Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Hector/anaconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quants = [i / 10.0 for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Donations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "donations = pd.read_csv('donations.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many times does a unique donor made a donation?\n",
    "There were a total of 1,282,092 donations. The top two donors made 57,093 and 49,985 donations respectively, almost \n",
    "10% of the total number of individual donations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.282092e+06\n",
       "mean     2.416355e+00\n",
       "std      7.898978e+01\n",
       "min      1.000000e+00\n",
       "25%      1.000000e+00\n",
       "50%      1.000000e+00\n",
       "75%      2.000000e+00\n",
       "max      5.709300e+04\n",
       "Name: donor_acctid, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donations_by_donor_id = donations.donor_acctid.value_counts()\n",
    "donations_by_donor_id.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 30 donors made 10% of total donations.\n"
     ]
    }
   ],
   "source": [
    "top = 30\n",
    "perc_top = donations_by_donor_id.head(top).sum() * 100.0 /donations_by_donor_id.sum()\n",
    "print('Top {0} donors made {1:.0f}% of total donations.'.format(top, perc_top))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "donations_by_donor = donations.groupby('donor_acctid').sum().donation_total.sort_values(ascending=False)\n",
    "top10 = donations_by_donor.head(10).sum()\n",
    "top50 = donations_by_donor.head(50).sum()\n",
    "sum_of_totaldonations = donations.donation_total.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 donors donated 12.08% of total donations, totaling $28.70 million USD.\n",
      "Top 50 donors donated 22.76% of total donations, totaling $54.06 million USD.\n",
      "Total donations were $237.54 million USD.\n"
     ]
    }
   ],
   "source": [
    "print('Top 10 donors donated {0:.2f}% of total donations, totaling ${1:.2f} million USD.'.format(top10/sum_of_totaldonations*100, top10/1000000))\n",
    "print('Top 50 donors donated {0:.2f}% of total donations, totaling ${1:.2f} million USD.'.format(top50/sum_of_totaldonations*100, top50/1000000))\n",
    "print('Total donations were ${0:.2f} million USD.'.format(sum_of_totaldonations / 1000000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of donations by state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x103a61190>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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3oSlfqKpdkxwKVFW9odU/BTgcuGiqTitfDDykqp4zVaeqvp5kY+BnVbV1q7NXVT27zfNf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\n1cgwj3RXZXnRSHvfVFXfGql3CvBndAe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x169231cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8))\n",
    "donations.donor_state.value_counts().plot(kind = 'bar')\n",
    "plt.title('Number of donations by state') \n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Donations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total amount of money by state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1151ff5d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AikcoBgAAQPEIxQAAACgeoRgAAADFIxQDAACgeIRiAAAAFI9QDAAAgOIRigEAAFA8QjEA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\nRygGAABA8QjFAAAAKB6hGAAAAMX7/x3MPssdjFUnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115296310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12,8))\n",
    "donations.groupby('donor_state').sum().donation_total.sort_values(ascending = False).plot(kind = 'bar')\n",
    "plt.title('Total amount of donations by state') \n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Donations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Essays submitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "essays =  pd.read_csv('essays.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    249555.000000\n",
       "mean          2.661129\n",
       "std           5.432593\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max         266.000000\n",
       "Name: teacher_acctid, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many projects does the teachers created\n",
    "essays.teacher_acctid.value_counts().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    1.0\n",
       "0.1    1.0\n",
       "0.2    1.0\n",
       "0.3    1.0\n",
       "0.4    1.0\n",
       "0.5    1.0\n",
       "0.6    2.0\n",
       "0.7    2.0\n",
       "0.8    3.0\n",
       "0.9    5.0\n",
       "Name: teacher_acctid, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "essays.teacher_acctid.value_counts().quantile(quants)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of 249,555 projects, the average teacher created 2 or 3 projects. 50% of the teachers created just one project, while 90% of them created 5 or less. One teacher created 266 projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "essays['essay_length'] = essays.essay.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x16192b3d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iIm6RtA3oioinJU0FXomIWcVj0pz1QC0iHhryM91zcc/FzMaoMj0XSa1AG/B0GrpNUq+k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\nyxwaWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1605c9c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "essays.essay_length.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "outcomes = pd.read_csv('outcomes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at_least_1_teacher_referred_donor has 94398 null values.\n",
      "at_least_1_green_donation has 94398 null values.\n",
      "three_or_more_non_teacher_referred_donors has 94398 null values.\n",
      "one_non_teacher_referred_donor_giving_100_plus has 94398 null values.\n",
      "donation_from_thoughtful_donor has 94398 null values.\n",
      "great_messages_proportion has 179839 null values.\n",
      "teacher_referred_count has 94398 null values.\n",
      "non_teacher_referred_count has 94398 null values.\n"
     ]
    }
   ],
   "source": [
    "# There are several columns with many null values\n",
    "for column in outcomes.columns:\n",
    "    if len(outcomes[column].isnull().value_counts().keys()) > 1:\n",
    "        print('{} has {} null values.'.format(column, outcomes[column].isnull().value_counts()[True]))\n",
    "        \n",
    "# Assume that if null value, answer is False.\n",
    "outcomes = outcomes.fillna(value='f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Replace true with 1 and false with 0 to work better.\n",
    "outcomes = outcomes.replace(to_replace='t', value=1)\n",
    "outcomes = outcomes.replace(to_replace='f', value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the total number of projects, 36,710 were considered \"exciting\" ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    582616\n",
       "1     36710\n",
       "Name: is_exciting, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes.is_exciting.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "378693 projects: at_least_1_green_donation.\n",
      "124660 projects: at_least_1_teacher_referred_donor.\n",
      "430683 projects: fully_funded.\n",
      "187170 projects: great_chat.\n",
      "36710 projects: is_exciting.\n",
      "377809 projects: one_non_teacher_referred_donor_giving_100_plus.\n",
      "41293 projects: teacher_referred_count.\n",
      "272378 projects: three_or_more_non_teacher_referred_donors.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Hector/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:1: FutureWarning: using '+' to provide set union with Indexes is deprecated, use '|' or .union()\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "check_labels = outcomes.columns[1:8] + outcomes.columns[10:11]\n",
    "\n",
    "for column in check_labels:\n",
    "    print('{} projects: {}.'.format(outcomes[column].value_counts()[1], column))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "projectid                                         ffff2d9c769c8fb5335e949c615425ebfffe0bb8af3b9c...\n",
       "is_exciting                                                                                   36710\n",
       "at_least_1_teacher_referred_donor                                                             36710\n",
