{
 "metadata": {
  "name": "",
  "signature": "sha256:4008a1f8a27854e7c2a82ef53f7618a0368dd20c49bf9d18f8895ecf6d20cd5a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Basic Imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy import *\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Basic Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = arange(0.,10.,0.1) # generate a range of values as an array, using begin, end, step as input \n",
      "y = sin(x)\n",
      "ll = plt.plot(x,y) # this is the simplest plotting idiom\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEZFQ+v9iGlqWq7mXSAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108168550>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Setting Plot Parameters and annotating"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ll = plt.plot(x,y)\n",
      "xl = plt.xlabel('horizontal axis')\n",
      "yl = plt.ylabel('vertical axis')\n",
      "ttl = plt.title('sine function')\n",
      "ax = plt.axis([-2, 12, -1.5, 1.5])\n",
      "grd = plt.grid(True)\n",
      "txt = plt.text(0,1.3,'here is some text')\n",
      "ann = plt.annotate('a point on curve',xy=(4.7,-1),xytext=(3,-1.3),arrowprops=dict(arrowstyle='->'))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108168510>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Multiple Graphs On the Same Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = arange(0.,10,0.1)\n",
      "a = cos(x)\n",
      "b = sin(x)\n",
      "c = exp(x/10)\n",
      "d = exp(-x/10)\n",
      "la = plt.plot(x,a,'b-',label='cosine')\n",
      "lb = plt.plot(x,b,'r--',label='sine')\n",
      "lc = plt.plot(x,c,'gx',label='exp(+x)')\n",
      "ld = plt.plot(x,d,'y-', linewidth = 5,label='exp(-x)')\n",
      "ll = plt.legend(loc='upper left')\n",
      "lx = plt.xlabel('xaxis')\n",
      "ly = plt.ylabel('yaxis')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEPCAYAAABRHfM8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noiiKoiiKoiiKoljL/wPva59Oc+2RHwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1087c1210>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Plotting with multiple plots on a grid"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Details on grid plotting <http://matplotlib.org/users/gridspec.html>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Some neat tricks with legends, bg colors and arrows in labels"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.arange(0,3,.02)\n",
      "b = np.arange(0,3,.02)\n",
      "c = np.exp(a)\n",
      "d = c[::-1]\n",
      "\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.plot(a,c,'k--',a,d,'k:',a,c+d,'k')\n",
      "leg = ax.legend(('Model length', 'Data length', 'Total message length'),\n",
      "           'upper center', shadow=True)\n",
      "ax.set_ylim([-1,20])\n",
      "ax.grid(True)\n",
      "ax.set_xlabel('Model complexity --->')\n",
      "ax.set_ylabel('Message length --->')\n",
      "ax.set_title('Minimum Message Length')\n",
      "\n",
      "ax.set_yticklabels([])\n",
      "ax.set_xticklabels([])\n",
      "\n",
      "# set some legend properties.  All the code below is optional.  The\n",
      "# defaults are usually sensible but if you need more control, this\n",
      "# shows you how\n",
      "\n",
      "# the matplotlib.patches.Rectangle instance surrounding the legend\n",
      "frame  = leg.get_frame()\n",
      "frame.set_facecolor('0.80')    # set the frame face color to light gray\n",
      "\n",
      "# matplotlib.text.Text instances\n",
      "for t in leg.get_texts():\n",
      "    t.set_fontsize('small')    # the legend text fontsize\n",
      "\n",
      "# matplotlib.lines.Line2D instances\n",
      "for l in leg.get_lines():\n",
      "    l.set_linewidth(1.5)  # the legend line width\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAEPCAYAAABbbZ8rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRVEURVEURVEURVEURVEURVEURXF48BeHtr32PbZS2gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1085f8e50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "### Plotting using the OO interface and setting low level parameters"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt #1\n",
      "figsize = (8, 5) #2 \n",
      "fig = plt.figure(figsize=figsize) #3 \n",
      "ax = fig.add_subplot(111) #4 \n",
      "line = ax.plot(range(10))[0] #5 \n",
      "ax.set_title('Plotted with OO interface') #6 \n",
      "ax.set_xlabel('measured') \n",
      "ax.set_ylabel('calculated')\n",
      "ax.grid(True) #7 \n",
      "line.set_marker('o')\n",
      "plt.savefig('oo.png',dpi=150)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAFRCAYAAABOnmU8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4GKODAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1086114d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Basic Pylab Boxplot \n",
      "also see pandas.boxplot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd    \n",
      "from pylab import boxplot as bp\n",
      "plt.figure()\n",
      "loansmin = pd.read_csv('../datasets/loanf.csv')\n",
      "data = loansmin['FICO.Score']\n",
      "arrdata = np.array(data)\n",
      "print(data[1:3])\n",
      "print(\"****\")\n",
      "print(type(arrdata))   \n",
      "    # basic plot\n",
      "b = bp(arrdata)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named scope",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-7-7f4c12b115be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpylab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mboxplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mbp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mloansmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'../datasets/loanf.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloansmin\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'FICO.Score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nitin/anaconda/lib/python2.7/site-packages/pandas/__init__.pyc\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_init\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nitin/anaconda/lib/python2.7/site-packages/pandas/core/api.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotnull\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategorical\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mset_eng_float_format\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mInt64Index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFloat64Index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nitin/anaconda/lib/python2.7/site-packages/pandas/core/groupby.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPandasObject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategorical\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mCategorical\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgeneric\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNDFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/nitin/anaconda/lib/python2.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpressions\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexpressions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0meval\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_eval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscope\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_ensure_scope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpercentile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_quantile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m from pandas.compat import(range, zip, lrange, lmap, lzip, StringIO, u,\n",
        "\u001b[0;31mImportError\u001b[0m: No module named scope"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###References\n",
      "\n",
      "[1] Graphics with Matplotlib <http://physics.nmt.edu/~raymond/software/python_notes/paper004.html>  \n",
      "[2] Details on grid plotting <http://matplotlib.org/users/gridspec.html>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "cannot import name hashtable\n"
       ]
      },
      {
       "ename": "ImportError",
       "evalue": "cannot import name hashtable",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-10-af55e7023913>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/nitin/anaconda/lib/python2.7/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhashtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtslib\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: cannot import name hashtable"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}