{
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  "signature": "sha256:21bfd4abb703448639b25206a5628f035b2d4cc7b8db829c46bef05937fabc52"
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 "nbformat": 3,
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 "worksheets": [
  {
   "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 lab last week 09/12/2014\n",
      "\n",
      "Previously discussed: \n",
      "\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"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Today, we will discuss the following:\n",
      "* Brief introduction to Numpy, Scipy\n",
      "    * Vectorizing functions\n",
      "* More pandas and matplotlib\n",
      "* Working in the command line\n",
      "* Overview of git and Github\n",
      "\n",
      "<a href=https://raw.githubusercontent.com/cs109/2014/master/labs/Lab3_Notes.ipynb download=Lab3_Notes.ipynb> Download this notebook from Github </a>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Numpy\n",
      "\n",
      "NumPy and SciPy are modules in Python for scientific computing.  [NumPy](http://www.numpy.org) lets you do fast, vectorized operations on arrays.  Why use this module?  \n",
      "\n",
      "* It gives you the performance of using low-level code (e.g. C or Fortran) with the benefit of writing the code in an interpreted scripting language (all while keeping the native Python code). \n",
      "* It gives you a fast, memory-efficient multidimensional array called `ndarray` which allows you perform vectorized operations on (and supports mathematical functions such as linear algebra and random number generation)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import NumPy\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To create a fast, multidimensional `ndarray` object, use the `np.array()` method on a python `list` or `tuple` or reading data from files. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array([1,2,3,4])\n",
      "y = np.array([[1,2], [3,4]])\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([[1, 2],\n",
        "       [3, 4]])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "numpy.ndarray"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Properties of NumPy arrays\n",
      "There are a set of properties about the `ndarray` object such the dimensions, the size, etc.  \n",
      "\n",
      "Property | Description\n",
      "--- | ----\n",
      "`y.shape` (or `shape(y)` | Shape or dimension of the array\n",
      "`y.size` (or `size(y)`) | Number of elements in the array \n",
      "`y.ndim` | number of dimensions \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(4,)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "(2, 2)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Other ways to generate NumPy arrays\n",
      "\n",
      "Function | Description\n",
      "--- | ---\n",
      "`np.arange(start,stop,step)` | Create a range between the start and stop arguments\n",
      "`np.linspace(start,stop,num)` | Create a range between start and stop (both ends included) of length num\n",
      "`np.logspace(start, stop,num,base)` | Create a range in the log space with a define base of length num\n",
      "`np.eye(n)` | Generate an n x n identity matrix"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(0, 21, 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try it: Create a numpy array from 0 to 20 in steps of size 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try it: Create a numpy array from -10 to 10 in steps of 0.5 (INCLUDING the number 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try it: Create a numpy array from 100 to 1000 of length 10"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In addition, the `numpy.random` module can be used to create arrays using a random number generation "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import random"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Function | Description\n",
      "--- | ---\n",
      "`np.random.randint(a, b, N)` | Generate N random integers between a and b\n",
      "`np.random.rand(n, m)` | Generate uniform random numbers in [0,1] of dim n x m\n",
      "`np.random.randn(n, m)` | Generate standard normal random numbers of dim n x m\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.randint(1, 100, 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([87, 29, 97, 35, 66, 84, 50, 93,  1, 10, 83, 56, 20, 49, 41, 58, 43,\n",
        "       60, 46, 98, 47, 91, 73, 73, 90, 26, 18,  3, 62, 65, 27, 58, 19, 49,\n",
        "       13,  5, 14, 16, 48, 38, 90, 19, 85, 61, 36, 38, 64,  6,  9, 97])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Try it: Create a numpy array filled with random samples \n",
      "# from a normal distribution of size 4 x 4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Reshaping, resizing and stacking NumPy arrays\n",
      "\n",
      "To reshape an array, use `reshape()`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z = np.random.rand(4,4)\n",