       "fully_funded                                                                                  36710\n",
       "at_least_1_green_donation                                                                     36710\n",
       "great_chat                                                                                    36710\n",
       "three_or_more_non_teacher_referred_donors                                                     31660\n",
       "one_non_teacher_referred_donor_giving_100_plus                                                27695\n",
       "donation_from_thoughtful_donor                                                                 1057\n",
       "great_messages_proportion                                                               2.86524e+06\n",
       "teacher_referred_count                                                                       112688\n",
       "non_teacher_referred_count                                                                   276271\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes[outcomes['is_exciting'] == 1].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    22338\n",
       "1    13690\n",
       "3      682\n",
       "Name: complies, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seems like they do comply with the requirements to be exciting!\n",
    "outcomes['complies'] = outcomes.three_or_more_non_teacher_referred_donors+outcomes.one_non_teacher_referred_donor_giving_100_plus+outcomes.donation_from_thoughtful_donor\n",
    "outcomes[outcomes['is_exciting'] == 1].complies.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) Projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "projects =  pd.read_csv('projects.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "exciting_projects = outcomes[outcomes.is_exciting == 1].merge(projects, on='projectid', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 15924 unique schools with exciting projects.\n",
      "These are located in 3750 cities accross 51 states (and territories).\n",
      "The city with the highest number of exciting projects is Chicago with 1640 projects.\n"
     ]
    }
   ],
   "source": [
    "unique_schools_with_exciting = exciting_projects.schoolid.unique()\n",
    "unique_cities_with_exciting = exciting_projects.school_city.unique()\n",
    "unique_states_with_exciting = exciting_projects.school_state.unique()\n",
    "city_most_exciting = exciting_projects.school_city.value_counts().head(1)\n",
    "\n",
    "print('There are {} unique schools with exciting projects.'.format(len(unique_schools_with_exciting)))\n",
    "print('These are located in {} cities accross {} states (and territories).'.format(len(unique_cities_with_exciting), len(unique_states_with_exciting)))\n",
    "print('The city with the highest number of exciting projects is {} with {} projects.'.format(city_most_exciting.keys()[0], city_most_exciting[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selected exciting projects seem to be evenly distributed among schools with different poverty levels. Each poverty bracket had around 5.5% of their projects selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high poverty        0.051734\n",
       "highest poverty     0.056067\n",
       "low poverty         0.056669\n",
       "moderate poverty    0.058481\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exciting_projects.groupby('poverty_level').count().is_exciting / projects.poverty_level.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In absolute numbers, however, the poverty bracket with the highest number of exciting projects is the highest poverty one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1c2481250>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UpFHAshExu1UsU6ZMeft9T08PPT09g9gzM7Pu09vbS29v74CWHfLReCWdCTwfEYcUyn5I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1146a6490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exciting_projects.groupby('poverty_level').count().is_exciting.ix[\n",
    "    ['highest poverty', 'high poverty', 'moderate poverty', 'low poverty']].plot(kind='bar')\n",
    "plt.title('Exciting projects by poverty bracket') \n",
    "plt.xlabel('Poverty bracket')\n",
    "plt.ylabel('Exciting projects')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several fields with a considerable amount of nulls. Zip code, public national center for education statistics id, cost of fulfillment, and number of students reached are some of these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Hector/anaconda2/lib/python2.7/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school_ncesid</th>\n",
       "      <th>school_latitude</th>\n",
       "      <th>school_longitude</th>\n",
       "      <th>school_zip</th>\n",
       "      <th>fulfillment_labor_materials</th>\n",
       "      <th>total_price_excluding_optional_support</th>\n",
       "      <th>total_price_including_optional_support</th>\n",
       "      <th>students_reached</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.213620e+05</td>\n",
       "      <td>664098.000000</td>\n",
       "      <td>664098.000000</td>\n",
       "      <td>664094.000000</td>\n",
       "      <td>629016.000000</td>\n",
       "      <td>6.640980e+05</td>\n",
       "      <td>6.640980e+05</td>\n",
       "      <td>663952.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.608577e+11</td>\n",
       "      <td>37.240948</td>\n",
       "      <td>-93.344760</td>\n",
       "      <td>52926.452644</td>\n",
       "      <td>27.495822</td>\n",
       "      <td>5.420415e+02</td>\n",
       "      <td>6.447128e+02</td>\n",
       "      <td>97.233547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.589754e+11</td>\n",
       "      <td>4.668155</td>\n",
       "      <td>17.761908</td>\n",
       "      <td>31193.738658</td>\n",
       "      <td>8.663046</td>\n",
       "      <td>1.267738e+04</td>\n",
       "      <td>1.545863e+04</td>\n",
       "      <td>2283.327661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000050e+10</td>\n",
       "      <td>18.249140</td>\n",
       "      <td>-176.634989</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>34.038082</td>\n",
       "      <td>-112.003213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.672000e+02</td>\n",
       "      <td>3.175000e+02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>37.662476</td>\n",
       "      <td>-87.681219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.110000e+02</td>\n",
       "      <td>4.878400e+02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>40.758827</td>\n",
       "      <td>-79.193091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.864900e+02</td>\n",
       "      <td>6.963875e+02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.100009e+11</td>\n",
       "      <td>67.258157</td>\n",
       "      <td>-66.628036</td>\n",
       "      <td>99950.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.025002e+07</td>\n",
       "      <td>1.250002e+07</td>\n",
       "      <td>999999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       school_ncesid  school_latitude  school_longitude     school_zip  \\\n",
       "count   6.213620e+05    664098.000000     664098.000000  664094.000000   \n",
       "mean    2.608577e+11        37.240948        -93.344760   52926.452644   \n",
       "std     1.589754e+11         4.668155         17.761908   31193.738658   \n",
       "min     1.000050e+10        18.249140       -176.634989     410.000000   \n",
       "25%              NaN        34.038082       -112.003213            NaN   \n",
       "50%              NaN        37.662476        -87.681219            NaN   \n",
       "75%              NaN        40.758827        -79.193091            NaN   \n",
       "max     6.100009e+11        67.258157        -66.628036   99950.000000   \n",
       "\n",
       "       fulfillment_labor_materials  total_price_excluding_optional_support  \\\n",
       "count                629016.000000                            6.640980e+05   \n",
       "mean                     27.495822                            5.420415e+02   \n",
       "std                       8.663046                            1.267738e+04   \n",
       "min                       9.000000                            0.000000e+00   \n",
