GoingOnVacation.ipynb

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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Linear Programing In Python : Deciding Where to Go on Vacation\n",
      "\n",
      "This iPython Notebook is an Example of Constructing a Linear Program in Python with PULP module.\n",
      "\n",
      "----------\n",
      "**Problem Formulation:**\n",
      "You want to go on vacation, but you have only limited number of days. On top of it, we also want keep the cost at minimum.\n",
      "And the internet offers plenty of options how to decide, so which packages/mix of packages should we select? \n",
      "\n",
      "----------\n",
      "> - Objective: Minimize Cost of Vacation while selecting Optimal Vacation Package\n",
      "> - LP Form: Minimization\n",
      "> - Decision Variables: Binary Variables whether to purchase the package or not.\n",
      "> - Constrains: Limited Number of Vacation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pulp import *\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "import re \n",
      "import matplotlib.pyplot as plt\n",
      "from IPython.display import Image\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Getting the Data**\n",
      ">There are multiple websites that provide full-priced and discount vacation packages.\n",
      "The dataset from this problem was scraped from The Clymb Adventures http://www.theclymb.com/adventures \n",
      "\n",
      "![TheClymb](https://photos-1.dropbox.com/t/2/AAB8eX8O_-HLLEXt482rsjiDDj-Cy-mvF1DZT6MjP5GKVg/12/49846494/png/32x32/1/_/1/2/thclymb.png/ENCbqCYY-KgMIAEoAQ/S5J_1b9un4Uy-zuWdfU7bKaGgVECPSAFNnMgrfDttqA?size=1024x768&size_mode=2)\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Understanding the Dataset**\n",
      ">The dataset contains:\n",
      "> - Final Destination\n",
      "> - Duration of the trip\n",
      "> - Total Cost of the trip\n",
      "> - Short Description of the adventure"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = pd.read_csv('clymb_adventures.csv')\n",
      "data[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>destination</th>\n",
        "      <th>duration</th>\n",
        "      <th>cost</th>\n",
        "      <th>description</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     Baja</td>\n",
        "      <td>  7</td>\n",
        "      <td>  899</td>\n",
        "      <td> Hike Bike and Sea Kayak and more on a Remote P...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>    Nepal</td>\n",
        "      <td> 11</td>\n",
        "      <td>  899</td>\n",
        "      <td> Explore the land and culture of the Himalayas....</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    Spain</td>\n",
        "      <td>  8</td>\n",
        "      <td>  568</td>\n",
        "      <td> Sport climb &amp; deep water solo in Mallorca. Dis...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Yosemite</td>\n",
        "      <td>  5</td>\n",
        "      <td>  750</td>\n",
        "      <td> Guided hiking through stunning high country. E...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>     Utah</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1356</td>\n",
        "      <td> Hike. Canyoneer. Rock Climb. and more!. Experi...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "  destination  duration  cost  \\\n",
        "0        Baja         7   899   \n",
        "1       Nepal        11   899   \n",
        "2       Spain         8   568   \n",
        "3    Yosemite         5   750   \n",
        "4        Utah         6  1356   \n",
        "\n",
        "                                         description  \n",
        "0  Hike Bike and Sea Kayak and more on a Remote P...  \n",
        "1  Explore the land and culture of the Himalayas....  \n",
        "2  Sport climb & deep water solo in Mallorca. Dis...  \n",
        "3  Guided hiking through stunning high country. E...  \n",
        "4  Hike. Canyoneer. Rock Climb. and more!. Experi...  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = plt.subplots(1,2)\n",
      "my_plot = data[['destination', 'cost']].plot(kind='bar', title=\"Destination by Cost\", ax=axs[0])\n",