      "z "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "array([[ 0.34961451,  0.75618943,  0.85774252,  0.29423465],\n",
        "       [ 0.72196235,  0.02541357,  0.7708488 ,  0.07240782],\n",
        "       [ 0.54376752,  0.41193452,  0.40132359,  0.63399867],\n",
        "       [ 0.12622657,  0.34662246,  0.27813886,  0.95162428]])"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(4, 4)"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z.reshape((8,2)) # dim is now 8 x 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "array([[ 0.34961451,  0.75618943],\n",
        "       [ 0.85774252,  0.29423465],\n",
        "       [ 0.72196235,  0.02541357],\n",
        "       [ 0.7708488 ,  0.07240782],\n",
        "       [ 0.54376752,  0.41193452],\n",
        "       [ 0.40132359,  0.63399867],\n",
        "       [ 0.12622657,  0.34662246],\n",
        "       [ 0.27813886,  0.95162428]])"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To flatten an array (convert a higher dimensional array into a vector), use `flatten()`"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "z.flatten()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([ 0.34961451,  0.75618943,  0.85774252,  0.29423465,  0.72196235,\n",
        "        0.02541357,  0.7708488 ,  0.07240782,  0.54376752,  0.41193452,\n",
        "        0.40132359,  0.63399867,  0.12622657,  0.34662246,  0.27813886,\n",
        "        0.95162428])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Operating on NumPy arrays\n",
      "\n",
      "#### Assigning values\n",
      "To assign values to a specific element in a `ndarray`, use the assignment operator. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.array([[1,2], [3,4]])\n",
      "y.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "(2, 2)"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y[0,0] = 10\n",
      "y "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([[10,  2],\n",
        "       [ 3,  4]])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Indexing and slicing arrays\n",
      "To extract elements of the NumPy arrays, use the bracket operator and the slice (i.e. colon) operator.  To slice specific elements in the array, use `dat[lower:upper:step]`. To extract the diagonal (and subdiagonal) elements, use `diag()`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      " # random samples from a uniform distribution between 0 and 1\n",
      "dat = np.random.rand(4,4)\n",
      "dat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([[ 0.60679169,  0.36100824,  0.18275644,  0.56561955],\n",
        "       [ 0.36584042,  0.12087577,  0.14576369,  0.21879333],\n",
        "       [ 0.27301492,  0.64171746,  0.62002836,  0.83744579],\n",
        "       [ 0.30159074,  0.71813527,  0.94443425,  0.19098029]])"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat[0, :] # row 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([ 0.60679169,  0.36100824,  0.18275644,  0.56561955])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat[:, 0] # column 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([ 0.60679169,  0.36584042,  0.27301492,  0.30159074])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dat[0:3:2, 0] # first and third elements in column 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([ 0.60679169,  0.27301492])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.diag(dat) # diagonal"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([ 0.60679169,  0.12087577,  0.62002836,  0.19098029])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.arange(32).reshape((8, 4)) # returns an 8 x 4 array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "array([[ 0,  1,  2,  3],\n",
        "       [ 4,  5,  6,  7],\n",
        "       [ 8,  9, 10, 11],\n",
        "       [12, 13, 14, 15],\n",
        "       [16, 17, 18, 19],\n",
        "       [20, 21, 22, 23],\n",
        "       [24, 25, 26, 27],\n",
        "       [28, 29, 30, 31]])"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x[0] # returns the first row"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Element-wise transformations on arrays\n",
      "There are many vectorized wrappers that take in one scalar and produce one ore more scalars (e.g. `np.exp()`, `np.sqrt()`). This element-wise array methods are also known as NumPy `ufuncs`. \n",
      "\n",
      "Function | Description \n",
      "--- | --- \n",
      "`np.abs(x)` | absolute value of each element\n",
      "`np.sqrt(x)` | square root of each element\n",
      "`np.square(x)` | square of each element\n",
      "`np.exp(x)` | exponential of each element\n",
      "`np.maximum(x, y)` | element-wise maximum from two arrays x and y\n",
      "`np.minimum(x,y)` | element-wise minimum\n",
      "`np.sign(x)` | compute the sign of each element: 1 (pos), 0 (zero), -1 (neg)\n",
      "`np.subtract(x, y)` | subtract elements in y from elements in x\n",
      "`np.power(x, y)` | raise elements in first array x to powers in second array y\n",
      "`np.where(cond, x, y)` | ifelse statement\n",
