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     "input": [
      "from IPython import utils\n",
      "from IPython.core.display import HTML\n",
      "import os\n",
      "def css_styling():\n",
      " \"\"\"Load default custom.css file from ipython profile\"\"\"\n",
      " base = utils.path.get_ipython_dir()\n",
      " styles = \"<style>\\n%s\\n</style>\" % (open(os.path.join(base,'/home/shanks/Documents/New Folder/base16-ipython-notebook/base16-londontube-light.css'),'r').read())\n",
      " return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        "/*\n",
        "\n",
        "    Name:       Base16 London Tube Light\n",
        "    Author:     Jan T. Sott\n",
        "\n",
        "    CodeMirror template adapted for IPython Notebook by Nikhil Sonnad (https://github.com/nsonnad/base16-ipython-notebook)\n",
        "    CodeMirror template by Jan T. Sott (https://github.com/idleberg/base16-chrome-devtools)\n",
        "    Original Base16 color scheme by Chris Kempson (https://github.com/chriskempson/base16)\n",
        "\n",
        "*/\n",
        "\n",
        "/* Uncomment to use a custom font\n",
        "div#notebook, div.CodeMirror, div.output_area pre, div.output_wrapper, div.prompt {\n",
        "  font-family: 'Custom Font Name', monospace !important;\n",
        "}\n",
        "*/\n",
        "\n",
        "/* GLOBALS */\n",
        "body {background-color: #ffffff;}\n",
        "a {color: #009ddc;}\n",
        "\n",
        "/* INTRO PAGE */\n",
        ".toolbar_info, .list-container {color: #1c3f95;}\n",
        "\n",
        "/* NOTEBOOK */\n",
        "\n",
        "/* comment out this line to bring the toolbar back */\n",
        "div#maintoolbar, div#header {display: none !important;}\n",
        "\n",
        "div#notebook {border-top: none;}\n",
        "\n",
        "div.input_prompt {color: #98005d;}\n",
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        "div.output_area pre {font-weight: normal; color: #5a5758;}\n",
        "div.output_subarea {font-weight: normal; color: #5a5758;}\n",
        "\n",
        ".rendered_html table, .rendered_html th, .rendered_html tr, .rendered_html td {\n",
        "  border: 1px  #737171 solid;\n",
        "  color: #737171;\n",
        "}\n",
        "div.output_html { font-family: sans-serif; }\n",
        "table.dataframe tr {border: 1px #5a5758;}\n",
        "\n",
        "div.cell.selected {border-radius: 0px;}\n",
        "div.cell.edit_mode {border-radius: 0px; border: thin solid #b06110;}\n",
        "div.text_cell_render, div.output_html {color: #5a5758;}\n",
        "\n",
        "span.ansiblack {color: #1c3f95;}\n",
        "span.ansiblue {color: #85cebc;}\n",
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        "\n",
        ".cm-s-ipython.CodeMirror {background: #ffffff; color: #1c3f95;}\n",
        ".cm-s-ipython div.CodeMirror-selected {background: #e7e7e8 !important;}\n",
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        "\n",
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       "text": [
        "<IPython.core.display.HTML at 0x7f6d78133410>"
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      }
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    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Dive-in to Pandas\n",
      "Pandas is a Python library for doing data analysis. It's really fast and lets you do exploratory work incredibly quickly. In this post, I will demonstrate basic capabilities of pandas. I also delve into Data Munging aspects of Pandas in a seperate post here.   \n",
      "\n",
      "NOTE: this notebook is complete with my own examples and illustration but inspired by the amazing Pandas Cookbook. If you're looking for a tutorial, that's the better place to start. \n",
      "\n",
      "OK, let's get started!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Importing required Python packages\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "from pprint import pprint\n",
      "import pandas as pd\n",
      "import zipfile\n",
      "import requests\n",
      "import StringIO\n",
      "import datetime\n",
      "import seaborn\n",
      "\n",
      "np.random.seed(sum(map(ord, \"aesthetics\")))\n",
      "seaborn.set_context('notebook')\n",
      "\n",
      "pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier\n",
      "plt.rcParams['figure.figsize'] = (15, 5) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Reading from CSV   \n",
      "\n",
      "It's easy to read CSV files directly in Pandas. Let's read a file from the UCI Machine Learning Repo.\n",
      "\n",
      "**Dataset name** - Citi Bike Trip Histories  \n",
      "**Source**         - NYC Bike Share/311 calls from NYC Open Data\n",
      "\n",
      "The dataset has info about where Citi Bikers ride, When they ride, How far they go and so on. We'll be looking at the Bike Trip dataset for Jan 2014."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#The Dataset is a zip file. So let's first read in the dataset through requests then pass it on to Pandas through the\n",
      "#read_csv command\n",
      "url=requests.get('https://s3.amazonaws.com/tripdata/201402-citibike-tripdata.zip')\n",
      "z=zipfile.ZipFile(StringIO.StringIO(url.content))\n",
      "\n",
      "bike=pd.read_csv(z.open('2014-02 - Citi Bike trip data.csv'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Display the Top 5 rows in the dataset\n",
      "print bike.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tripduration            starttime             stoptime  start station id  \\\n",
        "0           382  2014-02-01 00:00:00  2014-02-01 00:06:22               294   \n",
        "1           372  2014-02-01 00:00:03  2014-02-01 00:06:15               285   \n",
        "2           591  2014-02-01 00:00:09  2014-02-01 00:10:00               247   \n",
        "3           583  2014-02-01 00:00:32  2014-02-01 00:10:15               357   \n",
        "4           223  2014-02-01 00:00:41  2014-02-01 00:04:24               401   \n",
        "\n",
        "        start station name  start station latitude  start station longitude  \\\n",
        "0      Washington Square E               40.730494               -73.995721   \n",
        "1       Broadway & E 14 St               40.734546               -73.990741   \n",
        "2   Perry St & Bleecker St               40.735354               -74.004831   \n",
        "3       E 11 St & Broadway               40.732618               -73.991580   \n",
        "4  Allen St & Rivington St               40.720196               -73.989978   \n",
        "\n",
        "   end station id          end station name  end station latitude  \\\n",
        "0             265  Stanton St & Chrystie St             40.722293   \n",
        "1             439            E 4 St & 2 Ave             40.726281   \n",
