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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# special IPython command to prepare the notebook for matplotlib\n",
      "%matplotlib inline \n",
      "\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "pd.options.display.mpl_style = 'default'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Recall from From Verena's lecture on Tuesday 9/4/2014\n",
      "\n",
      "Previously discussed: \n",
      "\n",
      "* How to load a .txt file into a pandas DataFrame\n",
      "* Bar plots in matplotlib\n",
      "* Selecting a column from a DataFrame (subFrame)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Today, we will discuss the following:\n",
      "* Reading in a CSV file into a pandas DataFrame\n",
      "* Using histograms, scatterplots and boxplots as exploratory data analysis\n",
      "* Summary statistics\n",
      "* Functions to access a pandas DataFrame\n",
      "* Defining your own functions and using loops\n",
      "\n",
      "<a href=https://raw.githubusercontent.com/cs109/2014/master/labs/Lab2_Notes.ipynb download=HW1.ipynb> Download this notebook from Github </a>\n",
      "\n",
      "#### Important: Tips for good Python Coding Practices \n",
      "\n",
      "1. Always comment the code, avoiding inline comments\n",
      "2. Define functions to do commands that you have to do repeatedly; use docstrings when defining a function.\n",
      "3. Use xrange instead of range when simply iterating a procedure N times (iterator instead of actual list built in memory).\n",
      "4. Be aware of what parts of the code take the most time to run and plan accordingly (especially web requests, they tend to take a while)\n",
      "5. Don't write long lines (we shouldn't have to scroll to see the whole line of code)\n",
      "\n",
      "\n",
      "## Diamonds Data\n",
      "\n",
      "This data set contains the prices and other attributes of almost 54,000 diamonds. This dataset is available on Github in the [2014_data repository](https://github.com/cs109/2014_data) and is called `diamonds.csv`.  \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reading in the diamonds data (CSV file) from the web\n",
      "\n",
      "This is a `.csv` file, so we will use the function `read_csv()` that will read in a CSV file into a pandas DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url = 'https://raw.githubusercontent.com/cs109/2014_data/master/diamonds.csv'\n",
      "diamonds = pd.read_csv(url, sep = ',', index_col=0)\n",
      "diamonds.head()"
     ],
     "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>carat</th>\n",
        "      <th>cut</th>\n",
        "      <th>color</th>\n",
        "      <th>clarity</th>\n",
        "      <th>depth</th>\n",
        "      <th>table</th>\n",
        "      <th>price</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>   Ideal</td>\n",
        "      <td> E</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 61.5</td>\n",
        "      <td> 55</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.95</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> 2.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.21</td>\n",
        "      <td> Premium</td>\n",
        "      <td> E</td>\n",
        "      <td> SI1</td>\n",
        "      <td> 59.8</td>\n",
        "      <td> 61</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.89</td>\n",
        "      <td> 3.84</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>    Good</td>\n",
        "      <td> E</td>\n",
        "      <td> VS1</td>\n",
        "      <td> 56.9</td>\n",
        "      <td> 65</td>\n",
        "      <td> 327</td>\n",
        "      <td> 4.05</td>\n",
        "      <td> 4.07</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.29</td>\n",
        "      <td> Premium</td>\n",
        "      <td> I</td>\n",
        "      <td> VS2</td>\n",
        "      <td> 62.4</td>\n",
        "      <td> 58</td>\n",
        "      <td> 334</td>\n",
        "      <td> 4.20</td>\n",
        "      <td> 4.23</td>\n",
        "      <td> 2.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 0.31</td>\n",
        "      <td>    Good</td>\n",
        "      <td> J</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 63.3</td>\n",
        "      <td> 58</td>\n",
        "      <td> 335</td>\n",
        "      <td> 4.34</td>\n",
        "      <td> 4.35</td>\n",
        "      <td> 2.75</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "   carat      cut color clarity  depth  table  price     x     y     z\n",
        "1   0.23    Ideal     E     SI2   61.5     55    326  3.95  3.98  2.43\n",
        "2   0.21  Premium     E     SI1   59.8     61    326  3.89  3.84  2.31\n",
        "3   0.23     Good     E     VS1   56.9     65    327  4.05  4.07  2.31\n",
        "4   0.29  Premium     I     VS2   62.4     58    334  4.20  4.23  2.63\n",
        "5   0.31     Good     J     SI2   63.3     58    335  4.34  4.35  2.75"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a table containing a description of all the column names. \n",
      "\n",