      "\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Vectorizing functions\n",
      "\n",
      "It is important to state again that you should avoid looping through elements in vectors if at all possible.  One way to get around that when writing functions is to use what are called **vectorized functions**.  Say you wrote a function `f` which accepts some input `x` and checks if `x` is bigger or smaller than 0.  \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    if x >=0:\n",
      "        return True\n",
      "    else:\n",
      "        return False\n",
      "\n",
      "print f(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we give the function an array instead of just one value (e.g. 3), then Python will give an error because there is more than one element in `x`.  The way to get around this is to **vectorize** the function.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_vec = np.vectorize(f)\n",
      "z = np.arange(-5, 6)\n",
      "z "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f_vec(z)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([False, False, False, False, False,  True,  True,  True,  True,\n",
        "        True,  True], dtype=bool)"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Instead of vectorizing the function, you can also make the function itself aware that it will be accepting vectors from the beginning. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def f(x):\n",
      "    return (x >=0)\n",
      "\n",
      "print f(3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Scipy"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that you know a little bit about [NumPy](numpy.html) and SciPy is a collection of mathematical and scientific modules built on top of NumPy.  For example, SciPy can handle multidimensional arrays, integration, linear algebra, statistics and optimization.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Import SciPy\n",
      "import scipy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "SciPy includes most of NumPy, so importing SciPy should be generally OK. The main SciPy module is made up of many [submodules containing specialized topics](http://docs.scipy.org/doc/scipy/reference/). \n",
      "\n",
      "Favorite SciPy submodules | What does it contain? \n",
      "--- | --- \n",
      "`scipy.stats` | [statistics](http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html): random variables, probability density functions, cumulative distribution functions, survival functions\n",
      "`scipy.integrate` | [integration](http://docs.scipy.org/doc/scipy/reference/tutorial/integrate.html): single, double, triple integration, trapezoidal rule, Simpson's rule, differential equation solvers\n",
      "`scipy.signal` | [signal processing tools](http://docs.scipy.org/doc/scipy/reference/signal.html): signal processing tools such as wavelets, spectral densities, filters, B-splines\n",
      "`scipy.optimize` | [optimization](http://docs.scipy.org/doc/scipy/reference/optimize.html): find roots, curve fitting, least squares, etc \n",
      "`scipy.special` | [special functions](http://docs.scipy.org/doc/scipy/reference/tutorial/special.html): very specialized functions in mathematical physics e.g. bessel, gamma\n",
      "`scipy.linalg` | [linear algebra](http://docs.scipy.org/doc/scipy/reference/linalg.html): inverse of a matrix, determinant, Kronecker product, eigenvalue decomposition, SVD, functions for matrices (beyond those in `numpy.linalg`)\n",
      "\n",
      "If you want to import a SciPy submodule (e.g. the statistics submodule `scipy.stats`), use "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy import stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### scipy.stats \n",
      "Let's dive a bit deeper in `scipy.stats`. The real utility of this submodule is to access probability distributions functions (pdfs) and standard statistical tests (e.g. $t$-test).  \n",
      "\n",
      "#### Probability distribution functions\n",
      "There is a large collection of [continuous and discrete pdfs](http://docs.scipy.org/doc/scipy/reference/stats.html) in the `scipy.stats` submodule.  The syntax to simulate random variables from a specific pdf is the name of the distribution  followed by `.rvs`. To generate $n$=10 $N(0,1)$ random variables, "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import norm\n",
      "x = norm.rvs(loc = 0, scale = 1, size = 1000)\n",
      "plt.hist(x)\n",
      "plt.title('Histogram of 1000 normal random variables')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.text.Text at 0x1089af590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Izz77LMrKypCUlAQhBDIzM5GSkmJrrwG3XqT98MMPcerUKQghsGLFCvj7+6tydUTVs8m4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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087f1610>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# More Pandas and Matplotlib\n",
      "\n",
      "## Motor Trend Car Road Tests Data\n",
      "\n",
