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## Data Analysis With Python.ipynb
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   "cells": [
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     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Data Analysis with Python\n",
      "(A look at Numpy, Pandas and Matplotlib)\n",
      "\n",
      "###Numpy\n",
      "\n",
      "Numpy provides an n-dimensional array object that holds elements of a specific dtype (eg. numpy.float64 or numpy.int32). Most packages directly . Numpy also provides common operations on arrays such as element-wise arithmetic, indexing/slicing, and basic linear algebra (dot product, matrix decompositions, \u2026).\n",
      "\n",
      "Below we show some basic working with numpy arrays:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division  # always use floating point division\n",
      "import numpy as np  # convention, use alias ``np``\n",
      "\n",
      "# a one dimensional array\n",
      "x = np.array([2, 7, 5])\n",
      "print 'x:', x  # print x\n",
      "\n",
      "# a sequence starting from 4 to 12 with a step size of 3\n",
      "y = np.arange(4, 12, 3)\n",
      "print 'y:', y\n",
      "\n",
      "# element-wise operations on arrays\n",
      "print 'x + y:', x + y\n",
      "print 'x / y:', x / y\n",
      "print 'x ^ y:', x ** y  # python uses ** for exponentiation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x: [2 7 5]\n",
        "y: [ 4  7 10]\n",
        "x + y: [ 6 14 15]\n",
        "x / y: [ 0.5  1.   0.5]\n",
        "x ^ y: [     16  823543 9765625]\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Index and slice thearray using square brackets []. Numpy uses Python\u2019s slicing syntax x[start:end:step] where step is the step size which is optional. If you omit start or end it will use the beginning or end, respectively. Python uses exclusive semantics meaning that the element with position end is not included in the result. Indexing can be done either by position or by using a boolean mask:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print x[1]  # second element of x\n",
      "print x[1:3]  # slice of x that includes second and third elements\n",
      "print\n",
      "print x[-2]  # indexing using negative indices - starts from -1\n",
      "print x[-np.array([1, 2])]   # fancy indexing using index array\n",
      "print\n",
      "\n",
      "print x[np.array([False, True, True])]  # indexing using boolean mask"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7\n",
        "[7 5]\n",
        "\n",
        "7\n",
        "[5 7]\n",
        "\n",
        "[7 5]\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Slicing along multiple Dimensions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# reshape sequence to 2d array (=matrix) where rows hold contiguous sequences\n",
      "# then transpose so that columns hold contiguous sub sequences\n",
      "z_temp = np.arange(1, 13).reshape((3,4))\n",
      "print \"z_temp\"\n",
      "print z_temp\n",
      "print\n",
      "\n",
      "# transpose\n",
      "z = z_temp.T\n",
      "print \"z = z_temp.T (transpose of z_temp)\"\n",
      "print z\n",
      "print\n",
      "\n",
      "# slicing along two dimensions\n",
      "a = z[2:4, 1:3]\n",
      "print \"a = z[2:4, 1:3]\"\n",
      "print a\n",
      "print\n",
      "\n",
      "# slicing along 2nd dimension\n",
      "b = z[:, 1:3] \n",
      "print \"b = z[:, 1:3]\"\n",
      "print b\n",
      "print\n",
      "\n",
      "# first column, returns 1d array\n",
      "c = z[:, 0]\n",
      "print \"c = z[:, 0]\"\n",
      "print c # one dimensional\n",
      "print\n",
      "\n",
      "# first column but return 2d array (remember: exclusive semantics)\n",
      "cc = z[:, 0:1]\n",
      "print \"cc = z[:, 0:1]\"\n",
      "print cc  # two dimensional; column vector"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "z_temp\n",
        "[[ 1  2  3  4]\n",
        " [ 5  6  7  8]\n",
        " [ 9 10 11 12]]\n",
        "\n",
        "z = z_temp.T (transpose of z_temp)\n",
        "[[ 1  5  9]\n",
        " [ 2  6 10]\n",
        " [ 3  7 11]\n",
        " [ 4  8 12]]\n",
        "\n",
        "a = z[2:4, 1:3]\n",
        "[[ 7 11]\n",
        " [ 8 12]]\n",
        "\n",
        "b = z[:, 1:3]\n",
        "[[ 5  9]\n",
        " [ 6 10]\n",
        " [ 7 11]\n",
        " [ 8 12]]\n",
        "\n",
        "c = z[:, 0]\n",
        "[1 2 3 4]\n",
        "\n",
        "cc = z[:, 0:1]\n",
        "[[1]\n",
        " [2]\n",
        " [3]\n",
        " [4]]\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Extract information and dimensionality about the array with the following methods"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print z.shape  # number of elements along each axis (=dimension)\n",
      "print z.ndim  # number of dimensions\n",
      "\n",
      "print z[:, 0].ndim  # return first column as 1d array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(4, 3)\n",
        "2\n",
        "1\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Differences Between R and Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python uses 0-based indexing whereas indices in R start from 1\n",
      "\n",
      "Python\n",
      "<code>\n",
      "    x = np.arange(5) \n",