      "Column name | Description \n",
      "--- | --- \n",
      "carat | weight of the diamond (0.2\u20135.01)\n",
      "cut | quality of the cut (Fair, Good, Very Good, Premium, Ideal)\n",
      "colour | diamond colour, from J (worst) to D (best)\n",
      "clarity | a measurement of how clear the diamond is (I1 (worst), SI1, SI2, VS1, VS2, VVS1, VVS2, IF (best))\n",
      "depth | total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43\u201379)\n",
      "table | width of top of diamond relative to widest point (43\u201395)\n",
      "price | price in US dollars (\\$326\u2013\\$18,823)\n",
      "x | length in mm (0\u201310.74)\n",
      "y | width in mm (0\u201358.9)\n",
      "z | depth in mm (0\u201331.8)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploratory Data Analysis (EDA)\n",
      "\n",
      "The variables `carat` and `price` are both continuous variables, while `color` and `clarity` are discrete variables. First, let's look at some summary statistics of the diamonds data set. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.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>carat</th>\n",
        "      <th>depth</th>\n",
        "      <th>table</th>\n",
        "      <th>price</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "      <td> 53940.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>     0.797940</td>\n",
        "      <td>    61.749405</td>\n",
        "      <td>    57.457184</td>\n",
        "      <td>  3932.799722</td>\n",
        "      <td>     5.731157</td>\n",
        "      <td>     5.734526</td>\n",
        "      <td>     3.538734</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>     0.474011</td>\n",
        "      <td>     1.432621</td>\n",
        "      <td>     2.234491</td>\n",
        "      <td>  3989.439738</td>\n",
        "      <td>     1.121761</td>\n",
        "      <td>     1.142135</td>\n",
        "      <td>     0.705699</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>     0.200000</td>\n",
        "      <td>    43.000000</td>\n",
        "      <td>    43.000000</td>\n",
        "      <td>   326.000000</td>\n",
        "      <td>     0.000000</td>\n",
        "      <td>     0.000000</td>\n",
        "      <td>     0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>     0.400000</td>\n",
        "      <td>    61.000000</td>\n",
        "      <td>    56.000000</td>\n",
        "      <td>   950.000000</td>\n",
        "      <td>     4.710000</td>\n",
        "      <td>     4.720000</td>\n",
        "      <td>     2.910000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>     0.700000</td>\n",
        "      <td>    61.800000</td>\n",
        "      <td>    57.000000</td>\n",
        "      <td>  2401.000000</td>\n",
        "      <td>     5.700000</td>\n",
        "      <td>     5.710000</td>\n",
        "      <td>     3.530000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>     1.040000</td>\n",
        "      <td>    62.500000</td>\n",
        "      <td>    59.000000</td>\n",
        "      <td>  5324.250000</td>\n",
        "      <td>     6.540000</td>\n",
        "      <td>     6.540000</td>\n",
        "      <td>     4.040000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>     5.010000</td>\n",
        "      <td>    79.000000</td>\n",
        "      <td>    95.000000</td>\n",
        "      <td> 18823.000000</td>\n",
        "      <td>    10.740000</td>\n",
        "      <td>    58.900000</td>\n",
        "      <td>    31.800000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "              carat         depth         table         price             x  \\\n",
        "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
        "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
        "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
        "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
        "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
        "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
        "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
        "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
        "\n",
        "                  y             z  \n",
        "count  53940.000000  53940.000000  \n",
        "mean       5.734526      3.538734  \n",
        "std        1.142135      0.705699  \n",
        "min        0.000000      0.000000  \n",
        "25%        4.720000      2.910000  \n",
        "50%        5.710000      3.530000  \n",
        "75%        6.540000      4.040000  \n",
        "max       58.900000     31.800000  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at the distribution of carats and price using a histogram."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds['price'].hist(bins=50, color = 'black')\n",
      "plt.title('Distribution of Price')\n",
      "plt.xlabel('Price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0x107793b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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EyxBxWOm9997DzJkzYTR+f5BhNBqRn58f+IIvKCgYUJ2IiLQr4pHDUOORAxFR7GQfOfAm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\nCpsDERGpsDkQEZHK/wBqxNPN3IAcewAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107614e90>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try changing bin size from 20 to 500. What do you notice? \n",