      "The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973\u201374 models). This dataset is available on Github in the [2014_data repository](https://github.com/cs109/2014_data) and is called `mtcars.csv`. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reading in the mtcars 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/mtcars.csv'\n",
      "mtcars = pd.read_csv(url, sep = ',', index_col=0)\n",
      "mtcars.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>mpg</th>\n",
        "      <th>cyl</th>\n",
        "      <th>disp</th>\n",
        "      <th>hp</th>\n",
        "      <th>drat</th>\n",
        "      <th>wt</th>\n",
        "      <th>qsec</th>\n",
        "      <th>vs</th>\n",
        "      <th>am</th>\n",
        "      <th>gear</th>\n",
        "      <th>carb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Mazda RX4</th>\n",
        "      <td> 21.0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 160</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.90</td>\n",
        "      <td> 2.620</td>\n",
        "      <td> 16.46</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Mazda RX4 Wag</th>\n",
        "      <td> 21.0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 160</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.90</td>\n",
        "      <td> 2.875</td>\n",
        "      <td> 17.02</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Datsun 710</th>\n",
        "      <td> 22.8</td>\n",
        "      <td> 4</td>\n",
        "      <td> 108</td>\n",
        "      <td>  93</td>\n",
        "      <td> 3.85</td>\n",
        "      <td> 2.320</td>\n",
        "      <td> 18.61</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hornet 4 Drive</th>\n",
        "      <td> 21.4</td>\n",
        "      <td> 6</td>\n",
        "      <td> 258</td>\n",
        "      <td> 110</td>\n",
        "      <td> 3.08</td>\n",
        "      <td> 3.215</td>\n",
        "      <td> 19.44</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Hornet Sportabout</th>\n",
        "      <td> 18.7</td>\n",
        "      <td> 8</td>\n",
        "      <td> 360</td>\n",
        "      <td> 175</td>\n",
        "      <td> 3.15</td>\n",
        "      <td> 3.440</td>\n",
        "      <td> 17.02</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "                    mpg  cyl  disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
        "Mazda RX4          21.0    6   160  110  3.90  2.620  16.46   0   1     4   \n",
        "Mazda RX4 Wag      21.0    6   160  110  3.90  2.875  17.02   0   1     4   \n",
        "Datsun 710         22.8    4   108   93  3.85  2.320  18.61   1   1     4   \n",
        "Hornet 4 Drive     21.4    6   258  110  3.08  3.215  19.44   1   0     3   \n",
        "Hornet Sportabout  18.7    8   360  175  3.15  3.440  17.02   0   0     3   \n",
        "\n",
        "                   carb  \n",
        "Mazda RX4             4  \n",
        "Mazda RX4 Wag         4  \n",
        "Datsun 710            1  \n",
        "Hornet 4 Drive        1  \n",
        "Hornet Sportabout     2  "
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# DataFrame with 32 observations on 11 variables\n",
      "mtcars.shape "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "(32, 11)"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# return the column names\n",
      "mtcars.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "Index([u'mpg', u'cyl', u'disp', u'hp', u'drat', u'wt', u'qsec', u'vs', u'am', u'gear', u'carb'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here is a table containing a description of all the column names. \n",
      "\n",
      "Column name | Description \n",
      "--- | --- \n",
      "mpg | Miles/(US) gallon\n",
      "cyl | Number of cylinders\n",
      "disp | Displacement (cu.in.)\n",
      "hp | Gross horsepower\n",
      "drat | Rear axle ratio\n",
      "wt | Weight (lb/1000)\n",
      "qsec | 1/4 mile time\n",
      "vs | V/S\n",
      "am | Transmission (0 = automatic, 1 = manual)\n",
      "gear | Number of forward gears\n",
      "carb | Number of carburetors\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# return the actual data inside the panadas data frame\n",
      "mtcars.values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "array([[  21.   ,    6.   ,  160.   ,  110.   ,    3.9  ,    2.62 ,\n",
        "          16.46 ,    0.   ,    1.   ,    4.   ,    4.   ],\n",
        "       [  21.   ,    6.   ,  160.   ,  110.   ,    3.9  ,    2.875,\n",
        "          17.02 ,    0.   ,    1.   ,    4.   ,    4.   ],\n",
        "       [  22.8  ,    4.   ,  108.   ,   93.   ,    3.85 ,    2.32 ,\n",
        "          18.61 ,    1.   ,    1.   ,    4.   ,    1.   ],\n",
        "       [  21.4  ,    6.   ,  258.   ,  110.   ,    3.08 ,    3.215,\n",
        "          19.44 ,    1.   ,    0.   ,    3.   ,    1.   ],\n",
        "       [  18.7  ,    8.   ,  360.   ,  175.   ,    3.15 ,    3.44 ,\n",
        "          17.02 ,    0.   ,    0.   ,    3.   ,    2.   ],\n",