       "25%                            NaN                            2.672000e+02   \n",
       "50%                            NaN                            4.110000e+02   \n",
       "75%                            NaN                            5.864900e+02   \n",
       "max                      35.000000                            1.025002e+07   \n",
       "\n",
       "       total_price_including_optional_support  students_reached  \n",
       "count                            6.640980e+05     663952.000000  \n",
       "mean                             6.447128e+02         97.233547  \n",
       "std                              1.545863e+04       2283.327661  \n",
       "min                              0.000000e+00          0.000000  \n",
       "25%                              3.175000e+02               NaN  \n",
       "50%                              4.878400e+02               NaN  \n",
       "75%                              6.963875e+02               NaN  \n",
       "max                              1.250002e+07     999999.000000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "projects.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) Resources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "resources =  pd.read_csv('resources.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'resourceid', u'projectid', u'vendorid', u'vendor_name',\n",
       "       u'project_resource_type', u'item_name', u'item_number',\n",
       "       u'item_unit_price', u'item_quantity'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resources['total_cost'] = resources.item_unit_price * resources.item_quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Some projects requested an extraordinary amount of money.\n",
      "The 2 projects requesting the most resources requested a total of $458421996.\n",
      "\n",
      "\n",
      "The next 8 projects requesting the most resources requested a total of $2052667.\n"
     ]
    }
   ],
   "source": [
    "print('Some projects requested an extraordinary amount of money.')\n",
    "top2_by_resources = resources.groupby('projectid').sum().total_cost.sort_values(ascending=False).head(2)\n",
    "print('The 2 projects requesting the most resources requested a total of ${0:.0f}.'.format(top2_by_resources.sum()))\n",
    "print('\\n')\n",
    "next_by_resources = resources.groupby('projectid').sum().total_cost.sort_values(ascending=False).head(10)[2:10]\n",
    "print('The next 8 projects requesting the most resources requested a total of ${0:.0f}.'.format(next_by_resources.sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The average project requests resources with a cost of 1140.75303958.'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_project_cost = resources.groupby('projectid').sum().total_cost.mean()\n",
    "'The average project requests resources with a cost of {}.'.format(avg_project_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the requests for resources are to be acquired from the following 10 companies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lakeshore Learning Materials    744371\n",
       "AKJ Books                       653845\n",
       "Barnes and Noble                568438\n",
       "Amazon                          216278\n",
       "Best Buy for Business           176982\n",
       "Quill.com                       170423\n",
       "School Specialty                145543\n",
       "Blick Art Materials             113962\n",
       "Nasco                           113226\n",
       "Teachers' School Supply          84828\n",
       "Name: vendor_name, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources.vendor_name.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Books and Supplies are the categories with the highest number of resource requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Books         1575792\n",
      "Supplies      1341579\n",
      "Technology     477598\n",
      "Other          264644\n",
      "Trips            6186\n",
      "Visitors         1222\n",
      "Name: project_resource_type, dtype: int64\n",
      "\n",
      "Books and supplies make up 80% of the total requests.\n"
     ]
    }
   ],
   "source": [
    "print(resources.project_resource_type.value_counts())\n",
    "books = resources.project_resource_type.value_counts()['Books']\n",
    "supplies = resources.project_resource_type.value_counts()['Supplies']\n",
    "all = float(resources.project_resource_type.count())\n",
    "print('\\nBooks and supplies make up {0:.0f}% of the total requests.'.format((books+supplies)/all*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top vendor varies depending on the type of resource requested:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Technology\n",
      "Best Buy for Business           159653\n",
      "Lakeshore Learning Materials     55114\n",
      "Quill.com                        46384\n",
      "Amazon                           38109\n",
      "CDW-G                            36145\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "Supplies\n",
      "Lakeshore Learning Materials    430916\n",
      "Quill.com                       105861\n",
      "School Specialty                104535\n",
      "Blick Art Materials             103490\n",
      "Nasco                            81536\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "Other\n",
      "Lakeshore Learning Materials     107757\n",
      "School Specialty                  16644\n",
      "Amazon                            14827\n",
      "Nasco                             13205\n",
      "Kaplan Early Learning Company     13111\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "Books\n",
      "AKJ Books                        617344\n",
      "Barnes and Noble                 538059\n",
      "Lakeshore Learning Materials     150498\n",
      "Amazon                           113775\n",
      "Kaplan Early Learning Company     20877\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "Trips\n",
      "Barnes and Noble                76\n",
      "Quill.com                       51\n",
      "Nasco                           42\n",
      "Amazon                          29\n",
      "Lakeshore Learning Materials    26\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "Visitors\n",
      "Barnes and Noble                24\n",
      "AKJ Books                       22\n",
      "Nasco                           21\n",
      "Lakeshore Learning Materials    14\n",
      "Blick Art Materials             12\n",
      "Name: vendor_name, dtype: int64\n",
      "\n",
      "\n",
      "nan\n",
      "Series([], Name: vendor_name, dtype: int64)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "type_of_resources = resources.project_resource_type.unique()\n",
    "for resource in type_of_resources:\n",
    "    print(resource)\n",
    "    print(resources[resources['project_resource_type'] == resource].vendor_name.value_counts().head(5))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Story"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at why is there such an expensive project. Are they looking to build something big? Are they requesting like a lot of computers or electronic equipment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "top_by_resources = resources.groupby('projectid').sum().total_cost.sort_values(ascending=False).head(10)\n",
    "most_expensive_project = top_by_resources.index[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently this project requested only books, but there is one that has an unexpected cost of 450,000,000 USD. There must be something wrong with this book. Most likely, the price got mixed up with the item number (3.99 which sound much more like the price of a book)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "resourceid               3ed3a90cd5059bc8d5365bbd36973257\n",
       "projectid                cffed1b1f8307ae1b17d95d35029c29e\n",
       "vendorid                                              NaN\n",
       "vendor_name                                           NaN\n",
       "project_resource_type                               Books\n",
       "item_name                                   Great Gretzky\n",
       "item_number                                          3.99\n",
       "item_unit_price                               4.48422e+08\n",