      "diamonds['carat'].hist(bins=20, color = 'black', figsize=(6, 4))\n",
      "plt.title('Distribution of weights in carats')\n",
      "plt.xlim(0, 3)\n",
      "plt.xlabel('Weight in Carats')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x10810bf90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/nxosDkREpMDiQKSSzPfKy5wNkD+fGiwORESkwOJApJLM49YyZwPkz6cGiwMR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\nREQKLA5ERKTwf1PVYNB17RDwAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104567fd0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the density of the price of the diamonds"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds['price'].plot(kind='kde', color = 'black')\n",
      "plt.title('Distribution of Price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x10831fdd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bt2/LFoDqv7pO8QHlBerw4cMyJSIiLaixQO3fvx9Lly7FunXrAJSvzff+++8r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\nkFigiIhISP8LsUSja1bZCdYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108013110>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, let's look at the relationship between the price of a diamond and its weight in carats. Try changing alpha (ranges from 0 to 1) to control over plotting. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.plot(x='carat', y='price', kind = 'scatter', color = 'black', alpha = 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10a1cb1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eYcaMGXjw4AEYhsHu3buxbt06ODk5cfNOBAKB0vrwhtjER0h59ATCA3xtRy3r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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1034bfdd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also create a scatter plot using matplotlib.pyplot instead of pandas directly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(diamonds['carat'], diamonds['price'], color = 'black', alpha = 0.05)\n",
      "plt.xlabel('Carat')\n",
      "plt.ylabel('Price')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0x10ace49d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HgkMq3LdaLSwuLmJjYwPXrl3D8vIyTyOkAvxOU/yog6xQKKBYLLIKvVAoDNnU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\nA5Ik4dprr4UgCJb/e5jLx5PPISyEeCj+hRXfbD4+H8GwYM7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UhRIIB3iuo/IcO0DxW43i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\nCCGkLP8HMh5qyA2d3hEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108b2a990>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at the scatter plots of `price` and `carat` but grouped by color.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.groupby('color').plot(x='carat', y='price', kind = 'scatter', color = 'black', alpha = 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "color\n",
        "D        Axes(0.125,0.125;0.775x0.775)\n",
        "E        Axes(0.125,0.125;0.775x0.775)\n",
        "F        Axes(0.125,0.125;0.775x0.775)\n",
        "G        Axes(0.125,0.125;0.775x0.775)\n",
        "H        Axes(0.125,0.125;0.775x0.775)\n",
        "I        Axes(0.125,0.125;0.775x0.775)\n",
        "J        Axes(0.125,0.125;0.775x0.775)\n",
        "dtype: object"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+krLNwMRwOBzG5GNubg5fX1+MGjUKW7ZsAUVRWLx4MerXr6/4BSxDgwYNYGFh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a1cb650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zE0+fPoWDg4PKJmIEgiYQxcIAmGynVST7jh07aLWzxGIx4uPj8fDhQwDypqYm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\ni8LCQv2s9oEyMzMxf/58/Sx7Z2dnZGVlITAwcNCxxpZd6T/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       "text": [
        "<matplotlib.figure.Figure at 0x1090c3990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fD6AsMevnn3+mlEO5qULe8W+V4OBgxkaX1Fb2JUuWQFdXF8HBwdDU1MT69eth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\niKyKiYWIiKyKiYWIiKzq/wFxrUgcXHs3bQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a85c190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VCgC8ffsW69atk3gPgiCwZ88e/P7771i9erXEL/r79+9TlAoA/PvvvwCqXKv/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\nISIil2JhISIil/o/DSfRpmJSZ/cAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa0be50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nZyfxvJrlWWTh/v37aNeuHcaPH49evXqJJCrWlNXIyAgtWrSgVl4VFRUICQkR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\npFhYiIhIUiwsREQkKRYWIiKS1P8BNM5XNWaw4/YAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa3b950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xA1OnTkVgYKDcmvWqBkFaWFhg6tSpChM7pqam4ssvv8SWLVsQGhqKXbt2IT8/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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aadd1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4zlfJ5XKVLSxCdFWjd5GdnR0KCgowfPhwDBs2DPn5+XBwcOCtvLZiuR/VWLEn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\nUAIhhBCiF0oghBBC9EIJhBBCiF7+H/eIYkrv1TJJAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aaf1510>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What happens if you look at the scatter plots of `price` and `carat` but grouped by clarity.