      "    x[0]\n",
      "</code>\n",
      "\n",
      "R\n",
      "<code>\n",
      "     x <- seq(0, 4) \n",
      "     x[1] \n",
      "</code>\n",
      "\n",
      "\n",
      "Python uses exclusive semantics for slicing whereas R uses inclusive semantics\n",
      "\n",
      "Python\n",
      "<code>\n",
      "      x[0:2]  # doesnt include index 2\n",
      "</code>\n",
      "\n",
      "R\n",
      "<code>\n",
      "      x <- seq(0, 4)  # seq has incl semantics\n",
      "      print(x[1:2])  # includes index 2\n",
      "</code>\n",
      "\n",
      "Negative indices have different semantics: in Python they are used to index from the end on an array whereas in R they are used to drop positions:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Pandas\n",
      "As numpy, pandas provides a key data structure: the pandas.DataFrame; as can be inferred from the name it behaves very much like an R data frame. Pandas data frames address three deficiencies of arrays:\n",
      "\n",
      "They hold heterogenous data; each column can have its own numpy.dtype,\n",
      "the axes of a DataFrame are labeled with column names and row indices,\n",
      "and, they account for missing values which this is not directly supported by arrays.\n",
      "\n",
      "Data frames are extremely useful for data munging. They provide a large range of operations such as filter, join, and group-by aggregation.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd  # convention, alias ``pd``\n",
      "\n",
      "# Load car dataset\n",
      "auto = pd.read_csv(\"http://www-bcf.usc.edu/~gareth/ISL/Auto.csv\")\n",
      "auto.head()  # print the first lines"
     ],
     "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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>horsepower</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "      <th>name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 307</td>\n",
        "      <td> 130</td>\n",
        "      <td> 3504</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td> chevrolet chevelle malibu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 15</td>\n",
        "      <td> 8</td>\n",
        "      <td> 350</td>\n",
        "      <td> 165</td>\n",
        "      <td> 3693</td>\n",
        "      <td> 11.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>         buick skylark 320</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 318</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3436</td>\n",
        "      <td> 11.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>        plymouth satellite</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 16</td>\n",
        "      <td> 8</td>\n",
        "      <td> 304</td>\n",
        "      <td> 150</td>\n",
        "      <td> 3433</td>\n",
        "      <td> 12.0</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>             amc rebel sst</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 17</td>\n",
        "      <td> 8</td>\n",
        "      <td> 302</td>\n",
        "      <td> 140</td>\n",
        "      <td> 3449</td>\n",
        "      <td> 10.5</td>\n",
        "      <td> 70</td>\n",
        "      <td> 1</td>\n",
        "      <td>               ford torino</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   mpg  cylinders  displacement horsepower  weight  acceleration  year  \\\n",
        "0   18          8           307        130    3504          12.0    70   \n",
        "1   15          8           350        165    3693          11.5    70   \n",
        "2   18          8           318        150    3436          11.0    70   \n",
        "3   16          8           304        150    3433          12.0    70   \n",
        "4   17          8           302        140    3449          10.5    70   \n",
        "\n",
        "   origin                       name  \n",
        "0       1  chevrolet chevelle malibu  \n",
        "1       1          buick skylark 320  \n",
        "2       1         plymouth satellite  \n",
        "3       1              amc rebel sst  \n",
        "4       1                ford torino  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "auto.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>mpg</th>\n",
        "      <th>cylinders</th>\n",
        "      <th>displacement</th>\n",
        "      <th>weight</th>\n",
        "      <th>acceleration</th>\n",
        "      <th>year</th>\n",
        "      <th>origin</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 397.000000</td>\n",
        "      <td> 397.000000</td>\n",
        "      <td> 397.000000</td>\n",
        "      <td>  397.000000</td>\n",
        "      <td> 397.000000</td>\n",
        "      <td> 397.000000</td>\n",
        "      <td> 397.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>  23.515869</td>\n",
        "      <td>   5.458438</td>\n",
        "      <td> 193.532746</td>\n",
        "      <td> 2970.261965</td>\n",
        "      <td>  15.555668</td>\n",
        "      <td>  75.994962</td>\n",
        "      <td>   1.574307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>   7.825804</td>\n",
        "      <td>   1.701577</td>\n",
        "      <td> 104.379583</td>\n",
        "      <td>  847.904119</td>\n",
        "      <td>   2.749995</td>\n",