       "item_quantity                                           1\n",
       "total_cost                                    4.48422e+08\n",
       "Name: 682662, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_id = resources[resources.projectid == most_expensive_project].total_cost.sort_values(ascending=False).head(1).keys()[0]\n",
    "resources.iloc[res_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about the second most expensive project?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sec_most_expensive_project = top_by_resources.index[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why is an item called \"DC Test Item\" so expensive?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resourceid</th>\n",
       "      <th>projectid</th>\n",
       "      <th>vendorid</th>\n",
       "      <th>vendor_name</th>\n",
       "      <th>project_resource_type</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_unit_price</th>\n",
       "      <th>item_quantity</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1621933</th>\n",
       "      <td>e500bc7ef6492ebbc48153a6b09825d7</td>\n",
       "      <td>8ec6ef94555d01ede69ab3785aba8c40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>DC Test Item</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               resourceid                         projectid  \\\n",
       "1621933  e500bc7ef6492ebbc48153a6b09825d7  8ec6ef94555d01ede69ab3785aba8c40   \n",
       "\n",
       "         vendorid vendor_name project_resource_type     item_name item_number  \\\n",
       "1621933       NaN         NaN                 Other  DC Test Item         NaN   \n",
       "\n",
       "         item_unit_price  item_quantity  total_cost  \n",
       "1621933       10000000.0            1.0  10000000.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources[resources.projectid == sec_most_expensive_project]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resourceid</th>\n",
       "      <th>projectid</th>\n",
       "      <th>vendorid</th>\n",
       "      <th>vendor_name</th>\n",
       "      <th>project_resource_type</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_unit_price</th>\n",
       "      <th>item_quantity</th>\n",
       "      <th>total_cost</th>\n",
       "      <th>teacher_acctid</th>\n",
       "      <th>title</th>\n",
       "      <th>short_description</th>\n",
       "      <th>need_statement</th>\n",
       "      <th>essay</th>\n",
       "      <th>essay_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>e500bc7ef6492ebbc48153a6b09825d7</td>\n",
       "      <td>8ec6ef94555d01ede69ab3785aba8c40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>DC Test Item</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>8b0c1d11b3d14836db5383d8196e8de7</td>\n",
       "      <td>TEST - PLEASE DO NOT DONATE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PLEASE IGNORE - This is a test</td>\n",
       "      <td>Please ignore. This is a test. \\r\\n</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         resourceid                         projectid  \\\n",
       "0  e500bc7ef6492ebbc48153a6b09825d7  8ec6ef94555d01ede69ab3785aba8c40   \n",
       "\n",
       "   vendorid vendor_name project_resource_type     item_name item_number  \\\n",
       "0       NaN         NaN                 Other  DC Test Item         NaN   \n",
       "\n",
       "   item_unit_price  item_quantity  total_cost  \\\n",
       "0       10000000.0            1.0  10000000.0   \n",
       "\n",
       "                     teacher_acctid                        title  \\\n",
       "0  8b0c1d11b3d14836db5383d8196e8de7  TEST - PLEASE DO NOT DONATE   \n",
       "\n",
       "  short_description                  need_statement  \\\n",
       "0               NaN  PLEASE IGNORE - This is a test   \n",
       "\n",
       "                                 essay  essay_length  \n",
       "0  Please ignore. This is a test. \\r\\n          34.0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resources[resources.projectid == sec_most_expensive_project].merge(essays, on='projectid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently this is a 'test' entry on the resource table, so not actually a project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at some of the other most expensive projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resourceid</th>\n",
       "      <th>projectid</th>\n",
       "      <th>vendorid</th>\n",
       "      <th>vendor_name</th>\n",
       "      <th>project_resource_type</th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_number</th>\n",
       "      <th>item_unit_price</th>\n",
       "      <th>item_quantity</th>\n",
       "      <th>total_cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>510824</th>\n",
       "      <td>8a3877e740692270196891fd8fd7581f</td>\n",
       "      <td>dc106a943920d1fbe1f4f079c212c732</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>ActivBoard Adjustable &amp; Fixed System Frame</td>\n",
       "      <td>27649</td>\n",
       "      <td>600.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510825</th>\n",
       "      <td>ab93d94ee4c3d071283d5d0b6f003316</td>\n",
       "      <td>dc106a943920d1fbe1f4f079c212c732</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Freight</td>\n",
       "      <td>27660</td>\n",
       "      <td>1431.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1431.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510826</th>\n",
       "      <td>cfee389f0d2b14a2384e2fdedc65abf9</td>\n",
       "      <td>dc106a943920d1fbe1f4f079c212c732</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>ActivBoard 578Pro Mobile System</td>\n",
       "      <td>27651</td>\n",
       "      <td>3599.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>107970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583968</th>\n",
       "      <td>41f69e5021bbd7d76add67d6f99556ea</td>\n",
       "      <td>d6ef27c07c30c81f0c16c32b6acfa2ff</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Toshiba Tecra A8-EZ8412</td>\n",
       "      <td>1066435</td>\n",
       "      <td>925.28</td>\n",
       "      <td>420.0</td>\n",
       "      <td>388617.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636425</th>\n",
       "      <td>db591e00b01469f8b7a8884240ff6137</td>\n",
       "      <td>d3411fe4bd503899bd6ba9e4d88e5fc4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>12-Gallon Storage Bin</td>\n",
       "      <td>277137</td>\n",
       "      <td>11.55</td>\n",
       "      <td>4.0</td>\n",
       "      <td>46.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636426</th>\n",
       "      <td>a9d38b837f83920176c68565a0182573</td>\n",
       "      <td>d3411fe4bd503899bd6ba9e4d88e5fc4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Economy Adjustable-Shelf Storage Unit</td>\n",
       "      <td>359976</td>\n",
       "      <td>149.85</td>\n",
       "      <td>1.0</td>\n",
       "      <td>149.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636427</th>\n",
       "      <td>3aa028dc8e17c93dc5110be23f821b9e</td>\n",
       "      <td>d3411fe4bd503899bd6ba9e4d88e5fc4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>Eldon Fold' N Roll Cart System</td>\n",
       "      <td>$38.59</td>\n",
       "      <td>264275.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>264275.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993991</th>\n",
       "      <td>5962232d49c9f01e603a2521cdaafa23</td>\n",
       "      <td>ba33798533580b2d3167c455ba101a63</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP Mini 110 1020NR   Atom N270 1 6 GHz   10 1 ...</td>\n",
       "      <td>1786620</td>\n",
       "      <td>329.99</td>\n",
       "      <td>250.0</td>\n",
       "      <td>82497.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122610</th>\n",
       "      <td>dc75a41344fee7ccba653140bf4272e0</td>\n",
       "      <td>b13aaa06baad031277fdd7c7f7370c02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other</td>\n",
       "      <td>New playground</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93425.78</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93425.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357034</th>\n",
       "      <td>656df8f9b913df69c609d6cae49885cf</td>\n",