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# try here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We could also look at boxplots of the `price` grouped by `color`.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.boxplot('price', by = 'color')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10b8b1ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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NayEmYIcqt6vz5yMrtzvR496tokIcKsQgmjzfiHGgwjpJzhBnMFS5XYXaG1vW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1084d5ad0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## More with pandas\n",
      "\n",
      "Now that we have done some exploratory data analysis by looking at histograms, scatter plots and boxplots let's look more about how to work with the pandas DataFrame itself.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### More summary statistics\n",
      "\n",
      "We just learned about `diamonds.describe()` above, what else can we do? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "carat       0.797940\n",
        "depth      61.749405\n",
        "table      57.457184\n",
        "price    3932.799722\n",
        "x           5.731157\n",
        "y           5.734526\n",
        "z           3.538734\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.corr() # correlation"
     ],
     "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>carat</th>\n",
        "      <th>depth</th>\n",
        "      <th>table</th>\n",
        "      <th>price</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>carat</th>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.028224</td>\n",
        "      <td> 0.181618</td>\n",
        "      <td> 0.921591</td>\n",
        "      <td> 0.975094</td>\n",
        "      <td> 0.951722</td>\n",
        "      <td> 0.953387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>depth</th>\n",
        "      <td> 0.028224</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td>-0.295779</td>\n",
        "      <td>-0.010647</td>\n",
        "      <td>-0.025289</td>\n",
        "      <td>-0.029341</td>\n",
        "      <td> 0.094924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>table</th>\n",
        "      <td> 0.181618</td>\n",
        "      <td>-0.295779</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.127134</td>\n",
        "      <td> 0.195344</td>\n",
        "      <td> 0.183760</td>\n",
        "      <td> 0.150929</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>price</th>\n",
        "      <td> 0.921591</td>\n",
        "      <td>-0.010647</td>\n",
        "      <td> 0.127134</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.884435</td>\n",
        "      <td> 0.865421</td>\n",
        "      <td> 0.861249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>x</th>\n",
        "      <td> 0.975094</td>\n",
        "      <td>-0.025289</td>\n",
        "      <td> 0.195344</td>\n",
        "      <td> 0.884435</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.974701</td>\n",
        "      <td> 0.970772</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>y</th>\n",
        "      <td> 0.951722</td>\n",
        "      <td>-0.029341</td>\n",
        "      <td> 0.183760</td>\n",
        "      <td> 0.865421</td>\n",
        "      <td> 0.974701</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.952006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>z</th>\n",
        "      <td> 0.953387</td>\n",
        "      <td> 0.094924</td>\n",
        "      <td> 0.150929</td>\n",
        "      <td> 0.861249</td>\n",
        "      <td> 0.970772</td>\n",
        "      <td> 0.952006</td>\n",
        "      <td> 1.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "          carat     depth     table     price         x         y         z\n",
        "carat  1.000000  0.028224  0.181618  0.921591  0.975094  0.951722  0.953387\n",
        "depth  0.028224  1.000000 -0.295779 -0.010647 -0.025289 -0.029341  0.094924\n",
        "table  0.181618 -0.295779  1.000000  0.127134  0.195344  0.183760  0.150929\n",
        "price  0.921591 -0.010647  0.127134  1.000000  0.884435  0.865421  0.861249\n",
        "x      0.975094 -0.025289  0.195344  0.884435  1.000000  0.974701  0.970772\n",
        "y      0.951722 -0.029341  0.183760  0.865421  0.974701  1.000000  0.952006\n",
        "z      0.953387  0.094924  0.150929  0.861249  0.970772  0.952006  1.000000"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.var() # variance"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "carat           0.224687\n",
        "depth           2.052404\n",
        "table           4.992948\n",
        "price    15915629.424301\n",
        "x               1.258347\n",
        "y               1.304472\n",
        "z               0.498011\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diamonds.sort('price', ascending = True, inplace = False).head() # sorting"
