Numpy_Stack.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#How to stack a list to the front of array."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array([[1,2,3],\n",
      "\t[4,5,6],\n",
      "\t[7,8,9]]\n",
      "\t)\n",
      "b = np.array(['a', 'b', 'c'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We want to stack these as thought `b` is the names of the objects whose data are stored in `a`. This would normaly be [`np.hstack`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html),"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.hstack((b,a))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ValueError",
       "evalue": "all the input arrays must have same number of dimensions",
       "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-3-8b42a7a35e11>\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[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/usr/local/lib/python2.7/site-packages/numpy/core/shape_base.pyc\u001b[0m in \u001b[0;36mhstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;31m# As a special case, dimension 0 of 1-dimensional arrays is \"horizontal\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0marrs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mValueError\u001b[0m: all the input arrays must have same number of dimensions"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "but our dimentions are wrong."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b.shape, a.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(3,) (3, 3)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As seen above, `b`, is only one dimentional and from the documentation we need the first dimention the same: the \"rows\". This is where `.shape` is misleading. I don't know much more but we ant the anaswer for above to be `(3,1) (3,3)`.\n",
      "\n",
      "To add a new axis use `[np.newaxis]` and then we can transpose `b` to be the appropriate `shape`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b[np.newaxis].shape, a.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 3) (3, 3)\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print b[np.newaxis].T.shape, a.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(3, 1) (3, 3)\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally `hstack` will work properly:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.hstack((b[np.newaxis].T, a))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([['a', '1', '2', '3'],\n",
        "       ['b', '4', '5', '6'],\n",
        "       ['c', '7', '8', '9']], \n",
        "      dtype='|S1')"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that this does not work if `b` is a structured array. If you have a list or array of `objects`, then you might need to do `np.dsatck((b,a))[0]`. This stacks them along depth, or the 3rd axis. Use `[0]` inorder to get back to a 2D array. "
     ]
    }
   ],
   "metadata": {}
  }
 ]
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