        "2             251       Mott St & Prince St             40.723180   \n",
        "3             284     Greenwich Ave & 8 Ave             40.739017   \n",
        "4             439            E 4 St & 2 Ave             40.726281   \n",
        "\n",
        "   end station longitude  bikeid    usertype birth year  gender  \n",
        "0             -73.991475   21101  Subscriber       1991       1  \n",
        "1             -73.989780   15456  Subscriber       1979       2  \n",
        "2             -73.994800   16281  Subscriber       1948       2  \n",
        "3             -74.002638   17400  Subscriber       1981       1  \n",
        "4             -73.989780   19341  Subscriber       1990       1  \n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Let's specify some optional parameters to make the dataset more digestable\n",
      "bike=pd.read_csv(z.open('2014-02 - Citi Bike trip data.csv'), encoding='utf8', parse_dates=['starttime','stoptime'], \n",
      "                 index_col=None)\n",
      "bike.set_index('bikeid',drop='False')\n",
      "#Note above, we could've simply said index_cols = bikeid but it would drop the actual column. To retain bikeid in the\n",
      "#dataset, I set index to None initially then replaced it with bikeid, this time setting the option not to drop the column.\n",
      "#If this is complicated, skip and continue, I will illustrate in detail later.\n",
      "print bike.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   tripduration           starttime            stoptime  start station id  \\\n",
        "0           382 2014-02-01 00:00:00 2014-02-01 00:06:22               294   \n",
        "1           372 2014-02-01 00:00:03 2014-02-01 00:06:15               285   \n",
        "2           591 2014-02-01 00:00:09 2014-02-01 00:10:00               247   \n",
        "3           583 2014-02-01 00:00:32 2014-02-01 00:10:15               357   \n",
        "4           223 2014-02-01 00:00:41 2014-02-01 00:04:24               401   \n",
        "\n",
        "        start station name  start station latitude  start station longitude  \\\n",
        "0      Washington Square E               40.730494               -73.995721   \n",
        "1       Broadway & E 14 St               40.734546               -73.990741   \n",
        "2   Perry St & Bleecker St               40.735354               -74.004831   \n",
        "3       E 11 St & Broadway               40.732618               -73.991580   \n",
        "4  Allen St & Rivington St               40.720196               -73.989978   \n",
        "\n",
        "   end station id          end station name  end station latitude  \\\n",
        "0             265  Stanton St & Chrystie St             40.722293   \n",
        "1             439            E 4 St & 2 Ave             40.726281   \n",
        "2             251       Mott St & Prince St             40.723180   \n",
        "3             284     Greenwich Ave & 8 Ave             40.739017   \n",
        "4             439            E 4 St & 2 Ave             40.726281   \n",
        "\n",
        "   end station longitude  bikeid    usertype birth year  gender  \n",
        "0             -73.991475   21101  Subscriber       1991       1  \n",
        "1             -73.989780   15456  Subscriber       1979       2  \n",
        "2             -73.994800   16281  Subscriber       1948       2  \n",
        "3             -74.002638   17400  Subscriber       1981       1  \n",
        "4             -73.989780   19341  Subscriber       1990       1  \n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#The object we've imported bike is called a DataFrame, and is made up of rows and columns. \n",
      "#Think of this like excel on wheels.\n",
      "\n",
      "#Let's get some info on the columns our dataframe.\n",
      "bike.info()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 224736 entries, 0 to 224735\n",
        "Data columns (total 15 columns):\n",
        "tripduration               224736 non-null int64\n",
        "starttime                  224736 non-null datetime64[ns]\n",
        "stoptime                   224736 non-null datetime64[ns]\n",
        "start station id           224736 non-null int64\n",
        "start station name         224736 non-null object\n",
        "start station latitude     224736 non-null float64\n",
        "start station longitude    224736 non-null float64\n",
        "end station id             224736 non-null int64\n",
        "end station name           224736 non-null object\n",
        "end station latitude       224736 non-null float64\n",
        "end station longitude      224736 non-null float64\n",
        "bikeid                     224736 non-null int64\n",
        "usertype                   224736 non-null object\n",
        "birth year                 224736 non-null object\n",
        "gender                     224736 non-null int64\n",
        "dtypes: datetime64[ns](2), float64(4), int64(5), object(4)"
       ]
      }
     ],
     "prompt_number": 326
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Some more detail\n",
      "bike.describe()"
     ],
     "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>tripduration</th>\n",
        "      <th>start station id</th>\n",
        "      <th>start station latitude</th>\n",
        "      <th>start station longitude</th>\n",
        "      <th>end station id</th>\n",
        "      <th>end station latitude</th>\n",
        "      <th>end station longitude</th>\n",
        "      <th>gender</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "      <td> 224736.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>    874.519810</td>\n",
        "      <td>    439.203479</td>\n",
        "      <td>     40.734366</td>\n",
        "      <td>    -73.990386</td>\n",
        "      <td>    440.741995</td>\n",
        "      <td>     40.734221</td>\n",
        "      <td>    -73.990521</td>\n",
        "      <td>      1.154617</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   5486.092219</td>\n",
        "      <td>    335.723861</td>\n",
        "      <td>      0.019031</td>\n",
        "      <td>      0.011853</td>\n",
        "      <td>    341.497433</td>\n",
        "      <td>      0.019048</td>\n",
        "      <td>      0.011920</td>\n",
        "      <td>      0.436592</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>     60.000000</td>\n",
        "      <td>     72.000000</td>\n",
        "      <td>     40.680342</td>\n",
        "      <td>    -74.017134</td>\n",
        "      <td>     72.000000</td>\n",
        "      <td>     40.680342</td>\n",
        "      <td>    -74.017134</td>\n",
        "      <td>      0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>    360.000000</td>\n",
        "      <td>    305.000000</td>\n",
        "      <td>     40.721854</td>\n",
        "      <td>    -73.998522</td>\n",