       "      <td>5b9ec0ec98a6ef660892cca50e3d9f2c</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>ActivBoard 578Pro Mobile System</td>\n",
       "      <td>27651</td>\n",
       "      <td>3599.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>107970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357035</th>\n",
       "      <td>11348974f7830dbdb79d2b4575276dda</td>\n",
       "      <td>5b9ec0ec98a6ef660892cca50e3d9f2c</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Freight</td>\n",
       "      <td>27660</td>\n",
       "      <td>1431.19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1431.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357036</th>\n",
       "      <td>dd5f5131ade90c9adae4b23096fe36b5</td>\n",
       "      <td>5b9ec0ec98a6ef660892cca50e3d9f2c</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Kaplan Early Learning Company</td>\n",
       "      <td>Technology</td>\n",
       "      <td>ActivBoard Adjustable &amp; Fixed System Frame</td>\n",
       "      <td>27649</td>\n",
       "      <td>600.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>18000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977500</th>\n",
       "      <td>56a9e6ae22ddc04863ca9e29b81cd371</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP Executive Leather Case   Smart Buy</td>\n",
       "      <td>1152439</td>\n",
       "      <td>71.81</td>\n",
       "      <td>4.0</td>\n",
       "      <td>287.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977501</th>\n",
       "      <td>81e38800ab01b7448956421b1b0f9ade</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP 30 Notebook Mobility Cart   notebook storag...</td>\n",
       "      <td>1530775</td>\n",
       "      <td>2643.37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2643.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977502</th>\n",
       "      <td>2257e6739db54ac10ae62616b354ff2f</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP Desktop   Keyboard   Mouse</td>\n",
       "      <td>1054412</td>\n",
       "      <td>38.99</td>\n",
       "      <td>4.0</td>\n",
       "      <td>155.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977503</th>\n",
       "      <td>fbaa2408db75391eea3c7a32c13ed26e</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP LaserJet Q7516A Black Toner Cartridge</td>\n",
       "      <td>965197</td>\n",
       "      <td>203.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>203.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977504</th>\n",
       "      <td>40770a91d40ecf8dd145a217a9090539</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP LP2275W 22  Widescreen LCD  4 Pack</td>\n",
       "      <td>1710342</td>\n",
       "      <td>1538.14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1538.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977505</th>\n",
       "      <td>67e8231e20317e16547e9c67e0865bc9</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP EliteBook 2730p   Core 2 Duo SL9400 1 86 GH...</td>\n",
       "      <td>1911389</td>\n",
       "      <td>1679.99</td>\n",
       "      <td>30.0</td>\n",
       "      <td>50399.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977506</th>\n",
       "      <td>40d19dff6b702e8e9647063af948eb4b</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP LaserJet 5200tn Laser Printer</td>\n",
       "      <td>964882</td>\n",
       "      <td>2138.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2138.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977507</th>\n",
       "      <td>37f3577c4daa18aedd797895fa2ce781</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP Ultra slim Expansion Base   docking station...</td>\n",
       "      <td>1634985</td>\n",
       "      <td>305.23</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1220.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977508</th>\n",
       "      <td>8395b8492ce1dfac4ec043f71688e089</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP battery   Li Ion x 1</td>\n",
       "      <td>1637783</td>\n",
       "      <td>188.53</td>\n",
       "      <td>30.0</td>\n",
       "      <td>5655.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977509</th>\n",
       "      <td>110f08cc82654cda8626572b26c04a88</td>\n",
       "      <td>3069dafc2db7aeeb0c2094a7cfbe15f0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>CDW-G</td>\n",
       "      <td>Technology</td>\n",
       "      <td>HP ProCurve Wireless Access Point 10ag   wirel...</td>\n",
       "      <td>1359734</td>\n",
       "      <td>189.52</td>\n",
       "      <td>1.0</td>\n",
       "      <td>189.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041303</th>\n",
       "      <td>5925e2f5ffd7f37697f9784d5b963209</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Rosie's Walk</td>\n",
       "      <td>904037</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041304</th>\n",
       "      <td>29f519e8b3f3c6be6dd8a3e5184453ff</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Reading First:  Listening Library for Fluency II</td>\n",
       "      <td>56058</td>\n",
       "      <td>148.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041305</th>\n",
       "      <td>66f8b4b6a598788c5b05faa7e4b8e7d5</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Dora's Eggs</td>\n",
       "      <td>904043</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041306</th>\n",
       "      <td>75b2ade14d0fac6aa3a829b34aacd228</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>One Hundred Hungry Ants</td>\n",
       "      <td>904035</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041307</th>\n",
       "      <td>560a32febe58465529ad1789e39dfc53</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>My Friends</td>\n",
       "      <td>904034</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041308</th>\n",
       "      <td>7da48d3835a57b4e2db46306174c95ea</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Reading First:  Listening Library for Fluency I</td>\n",
       "      <td>56059</td>\n",
       "      <td>148.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041309</th>\n",
       "      <td>3d95655a7e2dbb18e62287ecf1695155</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Swimmy</td>\n",
       "      <td>904050</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041310</th>\n",
       "      <td>822aa0af9189a92dc60c1a34b3f0d707</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Pigs In The Mud</td>\n",
       "      <td>904036</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041311</th>\n",
       "      <td>f7e7b980441af4cfcbf17a7edee6cff2</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>It Takes A Village</td>\n",
       "      <td>904031</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041312</th>\n",
       "      <td>d2e7111b3709ebe89e7f9c72b15f3d63</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Little Cloud</td>\n",
       "      <td>9.95</td>\n",
       "      <td>904033.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>904033.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041313</th>\n",
       "      <td>675d7fc2b407fb8f8fb904e45ecc8ae3</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Mary Had A Little Lamb</td>\n",
       "      <td>4499</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3041314</th>\n",
       "      <td>42e25354cf3217a42331306f87deaeaf</td>\n",
       "      <td>2c17bd80453d962aa3a15310326a5e9a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Books</td>\n",
       "      <td>Today is Monday</td>\n",
       "      <td>904039</td>\n",
       "      <td>9.95</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               resourceid                         projectid  \\\n",
       "510824   8a3877e740692270196891fd8fd7581f  dc106a943920d1fbe1f4f079c212c732   \n",
       "510825   ab93d94ee4c3d071283d5d0b6f003316  dc106a943920d1fbe1f4f079c212c732   \n",
       "510826   cfee389f0d2b14a2384e2fdedc65abf9  dc106a943920d1fbe1f4f079c212c732   \n",
       "583968   41f69e5021bbd7d76add67d6f99556ea  d6ef27c07c30c81f0c16c32b6acfa2ff   \n",
       "636425   db591e00b01469f8b7a8884240ff6137  d3411fe4bd503899bd6ba9e4d88e5fc4   \n",
       "636426   a9d38b837f83920176c68565a0182573  d3411fe4bd503899bd6ba9e4d88e5fc4   \n",
