data-science-ipython-notebooks/numpy/numpy.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# NumPy\n",
      "\n",
      "* NumPy Arrays, dtype, and shape\n",
      "* Common Array Operations\n",
      "* Reshape and Update In-Place\n",
      "* Combine Arrays\n",
      "* Create Sample Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## NumPy Arrays, dtypes, and shapes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array([1, 2, 3])\n",
      "print(a)\n",
      "print(a.shape)\n",
      "print(a.dtype)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1 2 3]\n",
        "(3,)\n",
        "int64\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = np.array([[0, 2, 4], [1, 3, 5]])\n",
      "print(b)\n",
      "print(b.shape)\n",
      "print(b.dtype)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[0 2 4]\n",
        " [1 3 5]]\n",
        "(2, 3)\n",
        "int64\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.zeros(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "array([ 0.,  0.,  0.,  0.,  0.])"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.ones(shape=(3, 4), dtype=np.int32)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "array([[1, 1, 1, 1],\n",
        "       [1, 1, 1, 1],\n",
        "       [1, 1, 1, 1]], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Common Array Operations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = b * 0.5\n",
      "print(c)\n",
      "print(c.shape)\n",
      "print(c.dtype)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0.   1.   2. ]\n",
        " [ 0.5  1.5  2.5]]\n",
        "(2, 3)\n",
        "float64\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = a + c\n",
      "print(d)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 1.   3.   5. ]\n",
        " [ 1.5  3.5  5.5]]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([ 1.,  3.,  5.])"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d[0, 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "1.0"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d[:, 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([ 1. ,  1.5])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "19.5"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "3.25"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.sum(axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([  2.5,   6.5,  10.5])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.mean(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "array([ 3. ,  3.5])"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reshape and Update In-Place"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e = np.arange(12)\n",
      "print(e)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# f is a view of contents of e\n",
      "f = e.reshape(3, 4)\n",
      "print(f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0  1  2  3]\n",
        " [ 4  5  6  7]\n",
        " [ 8  9 10 11]]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Set last five values of e to zero\n",
      "e[5:] = 0\n",
      "print(e)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0 1 2 3 4 0 0 0 0 0 0 0]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# f is also updated\n",
      "f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "array([[0, 1, 2, 3],\n",
        "       [4, 0, 0, 0],\n",
        "       [0, 0, 0, 0]])"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# OWNDATA shows f does not own its data\n",
      "f.flags"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "  C_CONTIGUOUS : True\n",
        "  F_CONTIGUOUS : False\n",
        "  OWNDATA : False\n",
        "  WRITEABLE : True\n",
        "  ALIGNED : True\n",
        "  UPDATEIFCOPY : False"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Combine Arrays"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "array([1, 2, 3])"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "array([[0, 2, 4],\n",
        "       [1, 3, 5]])"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array([[ 1. ,  3. ,  5. ],\n",
        "       [ 1.5,  3.5,  5.5]])"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.concatenate([a, a, a])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Use broadcasting when needed to do this automatically\n",
      "np.vstack([a, b, d])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([[ 1. ,  2. ,  3. ],\n",
        "       [ 0. ,  2. ,  4. ],\n",
        "       [ 1. ,  3. ,  5. ],\n",
        "       [ 1. ,  3. ,  5. ],\n",
        "       [ 1.5,  3.5,  5.5]])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# In machine learning, useful to enrich or \n",
      "# add new/concatenate features with hstack\n",
      "np.hstack([b, d])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "array([[ 0. ,  2. ,  4. ,  1. ,  3. ,  5. ],\n",
        "       [ 1. ,  3. ,  5. ,  1.5,  3.5,  5.5]])"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Create Sample Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pylab as plt\n",
      "import seaborn\n",
      "\n",
      "seaborn.set()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create evenly spaced numbers over the specified interval\n",
      "x = np.linspace(0, 2, 10)\n",
      "plt.plot(x, 'o-');\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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        "<matplotlib.figure.Figure at 0x10bc25110>"
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     "prompt_number": 4
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     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.random.normal?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create sample data, add some noise\n",
      "x = np.random.uniform(1, 100, 1000)\n",
      "y = np.log(x) + np.random.normal(0, .3, 1000)\n",
      "\n",
      "plt.scatter(x, y)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAFXCAYAAACGDraSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+D3EY7Otq3euxcUHKShQxxGh
       "text": [
        "<matplotlib.figure.Figure at 0x10bd30110>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}