{
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 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = [1,2,3]\n",
      "print a\n",
      "#list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 2, 3]\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'yellow' + '_' + 'orange'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "'yellow_orange'"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = 2/5\n",
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "0"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "float(2)/float(5)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "0.4"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "\n",
      "tweet = {\n",
      "    'date': datetime.datetime(2014, 4, 1, 23, 14, 20),\n",
      "    'user': 899110,\n",
      "    'text': '<3 OMG BEST FRIENDS ARE THE BEST #bestiesforlife #oclove',\n",
      "    'location' : {\n",
      "        'lat': 27.0,\n",
      "        'lng': 114.9,\n",
      "        'hex': 'Efabe3'\n",
      "    }\n",
      "}\n",
      "#dictionary\n",
      "#datetime"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print tweet['text']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<3 OMG BEST FRIENDS ARE THE BEST #bestiesforlife #oclove\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "some_values = [100,107.7,92]\n",
      "print sum(some_values)/len(some_values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "99.9\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mean(arg):\n",
      "    return sum(arg) / len(arg)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mean(some_values)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "99.89999999999999"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "a = np.array([[1, 2, 3], [4, 5, 6]])\n",
      "b = np.array([[1, 2, 3], [4, 5, 6]])\n",
      "c = np.array([[1, 2], [3, 4], [5,6]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a*b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([[ 1,  4,  9],\n",
        "       [16, 25, 36]])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "help(np.dot)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on built-in function dot in module numpy.core._dotblas:\n",
        "\n",
        "dot(...)\n",
        "    dot(a, b, out=None)\n",
        "    \n",
        "    Dot product of two arrays.\n",
        "    \n",
        "    For 2-D arrays it is equivalent to matrix multiplication, and for 1-D\n",
        "    arrays to inner product of vectors (without complex conjugation). For\n",
        "    N dimensions it is a sum product over the last axis of `a` and\n",
        "    the second-to-last of `b`::\n",
        "    \n",
        "        dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])\n",
        "    \n",
        "    Parameters\n",
        "    ----------\n",
        "    a : array_like\n",
        "        First argument.\n",
        "    b : array_like\n",
        "        Second argument.\n",
        "    out : ndarray, optional\n",
        "        Output argument. This must have the exact kind that would be returned\n",
        "        if it was not used. In particular, it must have the right type, must be\n",
        "        C-contiguous, and its dtype must be the dtype that would be returned\n",
        "        for `dot(a,b)`. This is a performance feature. Therefore, if these\n",
        "        conditions are not met, an exception is raised, instead of attempting\n",
        "        to be flexible.\n",
        "    \n",
        "    Returns\n",
        "    -------\n",
        "    output : ndarray\n",
        "        Returns the dot product of `a` and `b`.  If `a` and `b` are both\n",
        "        scalars or both 1-D arrays then a scalar is returned; otherwise\n",
        "        an array is returned.\n",
        "        If `out` is given, then it is returned.\n",
        "    \n",
        "    Raises\n",
        "    ------\n",
        "    ValueError\n",
        "        If the last dimension of `a` is not the same size as\n",
        "        the second-to-last dimension of `b`.\n",
        "    \n",
        "    See Also\n",
        "    --------\n",
        "    vdot : Complex-conjugating dot product.\n",
        "    tensordot : Sum products over arbitrary axes.\n",
        "    einsum : Einstein summation convention.\n",
        "    \n",
        "    Examples\n",
        "    --------\n",
        "    >>> np.dot(3, 4)\n",
        "    12\n",
        "    \n",
        "    Neither argument is complex-conjugated:\n",
        "    \n",
        "    >>> np.dot([2j, 3j], [2j, 3j])\n",
        "    (-13+0j)\n",
        "    \n",
        "    For 2-D arrays it's the matrix product:\n",
        "    \n",
        "    >>> a = [[1, 0], [0, 1]]\n",
        "    >>> b = [[4, 1], [2, 2]]\n",
        "    >>> np.dot(a, b)\n",
        "    array([[4, 1],\n",
        "           [2, 2]])\n",
        "    \n",
        "    >>> a = np.arange(3*4*5*6).reshape((3,4,5,6))\n",
        "    >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))\n",
        "    >>> np.dot(a, b)[2,3,2,1,2,2]\n",
        "    499128\n",
        "    >>> sum(a[2,3,2,:] * b[1,2,:,2])\n",
        "    499128\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.dot(b)\n",
      "#The number of columns in a must match the number of rows in b for matrix multiplication"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "objects are not aligned",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-4-9a1f5761fa9d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mValueError\u001b[0m: objects are not aligned"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot(a,b)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "objects are not aligned",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-5-71281829ef00>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mValueError\u001b[0m: objects are not aligned"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a.dot(c)\n",
      "#The number of columns in a matches the number of rows in c for matrix multiplication\n",
      "# = [[1*1 + 2*3 + 3*5 = 22, 1*2 + 2*4 + 3*6 = 28], \n",
      "#    [4*1, 5*3, 6*5 = 49, 4*2 + 5*4 + 6*6 = 64]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "array([[22, 28],\n",
        "       [49, 64]])"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot(a,c)\n",
      "#yes it's the same because a is the first matrix using either notation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "array([[22, 28],\n",
        "       [49, 64]])"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.dot(c,a)\n",
      "#not the same because the result will have the same number of rows as the 1st matrix\n",
      "#and the same number of columns as the 2nd matrix:\n",
      "#above we are multiplying a 2x3 with 3x2 = 2x2\n",
      "#and here is a 3x2 with 2x3 = 3x3\n",
      "# = [[1*1 + 2*4 = 9, 1*2 + 2*5 = 12, 1*3 + 2*6 + 15],\n",
      "#    [3*1 + 4*4 = 19, 3*2 + 4*5 + 26, 3*3 + 4*6 + 33],\n",
      "#    [5*1 + 6*4 = 29, 5*2 + 6*5 = 40, 5*3 + 6*6 = 51]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([[ 9, 12, 15],\n",
        "       [19, 26, 33],\n",
        "       [29, 40, 51]])"
       ]
      }
     ],
     "prompt_number": 37
    }
   ],
   "metadata": {}
  }
 ]
}