        "      <td>    305.000000</td>\n",
        "      <td>     40.721816</td>\n",
        "      <td>    -73.999061</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>    544.000000</td>\n",
        "      <td>    403.000000</td>\n",
        "      <td>     40.736197</td>\n",
        "      <td>    -73.990617</td>\n",
        "      <td>    403.000000</td>\n",
        "      <td>     40.735877</td>\n",
        "      <td>    -73.990741</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>    845.000000</td>\n",
        "      <td>    490.000000</td>\n",
        "      <td>     40.749156</td>\n",
        "      <td>    -73.981918</td>\n",
        "      <td>    488.000000</td>\n",
        "      <td>     40.749013</td>\n",
        "      <td>    -73.981948</td>\n",
        "      <td>      1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 766108.000000</td>\n",
        "      <td>   3002.000000</td>\n",
        "      <td>     40.770513</td>\n",
        "      <td>    -73.950048</td>\n",
        "      <td>   3002.000000</td>\n",
        "      <td>     40.770513</td>\n",
        "      <td>    -73.950048</td>\n",
        "      <td>      2.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "        tripduration  start station id  start station latitude  \\\n",
        "count  224736.000000     224736.000000           224736.000000   \n",
        "mean      874.519810        439.203479               40.734366   \n",
        "std      5486.092219        335.723861                0.019031   \n",
        "min        60.000000         72.000000               40.680342   \n",
        "25%       360.000000        305.000000               40.721854   \n",
        "50%       544.000000        403.000000               40.736197   \n",
        "75%       845.000000        490.000000               40.749156   \n",
        "max    766108.000000       3002.000000               40.770513   \n",
        "\n",
        "       start station longitude  end station id  end station latitude  \\\n",
        "count            224736.000000   224736.000000         224736.000000   \n",
        "mean                -73.990386      440.741995             40.734221   \n",
        "std                   0.011853      341.497433              0.019048   \n",
        "min                 -74.017134       72.000000             40.680342   \n",
        "25%                 -73.998522      305.000000             40.721816   \n",
        "50%                 -73.990617      403.000000             40.735877   \n",
        "75%                 -73.981918      488.000000             40.749013   \n",
        "max                 -73.950048     3002.000000             40.770513   \n",
        "\n",
        "       end station longitude         gender  \n",
        "count          224736.000000  224736.000000  \n",
        "mean              -73.990521       1.154617  \n",
        "std                 0.011920       0.436592  \n",
        "min               -74.017134       0.000000  \n",
        "25%               -73.999061       1.000000  \n",
        "50%               -73.990741       1.000000  \n",
        "75%               -73.981948       1.000000  \n",
        "max               -73.950048       2.000000  "
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Accessing Columns\n",
      "print bike['gender'].head() #or just bike.gender"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0    1\n",
        "1    2\n",
        "2    2\n",
        "3    1\n",
        "4    1\n",
        "Name: gender, dtype: int64\n"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plenty of functions/transformations available - this below lists the unique values.\n",
      "print bike.gender.unique()\n",
      "\n",
      "#Calling a histogram on a column by using hist()\n",
      "bike['start station id'].hist()\n",
      "\n",
      "#Try hitting tab after bike.colname to see options"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1 2 0]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7f8757f3c350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAA4YAAAE4CAYAAAAHLO0hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEDhhCEAAEDh/h9jXKOzdN90WAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8757f46250>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plotting is easy, we could just do a table.colname.plot() but to get something pretty to display, let's do\n",
      "#a scatter plot\n",
      "plt.scatter(bike.index[:25], bike['tripduration'][:25], cmap=plt.cm.RdBu)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OLN3+tHO/hLfGhg0b8o//+I/ZuHFj/uiP/iiNRkNGJXH69OlcvHgxzz//fM6f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\nAACgwpQ+AACACvt/8mwr8895RN0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f87532b8650>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Slicing - first 5 rows in the DF\n",
      "bike[:5]"
     ],
     "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>tripduration</th>\n",
        "      <th>starttime</th>\n",
        "      <th>stoptime</th>\n",
        "      <th>start station id</th>\n",
        "      <th>start station name</th>\n",
        "      <th>start station latitude</th>\n",
        "      <th>start station longitude</th>\n",
        "      <th>end station id</th>\n",
        "      <th>end station name</th>\n",
        "      <th>end station latitude</th>\n",
        "      <th>end station longitude</th>\n",
        "      <th>usertype</th>\n",
        "      <th>birth year</th>\n",
        "      <th>gender</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bikeid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>21101</th>\n",
        "      <td> 382</td>\n",
        "      <td>2014-02-01 00:00:00</td>\n",
        "      <td>2014-02-01 00:06:22</td>\n",
        "      <td> 294</td>\n",
        "      <td>     Washington Square E</td>\n",
        "      <td> 40.730494</td>\n",
        "      <td>-73.995721</td>\n",
        "      <td> 265</td>\n",
        "      <td> Stanton St &amp; Chrystie St</td>\n",
        "      <td> 40.722293</td>\n",
        "      <td>-73.991475</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1991</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15456</th>\n",
        "      <td> 372</td>\n",
        "      <td>2014-02-01 00:00:03</td>\n",
        "      <td>2014-02-01 00:06:15</td>\n",
        "      <td> 285</td>\n",
        "      <td>      Broadway &amp; E 14 St</td>\n",
        "      <td> 40.734546</td>\n",
        "      <td>-73.990741</td>\n",
        "      <td> 439</td>\n",
        "      <td>           E 4 St &amp; 2 Ave</td>\n",
        "      <td> 40.726281</td>\n",
        "      <td>-73.989780</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1979</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16281</th>\n",
        "      <td> 591</td>\n",
        "      <td>2014-02-01 00:00:09</td>\n",
