stuartlynn/gist:859846929d4c2f9c666e

## gistfile1.txt
{
 "metadata": {
  "name": "Untitled0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import pandas as pd\nimport csv as csv\nfrom  pymongo import MongoClient\nfrom  pymongo import ASCENDING\nfrom pylab import *\nimport psycopg2\nimport operator\n\n%matplotlib inline\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "ImportError",
       "evalue": "No module named psycopg2",
       "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-37-1f141b9ad7bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m  \u001b[0mpymongo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mASCENDING\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpylab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpsycopg2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mImportError\u001b[0m: No module named psycopg2"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Price vs review dist"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "prices = []\nscores = []\nhelp_scores= []\nhelp_counts= []\nhelpfull_pcs =[]\ncomment_length = []\n\nwith open('all.csv', 'rb') as csvfile:\n    csvreader = csv.reader(csvfile, delimiter='\\t', quotechar='\"')\n    for i, row in enumerate(csvreader):\n        if i%1000000==0:\n            print \"done \"+str(i)\n        helpfull_score = int(row[5])\n        helpfull_count = int(row[6])\n        if helpfull_count == 0:\n            helpfill_pc =0\n        else:\n            helpfull_pc    = helpfull_score*100.0/helpfull_count\n        \n        score  = float(row[7])\n        if not \"unknown\" in row[2]:\n            price = float(row[2])\n        else:\n            price = -1 \n            prices.append(price)\n        scores.append(score)\n        help_scores.append(helpfull_score)\n        help_counts.append(helpfull_count)\n        comment_length.append(len(row[10]))\n        helpfull_pcs.append(helpfull_pc)\n        \n        ",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "done 0\ndone 1000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 2000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 3000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 4000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 5000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 6000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 7000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 8000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 9000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 10000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 11000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 12000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 13000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 14000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 15000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 16000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 17000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 18000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 19000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 20000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 21000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 22000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 23000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 24000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 25000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 26000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 27000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 28000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 29000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 30000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 31000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 32000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 33000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\ndone 34000000"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "title(\"score dist\")\nxlabel(\"score\")\nylabel(\"fraction\")\nhist(scores, normed=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": "(array([ 0.19795436,  0.        ,  0.12909958,  0.        ,  0.        ,\n        0.20847761,  0.        ,  0.47216604,  0.        ,  1.4923024 ]),\n array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZM2eqsLBQR48e1ZIlS3T//fe7eneNN/TTnTq9oZ/l5eUqKyuTJF24cEGrV6+u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\nBgCACcEAADAhGAAAJgQDAMDk/wFvledK90O7gQAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x32fbb4b10>"
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "title(\"prices\")\nhist(prices)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": "(array([        0.,         0.,         0.,         0.,         0.,\n        21550106.,         0.,         0.,         0.,         0.]),\n array([-1.5, -1.4, -1.3, -1.2, -1.1, -1. , -0.9, -0.8, -0.7, -0.6, -0.5]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEKCAYAAAAVaT4rAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAMf8HyRzPzpRdV8FAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x32fbad090>"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "less_1 = [ c[1] for c in zip(scores, comment_length) if c[0] == 1]\nbetween1and10 =[ c[1] for c in zip(scores, comment_length) if  c[0]>1 and c[0] <= 2]\nbetween10and100 =[ c[1] for c in zip(scores, comment_length) if  c[0]>2 and c[0] <= 3]\nover100 =[ c[1] for c in zip(scores, comment_length) if  c[0] >3]\n\nprint \"1 \"+ str(len(less_1)) + \" 10 \" + str(len(between1and10)) + \" 100 \" + str(len(between10and100))+ \" o100 \" + str(len(over100))",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "1 2746559 10 1791219 100 2892566 o100 27256426\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, sharex='col', sharey='row')\nax1.set_title('score=1')\nax1.hist(less_1, log=True)\nax2.set_title('score=2')\nax2.hist(between1and10, log=True)\nax3.set_title('score=3')\nax3.hist(between10and100, log=True)\nax4.set_title('score=4')\nax4.hist(over100, log=True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": "(array([  2.69663520e+07,   2.76639000e+05,   1.15820000e+04,\n         1.35700000e+03,   2.87000000e+02,   1.58000000e+02,\n         2.90000000e+01,   1.40000000e+01,   2.00000000e+00,\n         6.00000000e+00]),\n array([  1.00000000e+00,   3.26090000e+03,   6.52080000e+03,\n         9.78070000e+03,   1.30406000e+04,   1.63005000e+04,\n         1.95604000e+04,   2.28203000e+04,   2.60802000e+04,\n         2.93401000e+04,   3.26000000e+04]),\n <a list of 10 Patch objects>)"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZiZKSEpw+fRqHDx/GJ598gu3bt2PDhg1MUiIiP6C8UN3KlSsBAJs3b8awYcMU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\nAfj/L9WhGPu3iL8AAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x35ec87f90>"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(log(helpfull_pcs), log(comment_length), \"m\")",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": "[<matplotlib.lines.Line2D at 0x31f10b710>]"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": "<matplotlib.figure.Figure at 0x17d293610>"