      "my_plot = data[['destination', 'duration']].plot(kind='bar', title=\"Destination by Duration\", ax=axs[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+rzcnsI9hNDrrga3AIBEZCAwClqfrkmH0pmIN389iDgXmAcNVdZUvWgUM99sj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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d99d650>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Setting Up LP Problem:**\n",
      "\n",
      "> Define The LP Object\n",
      "\n",
      "> The *prob* variable is created to contain the formulation, and the usual parameters are passed into LpProblem."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# create the LP object, set up as a minimization problem --> since we want to minimize the costs \n",
      "prob = pulp.LpProblem('GoingOnVacation', pulp.LpMinimize)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Create Decision Variables:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "decision_variables = []\n",
      "for rownum, row in data.iterrows():\n",
      "\tvariable = str('x' + str(rownum))\n",
      "\tvariable = pulp.LpVariable(str(variable), lowBound = 0, upBound = 1, cat= 'Integer') #make variables binary\n",
      "\tdecision_variables.append(variable)\n",
      "\n",
      "print (\"Total number of decision_variables: \" + str(len(decision_variables)))\n",
      "print (\"Array with Decision Variables:\" + str(decision_variables))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Total number of decision_variables: 42\n",
        "Array with Decision Variables:[x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x20, x21, x22, x23, x24, x25, x26, x27, x28, x29, x30, x31, x32, x33, x34, x35, x36, x37, x38, x39, x40, x41]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Define Objective Function: (*Minimixing the Cost of The Trip*)\n",
      "\n",
      "> The variable prob now begins collecting problem data with the += operator. The objective function is logically entered first, with an important comma , at the end of the statement and a short string explaining what this objective function is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_cost = \"\"\n",
      "for rownum, row in data.iterrows():\n",
      "\tfor i, schedule in enumerate(decision_variables):\n",
      "\t\tif rownum == i:\n",
      "\t\t\tformula = row['cost']*schedule\n",
      "\t\t\ttotal_cost += formula\n",
      "\n",
      "prob += total_cost\n",
      "print (\"Optimization function: \" + str(total_cost))\t"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization function: 899*x0 + 899*x1 + 399*x10 + 385*x11 + 439*x12 + 339*x13 + 399*x14 + 265*x15 + 849*x16 + 1799*x17 + 1799*x18 + 1499*x19 + 568*x2 + 3175*x20 + 599*x21 + 1450*x22 + 399*x23 + 1199*x24 + 2799*x25 + 2675*x26 + 1699*x27 + 599*x28 + 1798*x29 + 750*x3 + 1799*x30 + 1199*x31 + 999*x32 + 1375*x33 + 1199*x34 + 299*x35 + 2898*x36 + 499*x37 + 1499*x38 + 450*x39 + 1356*x4 + 198*x40 + 375*x41 + 680*x5 + 559*x6 + 899*x7 + 1799*x8 + 1625*x9\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> Define Constrains: (*Selected Packages should not exceed total vacation days available*)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "aval_vacation_days = 10\n",
      "total_vacation_days = \"\"\n",
      "for rownum, row in data.iterrows():\n",
      "\tfor i, schedule in enumerate(decision_variables):\n",
      "\t\tif rownum == i:\n",
      "\t\t\tformula = row['duration']*schedule\n",
      "\t\t\ttotal_vacation_days += formula\n",
      "\n",
      "prob += (total_vacation_days == aval_vacation_days)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      ">The Final Format"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print prob\n",
      "prob.writeLP(\"GoingOnVacation.lp\" )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "GoingOnVacation:\n",
        "MINIMIZE\n",
        "899*x0 + 899*x1 + 399*x10 + 385*x11 + 439*x12 + 339*x13 + 399*x14 + 265*x15 + 849*x16 + 1799*x17 + 1799*x18 + 1499*x19 + 568*x2 + 3175*x20 + 599*x21 + 1450*x22 + 399*x23 + 1199*x24 + 2799*x25 + 2675*x26 + 1699*x27 + 599*x28 + 1798*x29 + 750*x3 + 1799*x30 + 1199*x31 + 999*x32 + 1375*x33 + 1199*x34 + 299*x35 + 2898*x36 + 499*x37 + 1499*x38 + 450*x39 + 1356*x4 + 198*x40 + 375*x41 + 680*x5 + 559*x6 + 899*x7 + 1799*x8 + 1625*x9 + 0\n",
        "SUBJECT TO\n",
        "_C1: 7 x0 + 11 x1 + 2 x10 + 2 x11 + 3 x12 + 2 x13 + 3 x14 + 4 x15 + 7 x16\n",
        " + 8 x17 + 10 x18 + 7 x19 + 8 x2 + 13 x20 + 3 x21 + 5 x22 + 2 x23 + 5 x24\n",
        " + 9 x25 + 14 x26 + 8 x27 + 4 x28 + 6 x29 + 5 x3 + 8 x30 + 11 x31 + 8 x32\n",