        "      <td>   3.690005</td>\n",
        "      <td>   0.802549</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>   9.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td>  68.000000</td>\n",
        "      <td> 1613.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td>  70.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>  17.500000</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td> 104.000000</td>\n",
        "      <td> 2223.000000</td>\n",
        "      <td>  13.800000</td>\n",
        "      <td>  73.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>  23.000000</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td> 146.000000</td>\n",
        "      <td> 2800.000000</td>\n",
        "      <td>  15.500000</td>\n",
        "      <td>  76.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>  29.000000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td> 262.000000</td>\n",
        "      <td> 3609.000000</td>\n",
        "      <td>  17.100000</td>\n",
        "      <td>  79.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>  46.600000</td>\n",
        "      <td>   8.000000</td>\n",
        "      <td> 455.000000</td>\n",
        "      <td> 5140.000000</td>\n",
        "      <td>  24.800000</td>\n",
        "      <td>  82.000000</td>\n",
        "      <td>   3.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "              mpg   cylinders  displacement       weight  acceleration        year      origin\n",
        "count  397.000000  397.000000    397.000000   397.000000    397.000000  397.000000  397.000000\n",
        "mean    23.515869    5.458438    193.532746  2970.261965     15.555668   75.994962    1.574307\n",
        "std      7.825804    1.701577    104.379583   847.904119      2.749995    3.690005    0.802549\n",
        "min      9.000000    3.000000     68.000000  1613.000000      8.000000   70.000000    1.000000\n",
        "25%     17.500000    4.000000    104.000000  2223.000000     13.800000   73.000000    1.000000\n",
        "50%     23.000000    4.000000    146.000000  2800.000000     15.500000   76.000000    1.000000\n",
        "75%     29.000000    8.000000    262.000000  3609.000000     17.100000   79.000000    2.000000\n",
        "max     46.600000    8.000000    455.000000  5140.000000     24.800000   82.000000    3.000000"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Index by column names and create new columns by bucket notation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mpg = auto.mpg  # get mpg column\n",
      "weight = auto['weight']  # get weight column\n",
      "auto['mpg_per_weight'] = mpg / weight\n",
      "\n",
      "print auto[['mpg', 'weight', 'mpg_per_weight']].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "   mpg  weight  mpg_per_weight\n",
        "0   18    3504        0.005137\n",
        "1   15    3693        0.004062\n",
        "2   18    3436        0.005239\n",
        "3   16    3433        0.004661\n",
        "4   17    3449        0.004929\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Columns and rows of a data frame are labeled, to access/manipulate the labels either use pd.DataFrame.columns or pd.DataFrame.index:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(auto.columns)\n",
      "print(auto.index[:10])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'mpg', u'cylinders', u'displacement', u'horsepower', u'weight', u'acceleration', u'year', u'origin', u'name', u'mpg_per_weight'], dtype=object)\n",
        "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "Indexing and slicing work similar as for numpy arrays just that you can also use column and row labels instead of positions:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "auto.ix[0:5, ['weight', 'mpg']]  # select the first 5 rows and two columns weight and mpg"
     ],
     "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>weight</th>\n",
        "      <th>mpg</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 3504</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 3693</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3436</td>\n",
        "      <td> 18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 3433</td>\n",
        "      <td> 16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 3449</td>\n",
        "      <td> 17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 4341</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   weight  mpg\n",
        "0    3504   18\n",
        "1    3693   15\n",
        "2    3436   18\n",
        "3    3433   16\n",
        "4    3449   17\n",
        "5    4341   15"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Difference beteen R and Pandas\n",
      "\n",
      "The major difference between the data frame in R and pandas from a user\u2019s point of view is that pandas uses an object-oriented interface (ie methods) whereas R uses a functional interface:\n",
      "\n",
      "| Function       | Pandas           | R  |\n",
      "| ------------- |:-------------:| -----:|\n",
      "| first rows      | auto.head() | head(auto) |\n",
      "| summary statistics     | auto.describe()      |   summary(auto) |\n",
      "| column access | auto['mpg']      |    `auto$mpg` |\n",
      "| row access      | auto.iloc[0] | auto[1,] |\n",
      "| column deletion    | del auto['mpg']    |   `auto$mpg = NULL` |\n",
