python_code_examples.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:006b5a709b716b58326387fb8f06ca30b3510e448bb7bdb2ae3fb35fefffa031"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Simple Data Visualization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "x = linspace(0,2*pi,150)\n",
      "y = exp(sin(x))\n",
      "plot(x,y)\n",
      "show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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K61JgFTDIbZxCKQEsAd52HaSQNgPLsH+Gz9xGiVtlYDKwGvvvp6nbOHGpg/2d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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe18c0b0fd0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#For Loops"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for x in range(9):\n",
      "    print(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "1\n",
        "2\n",
        "3\n",
        "4\n",
        "5\n",
        "6\n",
        "7\n",
        "8\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Creating Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def five_thru_seven(x):\n",
      "    if x<8 and x>4:\n",
      "        return x*2\n",
      "    else:\n",
      "        return x\n",
      "print(five_thru_seven(8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "8\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(five_thru_seven(6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pylab import *\n",
      "x = linspace(0,10,100)\n",
      "y = [five_thru_seven(i) for i in x]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x,y); show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEACAYAAABF+UbAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkiRF8P/j3qUTJo5BoAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe1865ae3d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}