       "636427   3aa028dc8e17c93dc5110be23f821b9e  d3411fe4bd503899bd6ba9e4d88e5fc4   \n",
       "993991   5962232d49c9f01e603a2521cdaafa23  ba33798533580b2d3167c455ba101a63   \n",
       "1122610  dc75a41344fee7ccba653140bf4272e0  b13aaa06baad031277fdd7c7f7370c02   \n",
       "2357034  656df8f9b913df69c609d6cae49885cf  5b9ec0ec98a6ef660892cca50e3d9f2c   \n",
       "2357035  11348974f7830dbdb79d2b4575276dda  5b9ec0ec98a6ef660892cca50e3d9f2c   \n",
       "2357036  dd5f5131ade90c9adae4b23096fe36b5  5b9ec0ec98a6ef660892cca50e3d9f2c   \n",
       "2977500  56a9e6ae22ddc04863ca9e29b81cd371  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977501  81e38800ab01b7448956421b1b0f9ade  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977502  2257e6739db54ac10ae62616b354ff2f  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977503  fbaa2408db75391eea3c7a32c13ed26e  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977504  40770a91d40ecf8dd145a217a9090539  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977505  67e8231e20317e16547e9c67e0865bc9  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977506  40d19dff6b702e8e9647063af948eb4b  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977507  37f3577c4daa18aedd797895fa2ce781  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977508  8395b8492ce1dfac4ec043f71688e089  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "2977509  110f08cc82654cda8626572b26c04a88  3069dafc2db7aeeb0c2094a7cfbe15f0   \n",
       "3041303  5925e2f5ffd7f37697f9784d5b963209  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041304  29f519e8b3f3c6be6dd8a3e5184453ff  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041305  66f8b4b6a598788c5b05faa7e4b8e7d5  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041306  75b2ade14d0fac6aa3a829b34aacd228  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041307  560a32febe58465529ad1789e39dfc53  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041308  7da48d3835a57b4e2db46306174c95ea  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041309  3d95655a7e2dbb18e62287ecf1695155  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041310  822aa0af9189a92dc60c1a34b3f0d707  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041311  f7e7b980441af4cfcbf17a7edee6cff2  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041312  d2e7111b3709ebe89e7f9c72b15f3d63  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041313  675d7fc2b407fb8f8fb904e45ecc8ae3  2c17bd80453d962aa3a15310326a5e9a   \n",
       "3041314  42e25354cf3217a42331306f87deaeaf  2c17bd80453d962aa3a15310326a5e9a   \n",
       "\n",
       "         vendorid                    vendor_name project_resource_type  \\\n",
       "510824        2.0  Kaplan Early Learning Company            Technology   \n",
       "510825        2.0  Kaplan Early Learning Company            Technology   \n",
       "510826        2.0  Kaplan Early Learning Company            Technology   \n",
       "583968      150.0                          CDW-G            Technology   \n",
       "636425        NaN                            NaN                 Other   \n",
       "636426        NaN                            NaN                 Other   \n",
       "636427        NaN                            NaN                 Other   \n",
       "993991      150.0                          CDW-G            Technology   \n",
       "1122610       NaN                            NaN                 Other   \n",
       "2357034       2.0  Kaplan Early Learning Company            Technology   \n",
       "2357035       2.0  Kaplan Early Learning Company            Technology   \n",
       "2357036       2.0  Kaplan Early Learning Company            Technology   \n",
       "2977500     150.0                          CDW-G            Technology   \n",
       "2977501     150.0                          CDW-G            Technology   \n",
       "2977502     150.0                          CDW-G            Technology   \n",
       "2977503     150.0                          CDW-G            Technology   \n",
       "2977504     150.0                          CDW-G            Technology   \n",
       "2977505     150.0                          CDW-G            Technology   \n",
       "2977506     150.0                          CDW-G            Technology   \n",
       "2977507     150.0                          CDW-G            Technology   \n",
       "2977508     150.0                          CDW-G            Technology   \n",
       "2977509     150.0                          CDW-G            Technology   \n",
       "3041303       NaN                            NaN                 Books   \n",
       "3041304       NaN                            NaN                 Books   \n",
       "3041305       NaN                            NaN                 Books   \n",
       "3041306       NaN                            NaN                 Books   \n",
       "3041307       NaN                            NaN                 Books   \n",
       "3041308       NaN                            NaN                 Books   \n",
       "3041309       NaN                            NaN                 Books   \n",
       "3041310       NaN                            NaN                 Books   \n",
       "3041311       NaN                            NaN                 Books   \n",
       "3041312       NaN                            NaN                 Books   \n",
       "3041313       NaN                            NaN                 Books   \n",
       "3041314       NaN                            NaN                 Books   \n",
       "\n",
       "                                                 item_name item_number  \\\n",
       "510824          ActivBoard Adjustable & Fixed System Frame       27649   \n",
       "510825                                             Freight       27660   \n",
       "510826                     ActivBoard 578Pro Mobile System       27651   \n",
       "583968                             Toshiba Tecra A8-EZ8412     1066435   \n",
       "636425                               12-Gallon Storage Bin      277137   \n",
       "636426               Economy Adjustable-Shelf Storage Unit      359976   \n",
       "636427                      Eldon Fold' N Roll Cart System      $38.59   \n",
       "993991   HP Mini 110 1020NR   Atom N270 1 6 GHz   10 1 ...     1786620   \n",
       "1122610                                     New playground         NaN   \n",
       "2357034                    ActivBoard 578Pro Mobile System       27651   \n",
       "2357035                                            Freight       27660   \n",
       "2357036         ActivBoard Adjustable & Fixed System Frame       27649   \n",
       "2977500              HP Executive Leather Case   Smart Buy     1152439   \n",
       "2977501  HP 30 Notebook Mobility Cart   notebook storag...     1530775   \n",
       "2977502                      HP Desktop   Keyboard   Mouse     1054412   \n",
       "2977503           HP LaserJet Q7516A Black Toner Cartridge      965197   \n",
       "2977504              HP LP2275W 22  Widescreen LCD  4 Pack     1710342   \n",
       "2977505  HP EliteBook 2730p   Core 2 Duo SL9400 1 86 GH...     1911389   \n",
       "2977506                   HP LaserJet 5200tn Laser Printer      964882   \n",
       "2977507  HP Ultra slim Expansion Base   docking station...     1634985   \n",
       "2977508                            HP battery   Li Ion x 1     1637783   \n",
       "2977509  HP ProCurve Wireless Access Point 10ag   wirel...     1359734   \n",
       "3041303                                       Rosie's Walk      904037   \n",
       "3041304   Reading First:  Listening Library for Fluency II       56058   \n",
       "3041305                                        Dora's Eggs      904043   \n",
       "3041306                            One Hundred Hungry Ants      904035   \n",
       "3041307                                         My Friends      904034   \n",
       "3041308    Reading First:  Listening Library for Fluency I       56059   \n",
       "3041309                                             Swimmy      904050   \n",
       "3041310                                    Pigs In The Mud      904036   \n",
       "3041311                                 It Takes A Village      904031   \n",