      "| unique elements | auto.year.unique()     |    `unique(auto$year)`|\n",
      "| value counts | auto.year.value_counts()   |    `table(auto$year)`|\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Matplotlib\n",
      "Most common plotting libary is Matplotlib\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "data = np.random.randn(500)  # array of 500 random numbers\n",
      "\n",
      "#make a histogram\n",
      "plt.hist(data)\n",
      "plt.ylabel(\"Counts\")\n",
      "plt.title(\"The Gaussian Distribution\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "(array([   7.,   15.,   47.,   76.,   89.,  105.,   75.,   53.,   24.,    9.]),\n",
        " array([-2.57998907, -2.07504116, -1.57009325, -1.06514533, -0.56019742,\n",
        "       -0.05524951,  0.4496984 ,  0.95464631,  1.45959423,  1.96454214,\n",
        "        2.46949005]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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aIactUu/2r2dTpkyH0xYBYBEh0AHAEgQ6AFiCQAcASxDoAGAJAh0ALEGgA4AlCHQAsASB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\nALAEgQ4AliDQAcAS/w/3Inqr3dBLrAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x104d12050>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.random.randn(50)\n",
      "y = np.random.randn(50)\n",
      "\n",
      "plt.plot(x, y, 'bo')  # b for blue, o for circles\n",
      "plt.xlabel(\"x\")\n",
      "plt.ylabel(\"y\")\n",
      "plt.title(\"A scatterplot\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x105bd3610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HjhzxUkXKcvWz04uwsLBWkw5KS0sRHh4usSJyV0NDAxYtWoSnnnoKCxYskF2O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\nPxGRATH8iYgMiOFPRGRADH8iIgP6/zc5Hte1O4twAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105b82510>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Example of specifiying plotting properties\n",
      "pecify color (b for blue, r for red, k for black etc.) and a symbol for the plotting character ('-' for solid lines, '--' for dashed lines, '*' for stars, \u2026)\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = np.arange(11)\n",
      "\n",
      "plt.plot(s, 30 + s ** 2, 'r--')\n",
      "plt.plot(s, s**2.2 - 2*s, 'b-')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10653a090>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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htm0zm24/8QT84hcNfrig5NHsml27djF9+nRycnK47LLLuPPOO3n77berPSckJISQc0xi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\nIHW6xnJtAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107289790>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Box Plots, useful to compare distributions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.boxplot([x, y])  # Pass a list of two arrays to plot them side-by-side\n",
      "plt.title(\"Two box plots, side-by-side\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.text.Text at 0x1071a0f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10728b6d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.boxplot([x, y])  # Pass a list of two arrays to plot them side-by-side\n",
      "plt.title(\"Two box plots, side-by-side\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "##Matplotlib and Pandas\n",
      "\n",
      "Pandas provides a convenience interface to matplotlib, you can create plots by using the pd.DataFrame.plot() method"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create a scatterplot of weight vs \"miles per galone\"\n",
      "auto.plot(x='weight', y='mpg', style='bo')\n",
      "plt.title(\"Scatterplot of weight and mpg\")\n",
      "\n",
      "# create a histogram of \"miles per galone\"\n",
      "plt.figure()\n",
      "auto.hist('mpg')\n",
      "plt.title(\"Histogram of mpg (miles per galone)\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.text.Text at 0x10710de50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BZrNh1qxZuO+++zB79mwcPXoUOp0OQ4YMwdatWxV/a1DqZOzsOVJs2lQiyIBl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\nk7/BM8ZibqrPOFUKN3ioY/0UNWQEOKfcpkrO5cuXoKZmGZY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       "text": [
        "<matplotlib.figure.Figure at 0x1070c1a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x1070c1f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UB/BgxHPPiJkCYbpGTkQUaTgjDxJn5Kxn9HqRfg5GIs7IiYhMxnSN3JgzMaE6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107111250>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Scatter matrixes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import scatter_matrix\n",
      "_ = scatter_matrix(auto[['mpg', 'cylinders', 'displacement']], figsize=(14, 10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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NLi4yHuhCbr31alat+pnAQL+L7lYkxIX06tXr18lJwN/fn9DQnhw7doJu3bo1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\nV5Ys+ZnWrZ31a2HuB41GQ0mJFhsbb4qKTqDVam/6zXfXrl3Qa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       "text": [
        "<matplotlib.figure.Figure at 0x1060dabd0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

## Multiple Regression with Python.ipynb

      
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## Word Frequency.ipynb

      
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