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "d =  [[0 , 128559 ,  384.6059396852806882 , 440.995751907143],\n     [1 , 282726 ,  502.8750733926133430 , 548.960866526568],\n     [2 , 259253 ,  618.1526462567453414 , 637.974581592498],\n     [3 , 264957 ,  708.8091652607781640 , 722.119724687115],\n     [4 , 236740 ,  787.0292726197516263 , 804.673076670843],\n     [5 , 264036 ,  844.0341203472253784 , 843.987112189355],\n     [6 , 254885 ,  909.6666849755772211 , 899.104760025035],\n     [7 , 295717 ,  962.3528441043294772 , 948.153009615592],\n     [8 , 523864 , 1002.8822843333384237 , 962.416372189590],\n     [9 , 892050 , 1034.0022487528725968 , 946.722931481890],\n     [10 , 892228 , 1009.9741781248739112 , 877.263038934269]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "title(\"Helpfulness score vs comment length\")\nxlabel(\"Hepfulness score\")\nylabel(\"Average comment length\")\nerrorbar( [p[0] for p in d] , [p[2] for p in d], yerr=[p[3] for p in d])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 55,
       "text": "<Container object of 3 artists>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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lp5Ra075oDxmEHGouHoAwSjw5ORlBQUHIzc1FUVERbGxsmhVUU6h48JWDhwy8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\nyqh4EEIIURkVD0IIISr7/2mmbf2LDx5ZAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x17710f210>"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "cons =  [[4 ,   198],\n        [5 ,  323],\n        [9 ,  1319],\n        [7 ,   361],\n        [10 ,  1799],\n        [0 ,    19],\n        [1 ,   117],\n        [8 ,   687],\n        [2 ,   120],\n        [6 ,   242],\n        [3 ,   171]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 81
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "title(\"Cons : helpfullness hist\")\nxlabel(\"helpfulness %ile\")\nylabel(\"no comments\")\nscatter([c[0] for c in cons],[c[1] for c in cons])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 83,
       "text": "<matplotlib.collections.PathCollection at 0x22d75b650>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y9SwWS7UWx7Fjxzhw4AClpaXVNg+R31J5iNRhZWVljBs3AZstiC5d+hEUFE52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\nYprKQ0RETFN5iIiIaSoPEREx7f8DXA4tLrT/KNMAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x2dcb8a490>"
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "pros=  [[ 4 ,   151],\n        [5 ,   266],\n     [9 ,  1555],\n     [7 ,   361],\n    [10 ,  2147],\n    [0 ,    25],\n     [1 ,   118],\n     [8 ,   757],\n     [2 ,   110],\n     [6 ,   232],\n     [3 ,   137]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "title(\"Pros : helpfullness hist\")\nxlabel(\"helpfulness %ile\")\nylabel(\"no comments\")\nscatter([c[0] for c in pros],[c[1] for c in pros])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": "<matplotlib.collections.PathCollection at 0x22d66f690>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ZjIwMfHx8Sl3vzeUhIiK3+uUX6wULFhha3mGbrfLy8sjJyQEgNzeXhIQEgoKC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\ntLEH4PcAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x1b9e0d1d0>"
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "\nwhen_I = [[8 , 24526],\n           [5 , 10340],\n           [9 , 48026],\n           [1 ,  8769],\n           [6 , 10062],\n           [3 ,  9163],\n           [2 ,  8269],\n           [4 ,  8417],\n           [7 , 13084],\n           [10 , 53898],\n           [0 ,  3703]]\n\nall_t =  [[2 , 259253],\n     [3 , 264957],\n     [1 , 282726],\n    [10 , 892228],\n    [ 8 , 523864],\n    [ 9 , 892050],\n    [ 5 , 264036],\n    [ 6 , 254885],\n    [ 7 , 295717],\n    [ 4 , 236740],\n    [ 0 , 128559]]\n\ncons.sort()\nwhen_I.sort()\npros.sort()\nall_t.sort()\n\ntotal_cons = sum([c[1] for c in cons])\ntotal_pros = sum([c[1] for c in pros])\ntotal_when_i = sum([c[1] for c in when_I])\nall_c        = sum([c[1] for c in all_t])\n\nscatter([c[0] for c in pros],[c[1]*1.0/all_t[i][1] for i,c in enumerate(pros)], color=\"red\")\nscatter([c[0] for