     ],
     "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>carat</th>\n",
        "      <th>cut</th>\n",
        "      <th>color</th>\n",
        "      <th>clarity</th>\n",
        "      <th>depth</th>\n",
        "      <th>table</th>\n",
        "      <th>price</th>\n",
        "      <th>x</th>\n",
        "      <th>y</th>\n",
        "      <th>z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>   Ideal</td>\n",
        "      <td> E</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 61.5</td>\n",
        "      <td> 55</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.95</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> 2.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 0.21</td>\n",
        "      <td> Premium</td>\n",
        "      <td> E</td>\n",
        "      <td> SI1</td>\n",
        "      <td> 59.8</td>\n",
        "      <td> 61</td>\n",
        "      <td> 326</td>\n",
        "      <td> 3.89</td>\n",
        "      <td> 3.84</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 0.23</td>\n",
        "      <td>    Good</td>\n",
        "      <td> E</td>\n",
        "      <td> VS1</td>\n",
        "      <td> 56.9</td>\n",
        "      <td> 65</td>\n",
        "      <td> 327</td>\n",
        "      <td> 4.05</td>\n",
        "      <td> 4.07</td>\n",
        "      <td> 2.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 0.29</td>\n",
        "      <td> Premium</td>\n",
        "      <td> I</td>\n",
        "      <td> VS2</td>\n",
        "      <td> 62.4</td>\n",
        "      <td> 58</td>\n",
        "      <td> 334</td>\n",
        "      <td> 4.20</td>\n",
        "      <td> 4.23</td>\n",
        "      <td> 2.63</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 0.31</td>\n",
        "      <td>    Good</td>\n",
        "      <td> J</td>\n",
        "      <td> SI2</td>\n",
        "      <td> 63.3</td>\n",
        "      <td> 58</td>\n",
        "      <td> 335</td>\n",
        "      <td> 4.34</td>\n",
        "      <td> 4.35</td>\n",
        "      <td> 2.75</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "   carat      cut color clarity  depth  table  price     x     y     z\n",
        "1   0.23    Ideal     E     SI2   61.5     55    326  3.95  3.98  2.43\n",
        "2   0.21  Premium     E     SI1   59.8     61    326  3.89  3.84  2.31\n",
        "3   0.23     Good     E     VS1   56.9     65    327  4.05  4.07  2.31\n",
        "4   0.29  Premium     I     VS2   62.4     58    334  4.20  4.23  2.63\n",
        "5   0.31     Good     J     SI2   63.3     58    335  4.34  4.35  2.75"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Accessing functions for a panda DataFrame"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "subtable = diamonds.iloc[0:2, 0:2]\n",
      "print \"subtable\"\n",
      "print subtable\n",
      "print \"\"\n",
      "\n",
      "column = diamonds['color']\n",
      "print \"head of the color column\"\n",
      "print column.head()\n",
      "print \"\"\n",
      "\n",
      "row = diamonds.ix[1:2] #row 1 and 2\n",
      "print \"row\"\n",
      "print row\n",
      "print \"\"\n",
      "\n",
      "rows = diamonds.ix[:3] # all the rows before 3\n",
      "print \"rows\"\n",
      "print rows\n",
      "print \"\"\n",
      "\n",
      "color = diamonds.ix[1,'color']\n",
      "print \"color of diamond in row 1\"\n",
      "print color\n",
      "print \"\"\n",
      "\n",
      "# max along column\n",
      "print \"max price %g\" % diamonds['price'].max() \n",
      "print \"\"\n",
      "\n",
      "# axes\n",
      "print \"axes\"\n",
      "print diamonds.axes\n",
      "print \"\"\n",
      "\n",
      "row = diamonds.ix[1]\n",
      "print \"row info\"\n",
      "print row.name\n",
      "print row.index\n",
      "print \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "subtable\n",
        "   carat      cut\n",
        "1   0.23    Ideal\n",
        "2   0.21  Premium\n",
        "\n",
        "head of the color column\n",
        "1    E\n",
        "2    E\n",
        "3    E\n",
        "4    I\n",
        "5    J\n",
        "Name: color, dtype: object\n",
        "\n",
        "row\n",
        "   carat      cut color clarity  depth  table  price     x     y     z\n",
        "1   0.23    Ideal     E     SI2   61.5     55    326  3.95  3.98  2.43\n",
        "2   0.21  Premium     E     SI1   59.8     61    326  3.89  3.84  2.31\n",
        "\n",
        "rows\n",
        "   carat      cut color clarity  depth  table  price     x     y     z\n",
        "1   0.23    Ideal     E     SI2   61.5     55    326  3.95  3.98  2.43\n",
        "2   0.21  Premium     E     SI1   59.8     61    326  3.89  3.84  2.31\n",
        "3   0.23     Good     E     VS1   56.9     65    327  4.05  4.07  2.31\n",
        "\n",
        "color of diamond in row 1\n",
        "E\n",
        "\n",
        "max price 18823\n",
        "\n",
        "axes\n",
        "[Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, ...], dtype='int64'), Index([u'carat', u'cut', u'color', u'clarity', u'depth', u'table', u'price', u'x', u'y', u'z'], dtype='object')]\n",
