hw1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello this a header\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of x : 100\n",
      "Mean of x : 1.66565914895\n",
      "Standard Deviation of x : 2.94862195594\n",
      "--------\n",
      "sum of x : 166.565914895\n",
      "sum/len : 1.66565914895\n",
      "8.69437143904\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.9486219559374667"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.random.normal(2, 3, 100) # mean, sd, count\n",
    "print 'Length of x : {0}'.format(len(x))\n",
    "print 'Mean of x : {0}'.format(np.mean(x))\n",
    "print 'Standard Deviation of x : {0}'.format(np.std(x))\n",
    "print '--------'\n",
    "print 'sum of x : {0}'.format(np.sum(x))\n",
    "print 'sum/len : {0}'.format(np.sum(x)/len(x))\n",
    "var = np.sum(((x - np.mean(x))**2)/(len(x))) # Variance\n",
    "print var\n",
    "np.sqrt(var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'list'>\n",
      "<type 'numpy.ndarray'>\n",
      "[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361, 400, 441, 484, 529, 576, 625, 676, 729, 784, 841, 900, 961, 1024, 1089, 1156, 1225, 1296, 1369, 1444, 1521, 1600, 1681, 1764, 1849, 1936, 2025, 2116, 2209, 2304, 2401, 2500, 2601, 2704, 2809, 2916, 3025, 3136, 3249, 3364, 3481, 3600, 3721, 3844, 3969, 4096, 4225, 4356, 4489, 4624, 4761, 4900, 5041, 5184, 5329, 5476, 5625, 5776, 5929, 6084, 6241, 6400, 6561, 6724, 6889, 7056, 7225, 7396, 7569, 7744, 7921, 8100, 8281, 8464, 8649, 8836, 9025, 9216, 9409, 9604]\n"
     ]
    }
   ],
   "source": [
    "N = 100\n",
    "x = np.linspace(0, stop=N-1, num=N)\n",
    "y = range(0, N - 1)\n",
    "z = [(i**2) for i in y]\n",
    "print type(y)\n",
    "print type(x)\n",
    "print z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 "nbformat": 4,
 "nbformat_minor": 2
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