        "       [  18.1  ,    6.   ,  225.   ,  105.   ,    2.76 ,    3.46 ,\n",
        "          20.22 ,    1.   ,    0.   ,    3.   ,    1.   ],\n",
        "       [  14.3  ,    8.   ,  360.   ,  245.   ,    3.21 ,    3.57 ,\n",
        "          15.84 ,    0.   ,    0.   ,    3.   ,    4.   ],\n",
        "       [  24.4  ,    4.   ,  146.7  ,   62.   ,    3.69 ,    3.19 ,\n",
        "          20.   ,    1.   ,    0.   ,    4.   ,    2.   ],\n",
        "       [  22.8  ,    4.   ,  140.8  ,   95.   ,    3.92 ,    3.15 ,\n",
        "          22.9  ,    1.   ,    0.   ,    4.   ,    2.   ],\n",
        "       [  19.2  ,    6.   ,  167.6  ,  123.   ,    3.92 ,    3.44 ,\n",
        "          18.3  ,    1.   ,    0.   ,    4.   ,    4.   ],\n",
        "       [  17.8  ,    6.   ,  167.6  ,  123.   ,    3.92 ,    3.44 ,\n",
        "          18.9  ,    1.   ,    0.   ,    4.   ,    4.   ],\n",
        "       [  16.4  ,    8.   ,  275.8  ,  180.   ,    3.07 ,    4.07 ,\n",
        "          17.4  ,    0.   ,    0.   ,    3.   ,    3.   ],\n",
        "       [  17.3  ,    8.   ,  275.8  ,  180.   ,    3.07 ,    3.73 ,\n",
        "          17.6  ,    0.   ,    0.   ,    3.   ,    3.   ],\n",
        "       [  15.2  ,    8.   ,  275.8  ,  180.   ,    3.07 ,    3.78 ,\n",
        "          18.   ,    0.   ,    0.   ,    3.   ,    3.   ],\n",
        "       [  10.4  ,    8.   ,  472.   ,  205.   ,    2.93 ,    5.25 ,\n",
        "          17.98 ,    0.   ,    0.   ,    3.   ,    4.   ],\n",
        "       [  10.4  ,    8.   ,  460.   ,  215.   ,    3.   ,    5.424,\n",
        "          17.82 ,    0.   ,    0.   ,    3.   ,    4.   ],\n",
        "       [  14.7  ,    8.   ,  440.   ,  230.   ,    3.23 ,    5.345,\n",
        "          17.42 ,    0.   ,    0.   ,    3.   ,    4.   ],\n",
        "       [  32.4  ,    4.   ,   78.7  ,   66.   ,    4.08 ,    2.2  ,\n",
        "          19.47 ,    1.   ,    1.   ,    4.   ,    1.   ],\n",
        "       [  30.4  ,    4.   ,   75.7  ,   52.   ,    4.93 ,    1.615,\n",
        "          18.52 ,    1.   ,    1.   ,    4.   ,    2.   ],\n",
        "       [  33.9  ,    4.   ,   71.1  ,   65.   ,    4.22 ,    1.835,\n",
        "          19.9  ,    1.   ,    1.   ,    4.   ,    1.   ],\n",
        "       [  21.5  ,    4.   ,  120.1  ,   97.   ,    3.7  ,    2.465,\n",
        "          20.01 ,    1.   ,    0.   ,    3.   ,    1.   ],\n",
        "       [  15.5  ,    8.   ,  318.   ,  150.   ,    2.76 ,    3.52 ,\n",
        "          16.87 ,    0.   ,    0.   ,    3.   ,    2.   ],\n",
        "       [  15.2  ,    8.   ,  304.   ,  150.   ,    3.15 ,    3.435,\n",
        "          17.3  ,    0.   ,    0.   ,    3.   ,    2.   ],\n",
        "       [  13.3  ,    8.   ,  350.   ,  245.   ,    3.73 ,    3.84 ,\n",
        "          15.41 ,    0.   ,    0.   ,    3.   ,    4.   ],\n",
        "       [  19.2  ,    8.   ,  400.   ,  175.   ,    3.08 ,    3.845,\n",
        "          17.05 ,    0.   ,    0.   ,    3.   ,    2.   ],\n",
        "       [  27.3  ,    4.   ,   79.   ,   66.   ,    4.08 ,    1.935,\n",
        "          18.9  ,    1.   ,    1.   ,    4.   ,    1.   ],\n",
        "       [  26.   ,    4.   ,  120.3  ,   91.   ,    4.43 ,    2.14 ,\n",
        "          16.7  ,    0.   ,    1.   ,    5.   ,    2.   ],\n",
        "       [  30.4  ,    4.   ,   95.1  ,  113.   ,    3.77 ,    1.513,\n",
        "          16.9  ,    1.   ,    1.   ,    5.   ,    2.   ],\n",
        "       [  15.8  ,    8.   ,  351.   ,  264.   ,    4.22 ,    3.17 ,\n",
        "          14.5  ,    0.   ,    1.   ,    5.   ,    4.   ],\n",
        "       [  19.7  ,    6.   ,  145.   ,  175.   ,    3.62 ,    2.77 ,\n",
        "          15.5  ,    0.   ,    1.   ,    5.   ,    6.   ],\n",
        "       [  15.   ,    8.   ,  301.   ,  335.   ,    3.54 ,    3.57 ,\n",
        "          14.6  ,    0.   ,    1.   ,    5.   ,    8.   ],\n",
        "       [  21.4  ,    4.   ,  121.   ,  109.   ,    4.11 ,    2.78 ,\n",
        "          18.6  ,    1.   ,    1.   ,    4.   ,    2.   ]])"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars[25:] # rows 25 to end of data frame"
     ],
     "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>cyl</th>\n",
        "      <th>disp</th>\n",
        "      <th>hp</th>\n",
        "      <th>drat</th>\n",
        "      <th>wt</th>\n",
        "      <th>qsec</th>\n",
        "      <th>vs</th>\n",
        "      <th>am</th>\n",
        "      <th>gear</th>\n",
        "      <th>carb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Fiat X1-9</th>\n",
        "      <td> 27.3</td>\n",
        "      <td> 4</td>\n",
        "      <td>  79.0</td>\n",
        "      <td>  66</td>\n",
        "      <td> 4.08</td>\n",
        "      <td> 1.935</td>\n",
        "      <td> 18.9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Porsche 914-2</th>\n",
        "      <td> 26.0</td>\n",
        "      <td> 4</td>\n",
        "      <td> 120.3</td>\n",
        "      <td>  91</td>\n",
        "      <td> 4.43</td>\n",