        "      <td>2014-02-01 00:10:00</td>\n",
        "      <td> 247</td>\n",
        "      <td>  Perry St &amp; Bleecker St</td>\n",
        "      <td> 40.735354</td>\n",
        "      <td>-74.004831</td>\n",
        "      <td> 251</td>\n",
        "      <td>      Mott St &amp; Prince St</td>\n",
        "      <td> 40.723180</td>\n",
        "      <td>-73.994800</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1948</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17400</th>\n",
        "      <td> 583</td>\n",
        "      <td>2014-02-01 00:00:32</td>\n",
        "      <td>2014-02-01 00:10:15</td>\n",
        "      <td> 357</td>\n",
        "      <td>      E 11 St &amp; Broadway</td>\n",
        "      <td> 40.732618</td>\n",
        "      <td>-73.991580</td>\n",
        "      <td> 284</td>\n",
        "      <td>    Greenwich Ave &amp; 8 Ave</td>\n",
        "      <td> 40.739017</td>\n",
        "      <td>-74.002638</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1981</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19341</th>\n",
        "      <td> 223</td>\n",
        "      <td>2014-02-01 00:00:41</td>\n",
        "      <td>2014-02-01 00:04:24</td>\n",
        "      <td> 401</td>\n",
        "      <td> Allen St &amp; Rivington St</td>\n",
        "      <td> 40.720196</td>\n",
        "      <td>-73.989978</td>\n",
        "      <td> 439</td>\n",
        "      <td>           E 4 St &amp; 2 Ave</td>\n",
        "      <td> 40.726281</td>\n",
        "      <td>-73.989780</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1990</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "        tripduration           starttime            stoptime  \\\n",
        "bikeid                                                         \n",
        "21101            382 2014-02-01 00:00:00 2014-02-01 00:06:22   \n",
        "15456            372 2014-02-01 00:00:03 2014-02-01 00:06:15   \n",
        "16281            591 2014-02-01 00:00:09 2014-02-01 00:10:00   \n",
        "17400            583 2014-02-01 00:00:32 2014-02-01 00:10:15   \n",
        "19341            223 2014-02-01 00:00:41 2014-02-01 00:04:24   \n",
        "\n",
        "        start station id       start station name  start station latitude  \\\n",
        "bikeid                                                                      \n",
        "21101                294      Washington Square E               40.730494   \n",
        "15456                285       Broadway & E 14 St               40.734546   \n",
        "16281                247   Perry St & Bleecker St               40.735354   \n",
        "17400                357       E 11 St & Broadway               40.732618   \n",
        "19341                401  Allen St & Rivington St               40.720196   \n",
        "\n",
        "        start station longitude  end station id          end station name  \\\n",
        "bikeid                                                                      \n",
        "21101                -73.995721             265  Stanton St & Chrystie St   \n",
        "15456                -73.990741             439            E 4 St & 2 Ave   \n",
        "16281                -74.004831             251       Mott St & Prince St   \n",
        "17400                -73.991580             284     Greenwich Ave & 8 Ave   \n",
        "19341                -73.989978             439            E 4 St & 2 Ave   \n",
        "\n",
        "        end station latitude  end station longitude    usertype birth year  \\\n",
        "bikeid                                                                       \n",
        "21101              40.722293             -73.991475  Subscriber       1991   \n",
        "15456              40.726281             -73.989780  Subscriber       1979   \n",
        "16281              40.723180             -73.994800  Subscriber       1948   \n",
        "17400              40.739017             -74.002638  Subscriber       1981   \n",
        "19341              40.726281             -73.989780  Subscriber       1990   \n",
        "\n",
        "        gender  \n",
        "bikeid          \n",
        "21101        1  \n",
        "15456        2  \n",
        "16281        2  \n",
        "17400        1  \n",
        "19341        1  "
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Slicing - first 5 rows of specific column(s)\n",
      "bike[['gender','tripduration']][:5]\n",
      "\n",
      "##Note the 2 sets of [] - list of lists"
     ],
     "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>gender</th>\n",
        "      <th>tripduration</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bikeid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>21101</th>\n",
        "      <td> 1</td>\n",
        "      <td> 382</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15456</th>\n",
        "      <td> 2</td>\n",
        "      <td> 372</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16281</th>\n",
        "      <td> 2</td>\n",
        "      <td> 591</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17400</th>\n",
        "      <td> 1</td>\n",
        "      <td> 583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19341</th>\n",
        "      <td> 1</td>\n",
        "      <td> 223</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "        gender  tripduration\n",
        "bikeid                      \n",
        "21101        1           382\n",
        "15456        2           372\n",
        "16281        2           591\n",
        "17400        1           583\n",
        "19341        1           223"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Find the startstation id with the maximum trips\n",
      "bike['start station id'].value_counts()\n",
      "\n",
      "#It's station 293 with 2920 trips"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 117,
       "text": [
        "293    2920\n",
        "519    2719\n",
        "497    2493\n",
        "435    2403\n",
        "521    2171\n",
        "490    1944\n",
        "477    1916\n",
        "382    1808\n",
        "151    1796\n",
        "285    1795\n",
        "402    1694\n",
        "318    1688\n",
        "492    1570\n",
        "444    1562\n",
        "379    1538\n",
        "...\n",
        "298     89\n",
        "339     82\n",
        "242     76\n",
        "218     76\n",
        "2001    75\n",
        "216     72\n",
        "253     69\n",
        "372     57\n",
        "119     57\n",
        "418     56\n",
        "431     54\n",
        "278     45\n",
        "443     41\n",
        "2005    36\n",
        "320      4\n",
        "Length: 329, dtype: int64"
       ]
      }
     ],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Find the start station with the longest trip\n",
      "\n",
      "#First find the longest trip\n",