c in cons],[c[1]*1.0/all_t[i][1] for i,c in enumerate(cons)], color=\"blue\")\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": "<matplotlib.collections.PathCollection at 0x22f17a950>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qaqp/WavVwul0jlnf3NyMrKwsZGVl4Zlnngm3RSIikkkZ6JcWiwUul2vE+5s2\nbRq2rFAooFAoRow7/z0hhOS4c99fsmQJDh06hMOHD+PWW2+F2WzGjBkzAv8lREQUeSJM11xzjThx\n4oQQQoju7m5xzTXXjBizd+9esWzZMv9yRUWFqKysDLpeCCFuvvlmsX///hHvp6WlCQB88cUXX3yF\n8EpLSwtpXx/wSCKQgoICVFdX45FHHkF1dTVWrFgxYkxOTg7a29vR2dmJ2bNno7a2FjU1NQHrOzs7\nodVqoVQq8cEHH6C9vR0Gg2HEuo8ePRpu60REFCSFEEKEU9jb24tVq1bh+PHj0Ol02LlzJ5KSktDd\n3Y21a9eioaEBALB7925s2LABXq8XxcXF2LhxY8D63/72t6isrIRKpYJKpcJPfvIT3HrrrZH7i4mI\nKGhhhwQREV36YnrG9UMPPYTMzEwYjUasXLkSn376abRbCorUBMNY4HA4cNNNNyErKwvz58/Hc889\nF+2WQub1epGdnY3ly5dHu5WQud1u3HHHHcjMzMS8efPQ1NQU7ZZC8sQTTyArKwsLFizA6tWr0d/f\nH+2WArr77ruhVquxYMEC/3vBTiQeD0brP9T9ZkyHRG5uLg4dOoR33nkH6enpeOKJJ6Ld0pgCTTCM\nBSqVCj//+c9x6NAhNDU14Ve/+lVM9Q8Azz77LObNmzfqlXbj3f3334/8/Hy0tbXh3XffRWZmZrRb\nClpnZyd+85vf4MCBA3jvvffg9XrxyiuvRLutgNasWQObzTbsvWAmEo8Xo/Uf6n4zpkPCYrH4J96Z\nTCZ0dXVFuaOxBZpgGAtSUlKwaNEiAMC0adOQmZmJ7u7uKHcVvK6uLlitVtxzzz2ItW9aP/30U7z5\n5pu4++67AQBKpTKmLg2/7LLLoFKpcObMGQwODuLMmTPQaDTRbiugG264ATNnzhz2XjATiceL0foP\ndb8Z0yFxrm3btiE/Pz/abYwp0ATDWNPZ2YmWlhaYTKZotxK0Bx54AE899ZT/f5JY0tHRgSuvvBJr\n1qzBtddei7Vr1+LMmTPRbitol19+OR588EHMmTMHs2fPRlJSEm655ZZotxWyYCYSx4pg9pvj/v8U\ni8WCBQsWjHj96U9/8o/ZtGkTJk+ejNWrV0ex0+DE4lccozl16hTuuOMOPPvss5g2bVq02wnKq6++\niuTkZGRnZ8fcUQQADA4O4sCBA/j+97+PAwcOIDExcVx/1XG+Y8eO4Re/+AU6OzvR3d2NU6dO4Xe/\n+12025JEJlR3AAACF0lEQVRFaiJxLAh2vxn2PImLpbGxMeDvX3rpJVitVuzZs+cidSSPRqOBw+Hw\nLzscDmi12ih2FLqBgQHcfvvt+M53vjPq/Jjx6q233kJ9fT2sVivOnj2Lzz77DHfddRd27NgR7daC\notVqodVqcd111wEA7rjjjpgKif379+OrX/0qrrjiCgDAypUr8dZbb+HOO++McmehUavVcLlcSElJ\nwYkTJ5CcnBztlkIWyn5z3B9JBGKz2fDUU0+hrq4O8fHx0W4nKOdOMPR4PKitrUVBQUG02wqaEALF\nxcWYN28eNmzYEO12QlJRUQGHw4GOjg688soruPnmm2MmIADf+aDU1FQcOXIEAPDGG28gKysryl0F\nLyMjA01NTejr64MQAm+88QbmzZsX7bZC9sVEYACSE4nHs5D3myHNzx5n9Hq9mDNnjli0aJFYtGiR\nWLduXbRbCorVahXp6ekiLS1NVFRURLudkLz55ptCoVAIo9Ho/3ffvXt3tNsKmd1uF8uXL492GyE7\nePCgyMnJEQsXLhS33XabcLvd0W4pJFVVVWLevHli/vz54q677hIejyfaLQVUWFgorrrqKqFSqYRW\nqxXbtm0TH3/8sVi6dKkwGAzCYrGITz75JNptSjq//xdffDHk/SYn0xERkaSY/rqJiIguLIYEERFJ\nYkgQEZEkhgQREUliSBARkSSGBBERSWJIEBGRJIYEERFJ+v8chW+fI9hh/wAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x22d674890>"
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "  when_I = [[8 , 24526],\n           [5 , 10340],\n           [9 , 48026],\n           [1 ,  8769],\n           [6 , 10062],\n           [3 ,  9163],\n           [2 ,  8269],\n           [4 ,  8417],\n           [7 , 13084],\n           [10 , 53898],\n           [0 |  3703]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print(total_cons)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "567144\n"
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "#Review length vs star "
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "star_length= [[ 1 , 552.6087686446932325 , 557.964211873107],\n             [2 , 683.8891375091487976 , 690.273541157660],\n             [3 , 726.4069794777370681 , 754.466997685679],\n             [4 , 703.5496181901053950 , 751.610688561584],\n             [5 , 562.4957655687491971 , 638.929225075966]]",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "bar([c[0] for c in star_length], [c[1] for c in star_length], yerr=[c[2] for c in star_length])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": "<Container object of 5 artists>"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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=\n",
       "text": "<matplotlib.figure.Figure at 0x22d777f90>"
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}