       "3041312                                       Little Cloud        9.95   \n",
       "3041313                             Mary Had A Little Lamb        4499   \n",
       "3041314                                    Today is Monday      904039   \n",
       "\n",
       "         item_unit_price  item_quantity  total_cost  \n",
       "510824            600.00           30.0    18000.00  \n",
       "510825           1431.19            1.0     1431.19  \n",
       "510826           3599.00           30.0   107970.00  \n",
       "583968            925.28          420.0   388617.60  \n",
       "636425             11.55            4.0       46.20  \n",
       "636426            149.85            1.0      149.85  \n",
       "636427         264275.00            1.0   264275.00  \n",
       "993991            329.99          250.0    82497.50  \n",
       "1122610         93425.78            1.0    93425.78  \n",
       "2357034          3599.00           30.0   107970.00  \n",
       "2357035          1431.19            1.0     1431.19  \n",
       "2357036           600.00           30.0    18000.00  \n",
       "2977500            71.81            4.0      287.24  \n",
       "2977501          2643.37            1.0     2643.37  \n",
       "2977502            38.99            4.0      155.96  \n",
       "2977503           203.99            1.0      203.99  \n",
       "2977504          1538.14            1.0     1538.14  \n",
       "2977505          1679.99           30.0    50399.70  \n",
       "2977506          2138.99            1.0     2138.99  \n",
       "2977507           305.23            4.0     1220.92  \n",
       "2977508           188.53           30.0     5655.90  \n",
       "2977509           189.52            1.0      189.52  \n",
       "3041303             9.95            1.0        9.95  \n",
       "3041304           148.00            1.0      148.00  \n",
       "3041305             9.95            1.0        9.95  \n",
       "3041306             9.95            1.0        9.95  \n",
       "3041307             9.95            1.0        9.95  \n",
       "3041308           148.00            1.0      148.00  \n",
       "3041309             9.95            1.0        9.95  \n",
       "3041310             9.95            1.0        9.95  \n",
       "3041311             9.95            1.0        9.95  \n",
       "3041312        904033.00            1.0   904033.00  \n",
       "3041313             9.95            1.0        9.95  \n",
       "3041314             9.95            1.0        9.95  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_most_expensive_project = top_by_resources.index[2:]\n",
    "resources[resources.projectid.isin(list(other_most_expensive_project))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently there is another book, Little Cloud, which seems to have mixed it's price with the number. This is making this item to cost 904,033 USD. Pretty high for a book. There is also Eldon Fold'N Roll Cart System, for 264,275 USD. This one is definitely swapped, as the item_number even has a '$' sign on it.\n",
    "\n",
    "There seems to be a relatively frequent issue with the item_unit_price and the item_number, at least in the most expensive projects. Some of these are swapped and that makes prices look terribly high. It would be useful to take a look at some of the item_number, item_unit_price pairs, see if they look like they should be swapped, and fix them. This might be inflating the costs of some of the projects in a very unusual way. As DonorsChoose.org, you wouldn't want to artificially inflate the amounts you're giving away, because you might leave other good projects without any budget."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questions for the project partner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on their goal, predict \"exciting\" funding requests, some of the questions I would do are.\n",
    "\n",
    "- There seems to be a higher number of schools classified as high-poverty to be selected as exciting. Is this because more schools within such category apply to DonorsChoose.org? Or is it because there is a preference to fund such schools?\n",
    "- California and New York are by far the states with highest number of donations. Any ideas on why this is? Could we somehow market to donors in other states to fund our projects? Because there seems to be an opportunity to expand the donor base in other states.\n",
    "- Is there a particular preference from the 'Thoughtful Donors' to choose a specific type of project? Do they maintain consistent preferences through the years?\n",
    "- They want to fund exciting projects, but what about effective projects? Do they have a way to measure which type of materials impact the students the most? Maybe it would be sueful to include this type of variable in the next years.\n",
    "- Could there be a type of underrepresented projects that should have a priority when assigning resources?\n",
    "\n",
    "I would also suggest a data validation step. The resources database had a couple of errors in prices that could impact the analysis. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about donation_from_thoughtful_donor? We would like to know how likely it is for a random project to be chosen by one of these donors. From the 619,326 projects, only 6,936 (1.12%) got money from these star donors. Getting money from the thoughtful donors is apparently important. It seems they influence others to donate, as 94% of the projects where they donate get fully funded (vs. 70% on average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.011199271466077639, 6936)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes.donation_from_thoughtful_donor.mean(), outcomes.donation_from_thoughtful_donor.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6936, 0.94031141868512114, 6522)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = outcomes[outcomes.donation_from_thoughtful_donor == 1].fully_funded\n",
    "temp.count(), temp.mean(), temp.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.69540597359064527"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcomes.fully_funded.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apparently these are not projects from just one year. They expand accross several years."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We build new variables to determine the year, month, and day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "projects['year'], projects['month'], projects['day'] = zip(*projects.date_posted.str.split('-'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bigger = outcomes.merge(projects, on='projectid', how='inner')\n",
    "label = 'is_exciting'\n",
    "\n",
    "categorical = ['teacher_acctid', 'schoolid', 'school_ncesid','school_city', 'school_state', 'school_zip',\n",
    "       'school_metro', 'school_district', 'school_county', 'school_charter', 'school_magnet', 'school_year_round',\n",
    "       'school_nlns', 'school_kipp', 'school_charter_ready_promise', 'teacher_teach_for_america',\n",
    "       'teacher_ny_teaching_fellow', 'primary_focus_subject', 'primary_focus_area', 'secondary_focus_subject',\n",
    "        'secondary_focus_area', 'resource_type', 'grade_level',\n",
    "       'eligible_almost_home_match']\n",
    "continuous  = ['school_latitude', 'school_longitude', 'fulfillment_labor_materials',\n",
    "       'total_price_excluding_optional_support', 'total_price_including_optional_support', 'students_reached',\n",
    "       'year', 'month']\n",
    "feats = categorical + continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bigger = bigger.replace(to_replace='t', value=1)\n",
    "bigger = bigger.replace(to_replace='f', value=0)\n",
    "\n",
    "for feat in categorical:\n",
    "    bigger[feat] = bigger[feat].fillna('')\n",
    "for feat in continuous:\n",
    "    bigger[feat] = bigger[feat].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "projectid                                         False\n",
       "is_exciting                                       False\n",
       "at_least_1_teacher_referred_donor                 False\n",
       "fully_funded                                      False\n",
       "at_least_1_green_donation                         False\n",