        "      <td> 2.140</td>\n",
        "      <td> 16.7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Lotus Europa</th>\n",
        "      <td> 30.4</td>\n",
        "      <td> 4</td>\n",
        "      <td>  95.1</td>\n",
        "      <td> 113</td>\n",
        "      <td> 3.77</td>\n",
        "      <td> 1.513</td>\n",
        "      <td> 16.9</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ford Pantera L</th>\n",
        "      <td> 15.8</td>\n",
        "      <td> 8</td>\n",
        "      <td> 351.0</td>\n",
        "      <td> 264</td>\n",
        "      <td> 4.22</td>\n",
        "      <td> 3.170</td>\n",
        "      <td> 14.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ferrari Dino</th>\n",
        "      <td> 19.7</td>\n",
        "      <td> 6</td>\n",
        "      <td> 145.0</td>\n",
        "      <td> 175</td>\n",
        "      <td> 3.62</td>\n",
        "      <td> 2.770</td>\n",
        "      <td> 15.5</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maserati Bora</th>\n",
        "      <td> 15.0</td>\n",
        "      <td> 8</td>\n",
        "      <td> 301.0</td>\n",
        "      <td> 335</td>\n",
        "      <td> 3.54</td>\n",
        "      <td> 3.570</td>\n",
        "      <td> 14.6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 5</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Volvo 142E</th>\n",
        "      <td> 21.4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 121.0</td>\n",
        "      <td> 109</td>\n",
        "      <td> 4.11</td>\n",
        "      <td> 2.780</td>\n",
        "      <td> 18.6</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "                 mpg  cyl   disp   hp  drat     wt  qsec  vs  am  gear  carb\n",
        "Fiat X1-9       27.3    4   79.0   66  4.08  1.935  18.9   1   1     4     1\n",
        "Porsche 914-2   26.0    4  120.3   91  4.43  2.140  16.7   0   1     5     2\n",
        "Lotus Europa    30.4    4   95.1  113  3.77  1.513  16.9   1   1     5     2\n",
        "Ford Pantera L  15.8    8  351.0  264  4.22  3.170  14.5   0   1     5     4\n",
        "Ferrari Dino    19.7    6  145.0  175  3.62  2.770  15.5   0   1     5     6\n",
        "Maserati Bora   15.0    8  301.0  335  3.54  3.570  14.6   0   1     5     8\n",
        "Volvo 142E      21.4    4  121.0  109  4.11  2.780  18.6   1   1     4     2"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# return index\n",
      "mtcars.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "Index([u'Mazda RX4', u'Mazda RX4 Wag', u'Datsun 710', u'Hornet 4 Drive', u'Hornet Sportabout', u'Valiant', u'Duster 360', u'Merc 240D', u'Merc 230', u'Merc 280', u'Merc 280C', u'Merc 450SE', u'Merc 450SL', u'Merc 450SLC', u'Cadillac Fleetwood', u'Lincoln Continental', u'Chrysler Imperial', u'Fiat 128', u'Honda Civic', u'Toyota Corolla', u'Toyota Corona', u'Dodge Challenger', u'AMC Javelin', u'Camaro Z28', u'Pontiac Firebird', u'Fiat X1-9', u'Porsche 914-2', u'Lotus Europa', u'Ford Pantera L', u'Ferrari Dino', u'Maserati Bora', u'Volvo 142E'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars.ix['Maserati Bora'] # access a row by an index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "mpg      15.00\n",
        "cyl       8.00\n",
        "disp    301.00\n",
        "hp      335.00\n",
        "drat      3.54\n",
        "wt        3.57\n",
        "qsec     14.60\n",
        "vs        0.00\n",
        "am        1.00\n",
        "gear      5.00\n",
        "carb      8.00\n",
        "Name: Maserati Bora, dtype: float64"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# What other methods are available when working with pandas DataFrames?\n",
      "# type 'mtcars.' and then click <TAB>\n",
      "# mtcars.<TAB>\n",
      "\n",
      "# try it here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Exploratory Data Analysis (EDA)\n",
      "\n",
      "Even though they may look like continuous variabes, `cyl`, `vs`, `am`, `gear` and `carb` are integer or categorical variables. First, let's look at some summary statistics of the mtcars data set. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars.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>cyl</th>\n",
        "      <th>disp</th>\n",
        "      <th>hp</th>\n",
        "      <th>drat</th>\n",
        "      <th>wt</th>\n",
        "      <th>qsec</th>\n",
        "      <th>vs</th>\n",
        "      <th>am</th>\n",
        "      <th>gear</th>\n",
        "      <th>carb</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td>  32.000000</td>\n",
        "      <td>  32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.000000</td>\n",
        "      <td> 32.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 20.090625</td>\n",
        "      <td>  6.187500</td>\n",
        "      <td> 230.721875</td>\n",
        "      <td> 146.687500</td>\n",
        "      <td>  3.596563</td>\n",
        "      <td>  3.217250</td>\n",
        "      <td> 17.848750</td>\n",
        "      <td>  0.437500</td>\n",
