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

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy\n",
    "\n",
    "Credits: Forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel\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",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy Arrays, dtypes, and shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "(3,)\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "print(a)\n",
    "print(a.shape)\n",
    "print(a.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 2 4]\n",
      " [1 3 5]]\n",
      "(2, 3)\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[0, 2, 4], [1, 3, 5]])\n",
    "print(b)\n",
    "print(b.shape)\n",
    "print(b.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 1]], dtype=int32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(shape=(3, 4), dtype=np.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common Array Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.   1.   2. ]\n",
      " [ 0.5  1.5  2.5]]\n",
      "(2, 3)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "c = b * 0.5\n",
    "print(c)\n",
    "print(c.shape)\n",
    "print(c.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.   3.   5. ]\n",
      " [ 1.5  3.5  5.5]]\n"
     ]
    }
   ],
   "source": [
    "d = a + c\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  3.,  5.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.5])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.5"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.25"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.5,   6.5,  10.5])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3. ,  3.5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshape and Update In-Place"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "e = np.arange(12)\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# f is a view of contents of e\n",
    "f = e.reshape(3, 4)\n",
    "print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 0 0 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# Set values of e from index 5 onwards to 0\n",
    "e[5:] = 0\n",
    "print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 0, 0, 0],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# f is also updated\n",
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : False\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# OWNDATA shows f does not own its data\n",
    "f.flags"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2, 4],\n",
       "       [1, 3, 5]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  3. ,  5. ],\n",
       "       [ 1.5,  3.5,  5.5]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([a, a, a])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1. ,  2. ,  3. ],\n",
       "       [ 0. ,  2. ,  4. ],\n",
       "       [ 1. ,  3. ,  5. ],\n",
       "       [ 1. ,  3. ,  5. ],\n",
       "       [ 1.5,  3.5,  5.5]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use broadcasting when needed to do this automatically\n",
    "np.vstack([a, b, d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  2. ,  4. ,  1. ,  3. ,  5. ],\n",
       "       [ 1. ,  3. ,  5. ,  1.5,  3.5,  5.5]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In machine learning, useful to enrich or \n",
    "# add new/concatenate features with hstack\n",
    "np.hstack([b, d])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Sample Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pylab as plt\n",
    "import seaborn\n",
    "\n",
    "seaborn.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bac8d50>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create evenly spaced numbers over the specified interval\n",
    "x = np.linspace(0, 2, 10)\n",
    "plt.plot(x, 'o-');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFVCAYAAAA+OJwpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nI1AEuJg4sW+T9722tpb
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bc3a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}