       "great_chat                                        False\n",
       "three_or_more_non_teacher_referred_donors         False\n",
       "one_non_teacher_referred_donor_giving_100_plus    False\n",
       "donation_from_thoughtful_donor                    False\n",
       "great_messages_proportion                         False\n",
       "teacher_referred_count                            False\n",
       "non_teacher_referred_count                        False\n",
       "complies                                          False\n",
       "teacher_acctid                                    False\n",
       "schoolid                                          False\n",
       "school_ncesid                                     False\n",
       "school_latitude                                   False\n",
       "school_longitude                                  False\n",
       "school_city                                       False\n",
       "school_state                                      False\n",
       "school_zip                                        False\n",
       "school_metro                                      False\n",
       "school_district                                   False\n",
       "school_county                                     False\n",
       "school_charter                                    False\n",
       "school_magnet                                     False\n",
       "school_year_round                                 False\n",
       "school_nlns                                       False\n",
       "school_kipp                                       False\n",
       "school_charter_ready_promise                      False\n",
       "teacher_prefix                                    False\n",
       "teacher_teach_for_america                         False\n",
       "teacher_ny_teaching_fellow                        False\n",
       "primary_focus_subject                             False\n",
       "primary_focus_area                                False\n",
       "secondary_focus_subject                           False\n",
       "secondary_focus_area                              False\n",
       "resource_type                                     False\n",
       "poverty_level                                     False\n",
       "grade_level                                       False\n",
       "fulfillment_labor_materials                       False\n",
       "total_price_excluding_optional_support            False\n",
       "total_price_including_optional_support            False\n",
       "students_reached                                  False\n",
       "eligible_double_your_impact_match                 False\n",
       "eligible_almost_home_match                        False\n",
       "date_posted                                       False\n",
       "year                                              False\n",
       "month                                             False\n",
       "day                                               False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigger.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "def process_categorical_features(df, feat):\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[feat])\n",
    "    df[feat] = le.transform(df[feat])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for feat in categorical:\n",
    "    process_categorical_features(bigger, feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96741009221116436"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test  = bigger[bigger.year == '2013']\n",
    "train = bigger[bigger.year != '2013']\n",
    "X_train, Y_train, X_test, Y_test = train[feats], train.donation_from_thoughtful_donor, test[feats], test.donation_from_thoughtful_donor\n",
    "1 - Y_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.grid_search import ParameterGrid\n",
    "from sklearn.cross_validation import KFold\n",
    "\n",
    "def classify(X_train, Y_train, X_test, Y_test, clfs, iters, verbose = False):\n",
    "    '''\n",
    "    Takes:\n",
    "        - X_train, a dataframe of features \n",
    "        - Y_train, a dataframe of the label\n",
    "        - X_test, a dataframe of features \n",
    "        - Y_test, a dataframe of the label\n",
    "        - clfs, a list of strings indicating classifiers to run (e.g. ['LR', 'DT'])\n",
    "        - iters, an integer referring to iterations of a model\n",
    "        - r, a float to determine if a classifier score yields a positive or negative prediction\n",
    "\n",
    "    Returns:\n",
    "        scores, a dictionary with parameters as keys and y-scores as values\n",
    "    '''\n",
    "    grid = {'LR' : {'penalty': ['l1', 'l2'], 'C': [0.0001, 0.001, 0.01, 0.05, 0.1, 1, 5, 10, 20]}}\n",
    "    classifiers = { 'LR' : LogisticRegression()}\n",
    "    scores = {}\n",
    "\n",
    "    # for every classifier, try any possible combination of parameters on grid\n",
    "    for index, clf in enumerate([classifiers[x] for x in clfs]):\n",
    "        name = clfs[index]\n",
    "        parameter_values = grid[name]\n",
    "        scores[name] = {}\n",
    "        i = 0\n",
    "\n",
    "        for p in ParameterGrid(parameter_values):\n",
    "            clf.set_params(**p)\n",
    "            scores[name][str(p)] = []\n",
    "            kf_train = KFold(len(X_train), n_folds = iters)\n",
    "            kf_test  = KFold(len(X_test), n_folds = iters)\n",
    "\n",
    "            for train_index, _ in kf_train: \n",
    "                xtrain, ytrain = X_train.iloc[train_index], Y_train.iloc[train_index]\n",
    "                print(i)\n",
    "                i += 1\n",
    "\n",
    "                try:\n",
    "                    if hasattr(clf, 'predict_proba'):\n",
    "                        yscores = clf.fit(xtrain,ytrain).predict_proba(X_test)[:,1]\n",
    "                    else:\n",
    "                        yscores = clf.fit(xtrain,ytrain).decision_function(X_test)\n",
    "\n",
    "                    scores[name][str(p)].append(yscores)\n",
    "\n",
    "                except IndexError:\n",
    "                    continue\n",
    "\n",
    "    return scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n"
     ]
    }
   ],
   "source": [
    "scores = classify(X_train, Y_train, X_test, Y_test, ['LR'], 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "def get_preds(yscores, thresh):\n",
    "    return [1 if score >= thresh else 0 for score in yscores]\n",
    "\n",
    "def get_best_model(scores, y):\n",
    "    max_met = 0\n",
    "    max_thresh = 0\n",
    "    best_params = ''\n",
    "    thresholds = [i / 10.0 for i in range(10)]\n",
    "    for params, score_array in scores['LR'].items():\n",
    "        for thresh in thresholds:\n",
    "            tot_met = []\n",
    "            metric = []\n",
    "            for score in score_array:\n",
    "                yhat = get_preds(score, thresh)\n",
    "                met_val = accuracy_score(y, yhat)\n",
    "                metric.append(met_val)\n",
    "            tot_met = sum(metric)/len(metric)\n",
    "            if tot_met > max_met:\n",
    "                max_met = tot_met\n",
    "                max_thresh = thresh\n",
    "                best_params = params\n",
    "    return max_met, max_thresh, best_params\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.96741009221116436, 0.1, \"{'penalty': 'l1', 'C': 10}\")"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "metric, threshold, parameters = get_best_model(scores, np.array(Y_test))\n",
    "metric, threshold, parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While it is true that accuracy is very high, the problem is that it seems every observation is just being labeled as not receiving a donation from a thoughtful donor. Even using other metrics (e.g. auc-roc, accuracy, recall) is not useful to get a good model, when using only Logistic Regression. It would be useful exploring other models/algorithms to get better predictions. \n",
    "\n",
    "There's also the fact that there are a lot of features in the model. It could be better to restrict features to just a few and see how they perform. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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