        "      <td>  0.406250</td>\n",
        "      <td>  3.687500</td>\n",
        "      <td>  2.8125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>  6.026948</td>\n",
        "      <td>  1.785922</td>\n",
        "      <td> 123.938694</td>\n",
        "      <td>  68.562868</td>\n",
        "      <td>  0.534679</td>\n",
        "      <td>  0.978457</td>\n",
        "      <td>  1.786943</td>\n",
        "      <td>  0.504016</td>\n",
        "      <td>  0.498991</td>\n",
        "      <td>  0.737804</td>\n",
        "      <td>  1.6152</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 10.400000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td>  71.100000</td>\n",
        "      <td>  52.000000</td>\n",
        "      <td>  2.760000</td>\n",
        "      <td>  1.513000</td>\n",
        "      <td> 14.500000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td>  1.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 15.425000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td> 120.825000</td>\n",
        "      <td>  96.500000</td>\n",
        "      <td>  3.080000</td>\n",
        "      <td>  2.581250</td>\n",
        "      <td> 16.892500</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  3.000000</td>\n",
        "      <td>  2.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 19.200000</td>\n",
        "      <td>  6.000000</td>\n",
        "      <td> 196.300000</td>\n",
        "      <td> 123.000000</td>\n",
        "      <td>  3.695000</td>\n",
        "      <td>  3.325000</td>\n",
        "      <td> 17.710000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  0.000000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td>  2.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 22.800000</td>\n",
        "      <td>  8.000000</td>\n",
        "      <td> 326.000000</td>\n",
        "      <td> 180.000000</td>\n",
        "      <td>  3.920000</td>\n",
        "      <td>  3.610000</td>\n",
        "      <td> 18.900000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  4.000000</td>\n",
        "      <td>  4.0000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 33.900000</td>\n",
        "      <td>  8.000000</td>\n",
        "      <td> 472.000000</td>\n",
        "      <td> 335.000000</td>\n",
        "      <td>  4.930000</td>\n",
        "      <td>  5.424000</td>\n",
        "      <td> 22.900000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  1.000000</td>\n",
        "      <td>  5.000000</td>\n",
        "      <td>  8.0000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "             mpg        cyl        disp          hp       drat         wt  \\\n",
        "count  32.000000  32.000000   32.000000   32.000000  32.000000  32.000000   \n",
        "mean   20.090625   6.187500  230.721875  146.687500   3.596563   3.217250   \n",
        "std     6.026948   1.785922  123.938694   68.562868   0.534679   0.978457   \n",
        "min    10.400000   4.000000   71.100000   52.000000   2.760000   1.513000   \n",
        "25%    15.425000   4.000000  120.825000   96.500000   3.080000   2.581250   \n",
        "50%    19.200000   6.000000  196.300000  123.000000   3.695000   3.325000   \n",
        "75%    22.800000   8.000000  326.000000  180.000000   3.920000   3.610000   \n",
        "max    33.900000   8.000000  472.000000  335.000000   4.930000   5.424000   \n",
        "\n",
        "            qsec         vs         am       gear     carb  \n",
        "count  32.000000  32.000000  32.000000  32.000000  32.0000  \n",
        "mean   17.848750   0.437500   0.406250   3.687500   2.8125  \n",
        "std     1.786943   0.504016   0.498991   0.737804   1.6152  \n",
        "min    14.500000   0.000000   0.000000   3.000000   1.0000  \n",
        "25%    16.892500   0.000000   0.000000   3.000000   2.0000  \n",
        "50%    17.710000   0.000000   0.000000   4.000000   2.0000  \n",
        "75%    18.900000   1.000000   1.000000   4.000000   4.0000  \n",
        "max    22.900000   1.000000   1.000000   5.000000   8.0000  "
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Using conditional statements\n",
      "\n",
      "To check if `any` or `all` elements in an array meet a certain criteria, use `any()` and `all()`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(mtcars.mpg >= 20).any()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(mtcars > 0).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "mpg      True\n",
        "cyl      True\n",
        "disp     True\n",
        "hp       True\n",
        "drat     True\n",
        "wt       True\n",
        "qsec     True\n",
        "vs      False\n",
        "am      False\n",
        "gear     True\n",
        "carb     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's look at the distribution of `mpg` using a histogram."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mtcars['mpg'].hist()\n",
      "plt.title('Distribution of MPG')\n",
      "plt.xlabel('Miles Per Gallon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 46,