      "print \"Longest Trip: %.6f\" %bike.tripduration.max()\n",
      "\n",
      "#Now retrieve row from DF where duration is the longest\n",
      "bike[bike.tripduration==bike.tripduration.max()]\n",
      "\n",
      "\n",
      "#bike[bike.tripduration.idxmax()]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Longest Trip: 766108.000000\n"
       ]
      },
      {
       "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>tripduration</th>\n",
        "      <th>starttime</th>\n",
        "      <th>stoptime</th>\n",
        "      <th>start station id</th>\n",
        "      <th>start station name</th>\n",
        "      <th>start station latitude</th>\n",
        "      <th>start station longitude</th>\n",
        "      <th>end station id</th>\n",
        "      <th>end station name</th>\n",
        "      <th>end station latitude</th>\n",
        "      <th>end station longitude</th>\n",
        "      <th>usertype</th>\n",
        "      <th>birth year</th>\n",
        "      <th>gender</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bikeid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>15379</th>\n",
        "      <td> 766108</td>\n",
        "      <td>2014-02-08 15:52:51</td>\n",
        "      <td>2014-02-17 12:41:19</td>\n",
        "      <td> 353</td>\n",
        "      <td> S Portland Ave &amp; Hanson Pl</td>\n",
        "      <td> 40.685396</td>\n",
        "      <td>-73.974315</td>\n",
        "      <td> 281</td>\n",
        "      <td> Grand Army Plaza &amp; Central Park S</td>\n",
        "      <td> 40.764397</td>\n",
        "      <td>-73.973715</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1982</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 167,
       "text": [
        "        tripduration           starttime            stoptime  \\\n",
        "bikeid                                                         \n",
        "15379         766108 2014-02-08 15:52:51 2014-02-17 12:41:19   \n",
        "\n",
        "        start station id          start station name  start station latitude  \\\n",
        "bikeid                                                                         \n",
        "15379                353  S Portland Ave & Hanson Pl               40.685396   \n",
        "\n",
        "        start station longitude  end station id  \\\n",
        "bikeid                                            \n",
        "15379                -73.974315             281   \n",
        "\n",
        "                         end station name  end station latitude  \\\n",
        "bikeid                                                            \n",
        "15379   Grand Army Plaza & Central Park S             40.764397   \n",
        "\n",
        "        end station longitude    usertype birth year  gender  \n",
        "bikeid                                                        \n",
        "15379              -73.973715  Subscriber       1982       2  "
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Find the start station with the longest trip by a Female Biker\n",
      "\n",
      "#First find the longest trip by a female biker\n",
      "print \"Longest Trip by a Female Biker: %.6f\" %bike[bike.gender==1].tripduration.max()\n",
      "\n",
      "#Now retrieve row from DF where duration is the longest\n",
      "bike[bike.tripduration==bike[bike.gender==1].tripduration.max()]\n",
      "\n",
      "\n",
      "#bike[bike.tripduration.idxmax()]\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Longest Trip by a Female Biker: 585281.000000\n"
       ]
      },
      {
       "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>tripduration</th>\n",
        "      <th>starttime</th>\n",
        "      <th>stoptime</th>\n",
        "      <th>start station id</th>\n",
        "      <th>start station name</th>\n",
        "      <th>start station latitude</th>\n",
        "      <th>start station longitude</th>\n",
        "      <th>end station id</th>\n",
        "      <th>end station name</th>\n",
        "      <th>end station latitude</th>\n",
        "      <th>end station longitude</th>\n",
        "      <th>usertype</th>\n",
        "      <th>birth year</th>\n",
        "      <th>gender</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bikeid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>16149</th>\n",
        "      <td> 585281</td>\n",
        "      <td>2014-02-13 16:52:23</td>\n",
        "      <td>2014-02-20 11:27:04</td>\n",
        "      <td> 252</td>\n",
        "      <td> MacDougal St &amp; Washington Sq</td>\n",
        "      <td> 40.732264</td>\n",
        "      <td>-73.998522</td>\n",
        "      <td> 348</td>\n",
        "      <td> W Broadway &amp; Spring St</td>\n",
        "      <td> 40.72491</td>\n",
        "      <td>-74.001547</td>\n",
        "      <td> Subscriber</td>\n",
        "      <td> 1973</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 171,
       "text": [
        "        tripduration           starttime            stoptime  \\\n",
        "bikeid                                                         \n",
        "16149         585281 2014-02-13 16:52:23 2014-02-20 11:27:04   \n",
        "\n",
        "        start station id            start station name  \\\n",
        "bikeid                                                   \n",
        "16149                252  MacDougal St & Washington Sq   \n",
        "\n",
        "        start station latitude  start station longitude  end station id  \\\n",
        "bikeid                                                                    \n",
        "16149                40.732264               -73.998522             348   \n",
        "\n",
        "              end station name  end station latitude  end station longitude  \\\n",
        "bikeid                                                                        \n",
        "16149   W Broadway & Spring St              40.72491             -74.001547   \n",
        "\n",
        "          usertype birth year  gender  \n",
        "bikeid                                 \n",
        "16149   Subscriber       1973       1  "
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Plot the Top 10 longest trips by Male Subscribers as a Bar Plot\n",
      "\n",
      "#Let's first get all Male Subscribers\n",
      "bike[(bike.usertype==\"Subscriber\") & (bike.gender==2)]\n",
      "\n",
      "#Now, valuecounts of tripduration for these subscribers - only the Top 10\n",
      "print \"Top 10 Male Subscribers with the longest trips\"\n",
      "print bike[(bike.usertype==\"Subscriber\") & (bike.gender==2)].tripduration.value_counts()[:10]\n",
      "\n",
      "#Now we'll just plot this as a bar\n",
      "bike[(bike.usertype==\"Subscriber\") & (bike.gender==2)].tripduration.value_counts()[:10].plot(kind='bar')\n",