        " + 8 x33 + 13 x34 + 4 x35 + 6 x36 + 3 x37 + 5 x38 + 4 x39 + 6 x4 + 2 x40\n",
        " + 2 x41 + 4 x5 + 4 x6 + 6 x7 + 10 x8 + 7 x9 = 10\n",
        "\n",
        "VARIABLES\n",
        "0 <= x0 <= 1 Integer\n",
        "0 <= x1 <= 1 Integer\n",
        "0 <= x10 <= 1 Integer\n",
        "0 <= x11 <= 1 Integer\n",
        "0 <= x12 <= 1 Integer\n",
        "0 <= x13 <= 1 Integer\n",
        "0 <= x14 <= 1 Integer\n",
        "0 <= x15 <= 1 Integer\n",
        "0 <= x16 <= 1 Integer\n",
        "0 <= x17 <= 1 Integer\n",
        "0 <= x18 <= 1 Integer\n",
        "0 <= x19 <= 1 Integer\n",
        "0 <= x2 <= 1 Integer\n",
        "0 <= x20 <= 1 Integer\n",
        "0 <= x21 <= 1 Integer\n",
        "0 <= x22 <= 1 Integer\n",
        "0 <= x23 <= 1 Integer\n",
        "0 <= x24 <= 1 Integer\n",
        "0 <= x25 <= 1 Integer\n",
        "0 <= x26 <= 1 Integer\n",
        "0 <= x27 <= 1 Integer\n",
        "0 <= x28 <= 1 Integer\n",
        "0 <= x29 <= 1 Integer\n",
        "0 <= x3 <= 1 Integer\n",
        "0 <= x30 <= 1 Integer\n",
        "0 <= x31 <= 1 Integer\n",
        "0 <= x32 <= 1 Integer\n",
        "0 <= x33 <= 1 Integer\n",
        "0 <= x34 <= 1 Integer\n",
        "0 <= x35 <= 1 Integer\n",
        "0 <= x36 <= 1 Integer\n",
        "0 <= x37 <= 1 Integer\n",
        "0 <= x38 <= 1 Integer\n",
        "0 <= x39 <= 1 Integer\n",
        "0 <= x4 <= 1 Integer\n",
        "0 <= x40 <= 1 Integer\n",
        "0 <= x41 <= 1 Integer\n",
        "0 <= x5 <= 1 Integer\n",
        "0 <= x6 <= 1 Integer\n",
        "0 <= x7 <= 1 Integer\n",
        "0 <= x8 <= 1 Integer\n",
        "0 <= x9 <= 1 Integer\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> The Actual Optimization:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "optimization_result = prob.solve()\n",
      "\n",
      "assert optimization_result == pulp.LpStatusOptimal\n",
      "print(\"Status:\", LpStatus[prob.status])\n",
      "print(\"Optimal Solution to the problem: \", value(prob.objective))\n",
      "print (\"Individual decision_variables: \")\n",
      "for v in prob.variables():\n",
      "\tprint(v.name, \"=\", v.varValue)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Status:', 'Optimal')\n",
        "('Optimal Solution to the problem: ', 762.0)\n",
        "Individual decision_variables: \n",
        "('x0', '=', 0.0)\n",
        "('x1', '=', 0.0)\n",
        "('x10', '=', 0.0)\n",
        "('x11', '=', 0.0)\n",
        "('x12', '=', 0.0)\n",
        "('x13', '=', 0.0)\n",
        "('x14', '=', 0.0)\n",
        "('x15', '=', 1.0)\n",
        "('x16', '=', 0.0)\n",
        "('x17', '=', 0.0)\n",
        "('x18', '=', 0.0)\n",
        "('x19', '=', 0.0)\n",
        "('x2', '=', 0.0)\n",
        "('x20', '=', 0.0)\n",
        "('x21', '=', 0.0)\n",
        "('x22', '=', 0.0)\n",
        "('x23', '=', 0.0)\n",
        "('x24', '=', 0.0)\n",
        "('x25', '=', 0.0)\n",
        "('x26', '=', 0.0)\n",
        "('x27', '=', 0.0)\n",
        "('x28', '=', 0.0)\n",
        "('x29', '=', 0.0)\n",
        "('x3', '=', 0.0)\n",
        "('x30', '=', 0.0)\n",
        "('x31', '=', 0.0)\n",
        "('x32', '=', 0.0)\n",
        "('x33', '=', 0.0)\n",
        "('x34', '=', 0.0)\n",
        "('x35', '=', 1.0)\n",
        "('x36', '=', 0.0)\n",
        "('x37', '=', 0.0)\n",
        "('x38', '=', 0.0)\n",
        "('x39', '=', 0.0)\n",
        "('x4', '=', 0.0)\n",
        "('x40', '=', 1.0)\n",
        "('x41', '=', 0.0)\n",
        "('x5', '=', 0.0)\n",
        "('x6', '=', 0.0)\n",
        "('x7', '=', 0.0)\n",
        "('x8', '=', 0.0)\n",
        "('x9', '=', 0.0)\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> The results are stored \n",
      "> If you don't pass the names to the variables and want to append the decision variables back in your desired file format, you want to loop through variable name object. \n",
      "\n",
      "> Depending on your initial data format you might want to parse the results differently. Since in this example we have used pandas dataframe, we will use the number of the variable as index to append the results back to initial dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variable_name = []\n",
      "variable_value = []\n",
      "\n",
      "for v in prob.variables():\n",
      "\tvariable_name.append(v.name)\n",
      "\tvariable_value.append(v.varValue)\n",
      "\n",
      "df = pd.DataFrame({'variable': variable_name, 'value': variable_value})\n",