       "text": [
        "<matplotlib.text.Text at 0x108a22550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dOunWW2/VjBkzFBMTI2OMrw0jSaNHj9aUKVPUpk0bXX311X5Du6ysTNOmTdN1\n111X7bmVXn75ZV122WW65557JEl9+/b1vd3P7Xbr0ksv1ahRo/xeR6CfDS4cfL0qADgELREAcAgC\nGwAcgsAGAIcgsAHAIQhsAHAIAhsAHILABgCHILABwCH+H9nv5v9VrucYAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087f14d0>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Relationship between cyl and mpg\n",
      "plt.plot(mtcars.cyl, mtcars.mpg, 'o')\n",
      "plt.xlim(3, 9)\n",
      "plt.xlabel('Cylinders')\n",
      "plt.ylabel('MPG')\n",
      "plt.title('Relationship between cylinders and MPG')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 47,
       "text": [
        "<matplotlib.text.Text at 0x10969b5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TJ05UO5xq1aFDB+WWkqmpqbhz5w58fX1Vjqr6LFmyBNeuXUPt2rUx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\nIjvzvyTZAihEfKubAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109538710>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Relationship between horsepower and mpg\n",
      "plt.plot(mtcars.hp, mtcars.mpg, 'o')\n",
      "plt.xlabel('Horsepower')\n",
      "plt.ylabel('MPG')\n",
      "plt.title('Relationship between horsepower and MPG')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.text.Text at 0x1097bc150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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S6Xq9URvPs5Z9znnGp10yx6YWE4KTeubRh3AxbZnZuosfJGHc8EEOKhERyY5t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\nIaOaUMJ+AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1089ff790>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.tools.plotting import scatter_matrix\n",
      "scatter_matrix(mtcars[['mpg', 'hp', 'cyl']], \n",
      "               figsize = (10, 6), alpha = 1, diagonal='kde')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x109811350>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x10990a910>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x1099899d0>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x1099eacd0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x109c6cc90>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x109cd0b90>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x109d4dc50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x109d89d90>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x109f3ca50>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xNx1lzWasfvs4PqvtgsvFzsz+IiZYjpeWpGOeIfyy9zVOp8Kf8yZiQrTnp2iy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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109693210>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Working on the command line\n",
      "\n",
      "Now we will discuss working on the command line. For this section and the next section on git and GitHub we will use slides from the [Data Science Specialization](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox) course on Coursera.  These slides are available from \n",
      "\n",
      "* [Command line interface](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_03_commandLineInterface) \n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introduction to git and GitHub\n",
      "\n",
      "Next we introduce git and GitHub. For this section we will also use slides from [Data Science Specialization](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox) course on Coursera.  These slides are available from \n",
      "\n",
      "* [Introduction to git](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_04_01_introToGit) \n",
      "* [Github](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_05_github)\n",
      "* [Create a new repo](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_06_01_createNewRepo)\n",
      "* [Fork a repository](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_06_02_forkRepo)\n",
      "* [Basic git commands](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_07_01_basicGitCommands)\n",
      "* [git workflow](https://github.com/DataScienceSpecialization/courses/tree/master/01_DataScientistToolbox/02_07_02_gitWorkflow)\n",
      "\n",
      "Other useful resources for learning git and github: \n",
      "* [Interactive tutorial to learn git (only takes under 15 mins to complete!)](https://try.github.io/levels/1/challenges/1)\n",
      "* [Github guides](https://guides.github.com)\n",
      "* [git - the simple guide](http://rogerdudler.github.io/git-guide/)\n",
      "* [Github Youtube videos](https://www.youtube.com/user/GitHubGuides)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Your turn\n",
      "\n",
      "* If you don't have a github account yet, [register for a github account](https://github.com/join)\n",
      "* Use `git clone` to clone the [CS109 2014 course repository](https://github.com/cs109/2014) on Github\n",
      "* Use `git clone` to clone the [CS109 2014 data repository](https://github.com/cs109/2014_data) on Github\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}