      "plt.suptitle('Top 10 Longest Trips by Male Subscribers', fontsize=14)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Top 10 Male Subscribers with the longest trips\n",
        "427    76\n",
        "557    71\n",
        "403    70\n",
        "385    70\n",
        "530    68\n",
        "458    68\n",
        "372    68\n",
        "364    67\n",
        "420    67\n",
        "531    66\n",
        "dtype: int64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Lk3/ts23bNq1Zs0Z9+vRRenq6nn32WdXW1mrs2LGcC9EG5eXlWrt2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\naAscY4wL/QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f875ea1d8d0>"
       ]
      }
     ],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#The columns of a Data Frame are of type called Series. These are internally just numpy arrays, which we can find out\n",
      "#by calling the values() method\n",
      "bike['start station id'].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 213,
       "text": [
        "array([294, 285, 247, ..., 497, 353, 252])"
       ]
      }
     ],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#OK let's dig into some Time Series functionality. Pandas again is excellent in this area.\n",
      "\n",
      "#Say we want to get the day from the timestamp\n",
      "temp=pd.DatetimeIndex(bike.starttime) \n",
      "temp.day\n",
      "#NOTE: the .day, .month.. functions work only if the Date field is indexed. Since in our case it's not we had to\n",
      "#temporarily index it. Otherwise we could've just done a bike.starttime.day\n",
      "\n",
      "#Let's calc and store the weekday. Note we're adding this as a new column to bike\n",
      "bike['weekday']=temp.weekday"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 327
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#GROUP BY - now let's say we want to group all bikers by weekday we could do\n",
      "\n",
      "#Let's add weekday as an index to the DF. We're choosing to append rather than replace the original index.\n",
      "bike.set_index('weekday',drop='false', append='true')\n",
      "test=bike.groupby('weekday').aggregate('count')['bikeid']\n",
      "print test\n",
      "\n",
      "#Say we wanted to plot this, but we want to change the indexes to something meaningful\n",
      "test.index=['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']\n",
      "test.plot(kind='bar',color='orange')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "weekday\n",
        "0          27222\n",
        "1          35022\n",
        "2          31020\n",
        "3          31900\n",
        "4          30371\n",
        "5          34062\n",
        "6          35139\n",
        "Name: bikeid, dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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gBv3hH/5hvvSlL+W8887Lz372s/zRH/1R0S0xQ3p6etLb25v9+/fna1/7Wq69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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f875624d050>"
       ]
      }
     ],
     "prompt_number": 336
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Let's now group all Customers on working days\n",
      "female_customers=bike[(bike.usertype=='Customer')]\n",
      "grouped=female_customers.groupby('weekday').aggregate('count')\n",
      "grouped=grouped[(grouped.index.values>=1) & (grouped.index.values<=5)]\n",
      "grouped.index=['Monday','Tuesday','Wednesday','Thursday','Friday']\n",
      "grouped['bikeid'].plot(kind='bar')\n",
      "plt.suptitle('Customers biking on Working Days', fontsize=14)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NArDRK4oidXV1qaurS5cu/msEoDa58gbARmnx4sVtUyZXTZu8//77\ns3z58lx99dXVLg8AViO8AbBROu+887LFFlu03bBk1Z9NN93U1TcAapLwBgAAUAJOLQIAAJSA8AYA\nAFACwhsAAEAJCG8AAAAlILwBAACUwP8HMtoO0IUvIlgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f8754db3dd0>"
       ]
      }
     ],
     "prompt_number": 369
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#OK great let's now grab some weather data from San Francisco from Weather Underground. Firstly we'll define a custom URL\n",
      "#to grab specific months of data.\n",
      "\n",
      "#The URL template will have placeholders for dates so we can fill them up as we like.\n",
      "url_template='http://www.wunderground.com/history/airport/KSFO/{start_year}/{start_month}/{start_day}' \\\n",
      "             '/CustomHistory.html?dayend={end_day}&monthend={end_month}&yearend={end_year}' \\\n",
      "             '&req_city=NA&req_state=NA&req_statename=NA&MR=1&format=1'\n",
      "\n",
      "#Let's generate the URL to retrieve data from Sep 1, 2014 to Dec 10,2014\n",
      "url=url_template.format(start_year=2014, start_month=9, start_day=1, end_year=2014, end_month=12, end_day=10)\n",
      "\n",
      "#Great, let's now open the URL and read it in as a CSV.\n",
      "sfo=pd.read_csv(url, header=1, parse_dates=True, index_col='PST')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sfo.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "            MaxTemperatureF  MeanTemperatureF  MinTemperatureF  MaxDewPointF  \\\n",
        "2014-08-01               72                66               59            58   \n",
        "2014-08-02               69                64               59            57   \n",
        "2014-08-03               70                65               60            57   \n",
        "2014-08-04               75                68               60            59   \n",
        "2014-08-05               74                68               62            62   \n",
        "\n",
        "            MeanDewPointF  MinDewpointF  MaxHumidity  MeanHumidity  \\\n",
        "2014-08-01             57            57           90            76   \n",
        "2014-08-02             56            55           93            78   \n",
        "2014-08-03             56            55           84            73   \n",
        "2014-08-04             57            55           84            73   \n",
        "2014-08-05             58            57           84            75   \n",
        "\n",
        "            MinHumidity  MaxSeaLevelPressureIn          ...            \\\n",
        "2014-08-01           61                  29.93          ...             \n",
        "2014-08-02           63                  29.96          ...             \n",
        "2014-08-03           61                  29.98          ...             \n",
        "2014-08-04           61                  30.01          ...             \n",
        "2014-08-05           66                  30.08          ...             \n",
        "\n",
        "            MaxVisibilityMiles  MeanVisibilityMiles  MinVisibilityMiles  \\\n",
        "2014-08-01                  10                   10                  10   \n",
        "2014-08-02                  10                   10                  10   \n",