        "\n",
        "row info\n",
        "1\n",
        "Index([u'carat', u'cut', u'color', u'clarity', u'depth', u'table', u'price', u'x', u'y', u'z'], dtype='object')\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Defining your own functions\n",
      "\n",
      "New functions can be defined using one of the 31 keywords in Python: `def`.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def squared(x):\n",
      "    \"\"\" Return the square of a  \n",
      "        value \"\"\"\n",
      "    return x ** 2\n",
      "\n",
      "squared(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "16"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The first line of the function (the header) must start with the keyword `def`, the name of the function (which can contain underscores), parentheses (with any arguments inside of it) and a colon.  The arguments can be specified in any order. \n",
      "\n",
      "The rest of the function (the body) always has an indentation of four spaces.  If you define a function in the interactive mode, the interpreter will print ellipses (...) to let you know the function isn't complete. To complete the function, enter an empty line (not necessary in a script).  \n",
      "\n",
      "To return a value from a function, use `return`. The function will immediately terminate and not run any code written past this point.\n",
      "\n",
      "#### The docstring\n",
      "When defining new functions, you can add a `docstring` (i.e. the documentation of function) at the beginning of the function that documents what the function does. The docstring is a triple quoted (multi-line) string.  We highly recommend you to document the functions you define as good python coding practice. \n",
      "\n",
      "#### Lambda functions\n",
      "Lambda functions are one-line functions. To define this function using the `lambda` keyword, you do not need to include the `return` argument.  For example, we can re-write the `squared()` function above using the following syntax:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = lambda x: x**2\n",
      "f(4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "16"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## For loops and while loops\n",
      "\n",
      "#### For loops\n",
      "Defining a `for` loop is similar to defining a new function. The header ends with a colon and the body is indented with four spaces. The function `range(n)` takes in an integer n and creates a set of values from 0 to n - 1.  `for` loops are not just for counters, but they can iterate through many types of objects such as strings, lists and dictionaries. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    print 'Hello world!'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello world!\n",
        "Hello world!\n",
        "Hello world!\n",
        "Hello world!\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To traverse through all characters in a given string, you can use `for` or `while` loops. Here we create the names of the duck statues in the Public Gardens in downtown Boston: Jack, Kack, Lack, Mack, Nack, Oack, Pack, Qack. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "prefixes = 'JKLMNOPQ'\n",
      "suffix = 'ack'\n",
      "for letter in prefixes:\n",
      "    print letter + suffix"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Jack\n",
        "Kack\n",
        "Lack\n",
        "Mack\n",
        "Nack\n",
        "Oack\n",
        "Pack\n",
        "Qack\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### while loops\n",
      "Defining a `while` loop is again similar to defining a `for` loop or new function. The header ends with a colon and the body is indented with four spaces. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def countdown(n):\n",
      "    while n > 0:\n",
      "        print n\n",
      "        n = n-1\n",
      "    print 'Blastoff!'\n",
      "\n",
      "countdown(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3\n",
        "2\n",
        "1\n",
        "Blastoff!\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### List comprehensions\n",
      "Another powerful feature of Python is **list comprehension** which maps one list onto another list and applying a function to each element.  Here, we take each element in the list `a` (temporarily assigning it the value i) and square each element in the list. This creates a new list and does not modify `a`.  In the second line, we can add a conditional statements of only squaring the elements if the element is not equal to 10."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = [5, 10, 15, 20]\n",
      "b = [i**2 for i in a]\n",
      "c = [i**2 for i in a if i != 10]\n",
      "\n",
      "print \"a: \", a\n",
      "print \"b: \", b\n",
      "print \"c: \", c"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "a:  [5, 10, 15, 20]\n",
        "b:  [25, 100, 225, 400]\n",
        "c:  [25, 225, 400]\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    }
   ],
   "metadata": {}
  }
 ]
}