        "      <td>     D.KVYAT</td>\n",
        "      <td>  849.817</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>208</th>\n",
        "      <td> 25</td>\n",
        "      <td>    J.VERGNE</td>\n",
        "      <td>  874.166</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>66 </th>\n",
        "      <td>  7</td>\n",
        "      <td> K.RAIKKONEN</td>\n",
        "      <td>  934.009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>  1</td>\n",
        "      <td>    S.VETTEL</td>\n",
        "      <td>  977.535</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27 </th>\n",
        "      <td>  3</td>\n",
        "      <td> D.RICCIARDO</td>\n",
        "      <td>  987.803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>198</th>\n",
        "      <td> 22</td>\n",
        "      <td>    J.BUTTON</td>\n",
        "      <td> 1036.979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td> 1116.970</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>246</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td> 1125.072</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>126</th>\n",
        "      <td> 14</td>\n",
        "      <td>    F.ALONSO</td>\n",
        "      <td> 1144.167</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>150</th>\n",
        "      <td> 19</td>\n",
        "      <td>     F.MASSA</td>\n",
        "      <td> 1177.634</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>266</th>\n",
        "      <td> 77</td>\n",
        "      <td>    V.BOTTAS</td>\n",
        "      <td> 1188.277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 </th>\n",
        "      <td>  6</td>\n",
        "      <td>   N.ROSBERG</td>\n",
        "      <td> 1201.803</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>253</th>\n",
        "      <td> 44</td>\n",
        "      <td>  L.HAMILTON</td>\n",
        "      <td> 1214.207</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>166</th>\n",
        "      <td> 20</td>\n",
        "      <td> K.MAGNUSSEN</td>\n",
        "      <td> 1239.418</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>288 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "     driver_no         name      time\n",
        "51           6    N.ROSBERG    92.506\n",
        "45           6    N.ROSBERG    92.629\n",
        "254         44   L.HAMILTON    92.703\n",
        "248         44   L.HAMILTON    92.946\n",
        "52           6    N.ROSBERG    92.950\n",
        "255         44   L.HAMILTON    92.982\n",
        "271         77     V.BOTTAS    93.128\n",
        "265         77     V.BOTTAS    93.329\n",
        "259         77     V.BOTTAS    93.443\n",
        "149         19      F.MASSA    93.527\n",
        "155         19      F.MASSA    93.527\n",
        "143         19      F.MASSA    93.551\n",
        "247         44   L.HAMILTON    93.611\n",
        "44           6    N.ROSBERG    93.671\n",
        "119         14     F.ALONSO    93.675\n",
        "132         14     F.ALONSO    93.740\n",
        "268         77     V.BOTTAS    93.801\n",
        "125         14     F.ALONSO    93.858\n",
        "128         14     F.ALONSO    94.005\n",
        "152         19      F.MASSA    94.059\n",
        "30           3  D.RICCIARDO    94.075\n",
        "159         20  K.MAGNUSSEN    94.229\n",
        "171         20  K.MAGNUSSEN    94.242\n",
        "260         77     V.BOTTAS    94.301\n",
        "201         22     J.BUTTON    94.317\n",
        "13           1     S.VETTEL    94.432\n",
        "165         20  K.MAGNUSSEN    94.437\n",
        "18           3  D.RICCIARDO    94.466\n",
        "144         19      F.MASSA    94.483\n",
        "120         14     F.ALONSO    94.497\n",
        "..         ...          ...       ...\n",
        "134         17    J.BIANCHI   842.000\n",
        "34           4    M.CHILTON   842.000\n",
        "205         25     J.VERGNE   842.000\n",
        "219         26      D.KVYAT   842.000\n",
        "109         13  P.MALDONADO   842.000\n",
        "258         77     V.BOTTAS   843.000\n",
        "55           7  K.RAIKKONEN   843.000\n",
        "245         44   L.HAMILTON   843.000\n",
        "42           6    N.ROSBERG   843.000\n",
        "142         19      F.MASSA   843.000\n",
        "118         14     F.ALONSO   844.000\n",
        "188         22     J.BUTTON   845.000\n",
        "158         20  K.MAGNUSSEN   845.000\n",
        "17           3  D.RICCIARDO   846.000\n",
        "0            1     S.VETTEL   846.000\n",
        "81           9   M.ERICSSON   847.000\n",
        "220         26      D.KVYAT   849.817\n",
        "208         25     J.VERGNE   874.166\n",
        "66           7  K.RAIKKONEN   934.009\n",
        "10           1     S.VETTEL   977.535\n",
        "27           3  D.RICCIARDO   987.803\n",
        "198         22     J.BUTTON  1036.979\n",
        "43           6    N.ROSBERG  1116.970\n",
        "246         44   L.HAMILTON  1125.072\n",
        "126         14     F.ALONSO  1144.167\n",
        "150         19      F.MASSA  1177.634\n",
        "266         77     V.BOTTAS  1188.277\n",
        "50           6    N.ROSBERG  1201.803\n",
        "253         44   L.HAMILTON  1214.207\n",
        "166         20  K.MAGNUSSEN  1239.418\n",
        "\n",
        "[288 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Seaborn Works Well for Statistical Plots\n",
      "Pandas has some built-in plotting functionality as well. One thing to notice is that the tools built around dataframes can infer information from the DataFrame or Series that wouldn't be available with simple arrays, like the x axis labeled as 'time'."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.distplot(df.time)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x16a0a160>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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JogwcP515znau/RVZ6rcQkWJRsigDx/3O7XXtuV0JldXcUAHAgK6IEpECU7IoA8d7RgkB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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x16a0a6a0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Only Look at the Lower Times Posted"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.distplot(df.loc[df.time < 100, 'time'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x167f1358>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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l1OPTrz+llKoEjgBeIKqU+iqwSWs9Ot+xqWxMrhsdC9PRHaDY7aC6/OPDQCJ7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\ntlRw44YKNtQXywqgQmQxi8V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       "text": [
        "<matplotlib.figure.Figure at 0x16ce6e10>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}