        "2014-08-03                  10                   10                  10   \n",
        "2014-08-04                  10                   10                  10   \n",
        "2014-08-05                  10                   10                  10   \n",
        "\n",
        "            MaxWindSpeedMPH  MeanWindSpeedMPH  MaxGustSpeedMPH  \\\n",
        "2014-08-01               24                15               29   \n",
        "2014-08-02               27                16               33   \n",
        "2014-08-03               22                11               28   \n",
        "2014-08-04               16                 9               22   \n",
        "2014-08-05               23                11               26   \n",
        "\n",
        "            PrecipitationIn  CloudCover Events  WindDirDegrees<br/>  \n",
        "2014-08-01             0.00           3    NaN            297<br />  \n",
        "2014-08-02             0.00           4    NaN            286<br />  \n",
        "2014-08-03             0.00           6    NaN            254<br />  \n",
        "2014-08-04             0.00           6    NaN            261<br />  \n",
        "2014-08-05             0.00           6    NaN            296<br />  \n",
        "\n",
        "[5 rows x 22 columns]\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Let's remove spaces in column names to make things easier down the line.\n",
      "sfo = sfo.rename(columns=lambda x: x.replace(' ', ''))\n",
      "sfo.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "Index([u'MaxTemperatureF', u'MeanTemperatureF', u'MinTemperatureF', u'MaxDewPointF', u'MeanDewPointF', u'MinDewpointF', u'MaxHumidity', u'MeanHumidity', u'MinHumidity', u'MaxSeaLevelPressureIn', u'MeanSeaLevelPressureIn', u'MinSeaLevelPressureIn', u'MaxVisibilityMiles', u'MeanVisibilityMiles', u'MinVisibilityMiles', u'MaxWindSpeedMPH', u'MeanWindSpeedMPH', u'MaxGustSpeedMPH', u'PrecipitationIn', u'CloudCover', u'Events', u'WindDirDegrees<br/>'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Great, now let's plot the mean temp\n",
      "with seaborn.axes_style('darkgrid'):\n",
      "    sfo.MeanTemperatureF.plot(figsize=(15,5), color='brown')\n",
      "plt.suptitle('San Francisco - Mean Temperatures in Fahrenheit',fontsize=15)\n",
      "plt.show()\n",
      "\n",
      "#That was as easy as it could get."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YjBEREREREcmAyRgREREREZEMmIwRERERERHJgMkYERERERGRDJiMERERERERyYDJGBER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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff4501c5f90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sfo.describe()"
     ],
     "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>MaxTemperatureF</th>\n",
        "      <th>MeanTemperatureF</th>\n",
        "      <th>MinTemperatureF</th>\n",
        "      <th>MaxDewPointF</th>\n",
        "      <th>MeanDewPointF</th>\n",
        "      <th>MinDewpointF</th>\n",
        "      <th>MaxHumidity</th>\n",
        "      <th>MeanHumidity</th>\n",
        "      <th>MinHumidity</th>\n",
        "      <th>MaxSeaLevelPressureIn</th>\n",
        "      <th>MeanSeaLevelPressureIn</th>\n",
        "      <th>MinSeaLevelPressureIn</th>\n",
        "      <th>MaxVisibilityMiles</th>\n",
        "      <th>MeanVisibilityMiles</th>\n",
        "      <th>MinVisibilityMiles</th>\n",
        "      <th>MaxWindSpeedMPH</th>\n",
        "      <th>MeanWindSpeedMPH</th>\n",
        "      <th>MaxGustSpeedMPH</th>\n",
        "      <th>CloudCover</th>\n",
        "      <th>hour</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101.000000</td>\n",
        "      <td> 101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  72.495050</td>\n",
        "      <td>  65.405941</td>\n",
        "      <td>  57.831683</td>\n",
        "      <td>  59.316832</td>\n",
        "      <td>  55.722772</td>\n",
        "      <td>  51.821782</td>\n",
        "      <td>  91.009901</td>\n",
        "      <td>  73.871287</td>\n",
        "      <td>  56.237624</td>\n",
        "      <td>  30.047327</td>\n",
        "      <td>  29.989208</td>\n",
        "      <td>  29.931188</td>\n",
        "      <td>  10</td>\n",
        "      <td>   9.465347</td>\n",
        "      <td>   7.445545</td>\n",
        "      <td>  19.990099</td>\n",
        "      <td>   7.831683</td>\n",
        "      <td>  24.821782</td>\n",
        "      <td>   3.801980</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   7.315222</td>\n",
        "      <td>   5.312585</td>\n",
        "      <td>   4.406970</td>\n",
        "      <td>   3.679486</td>\n",
        "      <td>   4.343084</td>\n",
        "      <td>   6.358295</td>\n",
        "      <td>   7.686996</td>\n",
        "      <td>  10.905653</td>\n",
        "      <td>  15.820966</td>\n",
        "      <td>   0.134907</td>\n",
        "      <td>   0.134964</td>\n",
        "      <td>   0.138818</td>\n",
        "      <td>   0</td>\n",
        "      <td>   0.975339</td>\n",
        "      <td>   3.579037</td>\n",
        "      <td>   5.148777</td>\n",
        "      <td>   3.307777</td>\n",
        "      <td>   6.219961</td>\n",
        "      <td>   2.135508</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>  60.000000</td>\n",
        "      <td>  55.000000</td>\n",
        "      <td>  46.000000</td>\n",
        "      <td>  49.000000</td>\n",
        "      <td>  38.000000</td>\n",
        "      <td>  28.000000</td>\n",
        "      <td>  67.000000</td>\n",
        "      <td>  43.000000</td>\n",
        "      <td>  13.000000</td>\n",
        "      <td>  29.810000</td>\n",
        "      <td>  29.750000</td>\n",
        "      <td>  29.680000</td>\n",
        "      <td>  10</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>  12.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  67.000000</td>\n",
        "      <td>  61.000000</td>\n",
        "      <td>  56.000000</td>\n",
        "      <td>  57.000000</td>\n",
        "      <td>  54.000000</td>\n",
        "      <td>  49.000000</td>\n",
        "      <td>  87.000000</td>\n",
        "      <td>  69.000000</td>\n",
        "      <td>  48.000000</td>\n",
        "      <td>  29.950000</td>\n",
        "      <td>  29.900000</td>\n",
        "      <td>  29.840000</td>\n",
        "      <td>  10</td>\n",
        "      <td>   9.000000</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td>  16.000000</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td>  21.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  72.000000</td>\n",
        "      <td>  66.000000</td>\n",
        "      <td>  58.000000</td>\n",
        "      <td>  60.000000</td>\n",
        "      <td>  57.000000</td>\n",
        "      <td>  54.000000</td>\n",
        "      <td>  90.000000</td>\n",
        "      <td>  74.000000</td>\n",
        "      <td>  57.000000</td>\n",