      "for rownum, row in df.iterrows():\n",
      "\tvalue = re.findall(r'(\\d+)', row['variable'])\n",
      "\tdf.loc[rownum, 'variable'] = int(value[0])\n",
      "\n",
      "df = df.sort_index(by='variable')\n",
      "\n",
      "#append results\n",
      "for rownum, row in data.iterrows():\n",
      "\tfor results_rownum, results_row in df.iterrows():\n",
      "\t\tif rownum == results_row['variable']:\n",
      "\t\t\tdata.loc[rownum, 'decision'] = results_row['value']\n",
      "            \n",
      "data[:5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>destination</th>\n",
        "      <th>duration</th>\n",
        "      <th>cost</th>\n",
        "      <th>description</th>\n",
        "      <th>decision</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>     Baja</td>\n",
        "      <td>  7</td>\n",
        "      <td>  899</td>\n",
        "      <td> Hike Bike and Sea Kayak and more on a Remote P...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>    Nepal</td>\n",
        "      <td> 11</td>\n",
        "      <td>  899</td>\n",
        "      <td> Explore the land and culture of the Himalayas....</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    Spain</td>\n",
        "      <td>  8</td>\n",
        "      <td>  568</td>\n",
        "      <td> Sport climb &amp; deep water solo in Mallorca. Dis...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Yosemite</td>\n",
        "      <td>  5</td>\n",
        "      <td>  750</td>\n",
        "      <td> Guided hiking through stunning high country. E...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>     Utah</td>\n",
        "      <td>  6</td>\n",
        "      <td> 1356</td>\n",
        "      <td> Hike. Canyoneer. Rock Climb. and more!. Experi...</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "  destination  duration  cost  \\\n",
        "0        Baja         7   899   \n",
        "1       Nepal        11   899   \n",
        "2       Spain         8   568   \n",
        "3    Yosemite         5   750   \n",
        "4        Utah         6  1356   \n",
        "\n",
        "                                         description  decision  \n",
        "0  Hike Bike and Sea Kayak and more on a Remote P...         0  \n",
        "1  Explore the land and culture of the Himalayas....         0  \n",
        "2  Sport climb & deep water solo in Mallorca. Dis...         0  \n",
        "3  Guided hiking through stunning high country. E...         0  \n",
        "4  Hike. Canyoneer. Rock Climb. and more!. Experi...         0  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> The Final Decisions and Results of the Optimization in the \"User Friendly Way\":"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data['decision'] == 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>destination</th>\n",
        "      <th>duration</th>\n",
        "      <th>cost</th>\n",
        "      <th>description</th>\n",
        "      <th>decision</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>  Maine</td>\n",
        "      <td> 4</td>\n",
        "      <td> 265</td>\n",
        "      <td> Ride endless singletrack. Escape to Western Ma...</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td> Oregon</td>\n",
        "      <td> 4</td>\n",
        "      <td> 299</td>\n",
        "      <td> Mountain bike from hut-to-hut on the Mt Hood L...</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td> Oregon</td>\n",
        "      <td> 2</td>\n",
        "      <td> 198</td>\n",
        "      <td> Learn to windsurf in Hood River. Trip for 2!. ...</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "   destination  duration  cost  \\\n",
        "15       Maine         4   265   \n",
        "35      Oregon         4   299   \n",
        "40      Oregon         2   198   \n",
        "\n",
        "                                          description  decision  \n",
        "15  Ride endless singletrack. Escape to Western Ma...         1  \n",
        "35  Mountain bike from hut-to-hut on the Mt Hood L...         1  \n",
        "40  Learn to windsurf in Hood River. Trip for 2!. ...         1  "
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[data['decision'] == 1]['cost'].sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "762"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}