        "      <td>  30.020000</td>\n",
        "      <td>  29.970000</td>\n",
        "      <td>  29.910000</td>\n",
        "      <td>  10</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  21.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>  25.000000</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  76.000000</td>\n",
        "      <td>  69.000000</td>\n",
        "      <td>  61.000000</td>\n",
        "      <td>  61.000000</td>\n",
        "      <td>  58.000000</td>\n",
        "      <td>  56.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  80.000000</td>\n",
        "      <td>  66.000000</td>\n",
        "      <td>  30.130000</td>\n",
        "      <td>  30.070000</td>\n",
        "      <td>  30.020000</td>\n",
        "      <td>  10</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  24.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  29.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  95.000000</td>\n",
        "      <td>  81.000000</td>\n",
        "      <td>  66.000000</td>\n",
        "      <td>  66.000000</td>\n",
        "      <td>  64.000000</td>\n",
        "      <td>  62.000000</td>\n",
        "      <td> 100.000000</td>\n",
        "      <td>  95.000000</td>\n",
        "      <td>  90.000000</td>\n",
        "      <td>  30.460000</td>\n",
        "      <td>  30.390000</td>\n",
        "      <td>  30.330000</td>\n",
        "      <td>  10</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  10.000000</td>\n",
        "      <td>  32.000000</td>\n",
        "      <td>  14.000000</td>\n",
        "      <td>  50.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>   0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "       MaxTemperatureF  MeanTemperatureF  MinTemperatureF  MaxDewPointF  \\\n",
        "count       101.000000        101.000000       101.000000    101.000000   \n",
        "mean         72.495050         65.405941        57.831683     59.316832   \n",
        "std           7.315222          5.312585         4.406970      3.679486   \n",
        "min          60.000000         55.000000        46.000000     49.000000   \n",
        "25%          67.000000         61.000000        56.000000     57.000000   \n",
        "50%          72.000000         66.000000        58.000000     60.000000   \n",
        "75%          76.000000         69.000000        61.000000     61.000000   \n",
        "max          95.000000         81.000000        66.000000     66.000000   \n",
        "\n",
        "       MeanDewPointF  MinDewpointF  MaxHumidity  MeanHumidity  MinHumidity  \\\n",
        "count     101.000000    101.000000   101.000000    101.000000   101.000000   \n",
        "mean       55.722772     51.821782    91.009901     73.871287    56.237624   \n",
        "std         4.343084      6.358295     7.686996     10.905653    15.820966   \n",
        "min        38.000000     28.000000    67.000000     43.000000    13.000000   \n",
        "25%        54.000000     49.000000    87.000000     69.000000    48.000000   \n",
        "50%        57.000000     54.000000    90.000000     74.000000    57.000000   \n",
        "75%        58.000000     56.000000   100.000000     80.000000    66.000000   \n",
        "max        64.000000     62.000000   100.000000     95.000000    90.000000   \n",
        "\n",
        "       MaxSeaLevelPressureIn  MeanSeaLevelPressureIn  MinSeaLevelPressureIn  \\\n",
        "count             101.000000              101.000000             101.000000   \n",
        "mean               30.047327               29.989208              29.931188   \n",
        "std                 0.134907                0.134964               0.138818   \n",
        "min                29.810000               29.750000              29.680000   \n",
        "25%                29.950000               29.900000              29.840000   \n",
        "50%                30.020000               29.970000              29.910000   \n",
        "75%                30.130000               30.070000              30.020000   \n",
        "max                30.460000               30.390000              30.330000   \n",
        "\n",
        "       MaxVisibilityMiles  MeanVisibilityMiles  MinVisibilityMiles  \\\n",
        "count                 101           101.000000          101.000000   \n",
        "mean                   10             9.465347            7.445545   \n",
        "std                     0             0.975339            3.579037   \n",
        "min                    10             5.000000            0.000000   \n",
        "25%                    10             9.000000            4.000000   \n",
        "50%                    10            10.000000           10.000000   \n",
        "75%                    10            10.000000           10.000000   \n",
        "max                    10            10.000000           10.000000   \n",
        "\n",
        "       MaxWindSpeedMPH  MeanWindSpeedMPH  MaxGustSpeedMPH  CloudCover  hour  \n",
        "count       101.000000        101.000000       101.000000  101.000000   101  \n",
        "mean         19.990099          7.831683        24.821782    3.801980     0  \n",
        "std           5.148777          3.307777         6.219961    2.135508     0  \n",
        "min           8.000000          1.000000        12.000000    0.000000     0  \n",
        "25%          16.000000          5.000000        21.000000    2.000000     0  \n",
        "50%          21.000000          8.000000        25.000000    4.000000     0  \n",
        "75%          24.000000         10.000000        29.000000    6.000000     0  \n",
        "max          32.000000         14.000000        50.000000    8.000000     0  "
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#It's easy to concactenate dataframes\n",
      "\n",
      "#Let's generate the URL to retrieve data from Aug 1, 2014 to Aug 31,2014\n",
      "url=url_template.format(start_year=2014, start_month=8, start_day=1, end_year=2014, end_month=8, end_day=31)\n",
      "\n",
      "#Great, let's now open the URL and read it in as a CSV.\n",
      "sfo_aug=pd.read_csv(url, header=1, parse_dates=True, index_col='PDT')\n",
      "\n",
      "#Renaming columns\n",
      "sfo_aug = sfo_aug.rename(columns=lambda x: x.replace(' ', ''))\n",
      "\n",
      "#Concatenate Aug and Sep months\n",
      "sfo = pd.concat([sfo_aug,sfo])\n",
      "\n",
      "sfo.index.get_values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<bound method DatetimeIndex.get_values of <class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2014-08-01, ..., 2014-12-10]\n",
        "Length: 194, Freq: None, Timezone: None>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Saving back to CSV\n",
      "sfo.to_csv('SFO_Weather_Aug_to_Dec_14.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    }
   ],
   "metadata": {}
  }
 ]
}