IRS990_Tech_Exercise_CLUSTERING.ipynb

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    "# Data Insights Take-Home: Clustering on Expenses and Revenues\n",
    "**Marianne C. Halloran\n",
    "October 12, 2017**\n",
    "<br>\n",
    "<br>\n",
    "<p>*** The principal value of detailing the financial information obtained in Form 990 is to bring insight and arrive at data-backed conclusions about the NPOs, and their ability to garner financial support to continue operations. ***\n",
    "Here, The idea is that, by understanding how a NPO obtains revenue and spends its funds, we will be better poised to understand its efficacy. It also answers the questions of the financial strength of the NPO (its ability to attract resources, level of reserves, financial accountability, etc).\n",
    "\n",
    "#### Data selection rationale and visualizations\n",
    "First, I generate visualizations of the sources of income for 501(c)(3) NPOs, based on fundraising, campaign, membership, government grants, gifts and service revenues. This can provide insights into the income nature of the NPOs. Some NPOs can receive most of their funds from chargings fees, or through government grants. *To some individuals, this can often play an important factor in their donation decision*.\n",
    "\n",
    "Similarly, I generate visualizations of the expenses, based on functional, service, management, and fundraising expenses. Individuals interested in NPOs can be interested in how the NPOs are spending most of its resources on program matters and not on management or fundraising, for example. \n",
    "\n",
    "Net assets provide some indication of the level of resources the filer has to help support its activities in the future. \n",
    "\n",
    "Moreover, compensation of its employees versus its income and expenditure can bring important information about the NPOs and their financial health and resource allocation.\n",
    "\n",
    "Here, I perform a basic clustering for three features: ***total compensation***, **total income plus assets**, and ***total expenses***. The idea behind this selection would  be to identify similar NPOs and the relationship between the three features: is there an inherent separation in the data?\n",
    "\n",
    "#### Pre-processing and Clustering\n",
    "Data was normalized using z-scores.\n",
    "\t\n",
    "I chose ***z-score normalization*** although all data is given in dollars, these values necessarily comparable. Standardizing them using z-scores is a best-practice to give it equal weights by minimizing the error function using the Newton algorithm, i.e. a gradient-based optimization algorithm. Normalizing the data improves convergence of such algorithms.\n",
    "\n",
    "**Suggestions:**\n",
    "\n",
    "- Financial information is more meaningful if viewed over a period of several years, seeing how organizations can change over time. A single year's Form 990 provides only a snapshot in time. "
   ]
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    "#=================================================================#\n",
    "# LIBRARIES                                                       #\n",
    "#=================================================================#\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "from scipy.spatial.distance import cdist\n",
    "from pylab import rcParams\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 8,8"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Cleaned data, removed NaN\n"
     ]
    },
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>org_name</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>activity</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
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       "      <th>rev_campaigns</th>\n",
       "      <th>...</th>\n",
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       "      <th>total_expenses</th>\n",
       "      <th>total_compensations</th>\n",
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       "      <th>net_assets</th>\n",
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       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>KBL LLP</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Davis &amp; Deal CPAs</td>\n",
       "      <td>GLENDORA</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>CBIZ Tofias</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>RI</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7832</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15049</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>RAYMOND F BOOK &amp; ASSOCIATES PA</td>\n",
       "      <td>DOVER</td>\n",
       "      <td>DE</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO PROVIDE EMERGENCY SHELTER FOR HOMELESS FAMI...</td>\n",
       "      <td>1988</td>\n",
       "      <td>250</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>Larry D Sturgill CPA PC</td>\n",
       "      <td>WISE</td>\n",
       "      <td>VA</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO PROVIDE SUPPORT TO THE LOCAL FIRE DEPARTMEN...</td>\n",
       "      <td>2007</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>MORGENSTERN WAXMAN ELLERSHAW</td>\n",
       "      <td>DETROIT</td>\n",
       "      <td>MI</td>\n",
       "      <td>2014</td>\n",
       "      <td>HELPS FAITH GROUPS WHO CARE FOR THE HOMELESS R...</td>\n",
       "      <td>2009</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6107</td>\n",
       "      <td>384828</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>252121</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>Douglass Mischley and Associates</td>\n",
       "      <td>ELK GROVE</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>Chek Tan and Company</td>\n",
       "      <td>SAN FRANCISCO</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15794</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>RUBINO AND COMPANY CHARTERED</td>\n",
       "      <td>ROCKVILLE</td>\n",
       "      <td>MD</td>\n",
       "      <td>2014</td>\n",
       "      <td>TRANSPORTATION RELATED EDUCATION</td>\n",
       "      <td>1987</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>NEW HORIZON ACADEMY FOR EXCEPTIONAL STUDENTSINC</td>\n",
       "      <td>Ocala</td>\n",
       "      <td>FL</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>162890</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22472</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>ROBERTS ALEXONIS GROUP PLLC</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>AZ</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>137300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17389</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>MYTEAM TRIUMPH INC</td>\n",
       "      <td>ADA</td>\n",
       "      <td>MI</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>51143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>Dittrich &amp; Associates PLLC</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42650</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65349</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>MITCHELL &amp; CO PC</td>\n",
       "      <td>LEESBURG</td>\n",
       "      <td>VA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>34981</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31675</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>ERICKSON DEMEL &amp; CO PLLC</td>\n",
       "      <td>AUSTIN</td>\n",
       "      <td>TX</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>173337</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22880</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>MURPHY &amp; MURPHY CPA LLC</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>DC</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3863</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70748</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>SCHEULEN PATCHETT &amp; EDWARDS PC</td>\n",
       "      <td>WARRENTON</td>\n",
       "      <td>VA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>34657</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97838</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>Grace Tax Advisory Group LLC</td>\n",
       "      <td>North Fort Myers</td>\n",
       "      <td>FL</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>102701</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>BEREA ROTARY FOUNDATION INC</td>\n",
       "      <td>BEREA</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59413</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>Parmelee Poirier &amp; Associates LLP</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>RI</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO ENCOURAGE CHARACTER BUILDING THROUGH SAIL T...</td>\n",
       "      <td>1973</td>\n",
       "      <td>34</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>31205</td>\n",
       "      <td>608126</td>\n",
       "      <td>80000</td>\n",
       "      <td>0</td>\n",
       "      <td>112584</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>ROBERT C ALARIO CPA PC</td>\n",
       "      <td>WORCESTER</td>\n",
       "      <td>MA</td>\n",
       "      <td>2014</td>\n",
       "      <td>MISSIONARIES OF OUR LADY OF HOPE IS A COMMUNIT...</td>\n",
       "      <td>1994</td>\n",
       "      <td>300</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>212968</td>\n",
       "      <td>39750</td>\n",
       "      <td>0</td>\n",
       "      <td>80628</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>HENDERSON HUTCHERSON &amp; MCCULLOUGH PLLC</td>\n",
       "      <td>CHATTANOOGA</td>\n",
       "      <td>TN</td>\n",
       "      <td>2014</td>\n",
       "      <td>THE RIVERCITY COMPANY'S MISSION IS TO ASSIST A...</td>\n",
       "      <td>1986</td>\n",
       "      <td>70</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1426413</td>\n",
       "      <td>265316</td>\n",
       "      <td>1</td>\n",
       "      <td>20559123</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>GARRIS AND COMPANY PC</td>\n",
       "      <td>CHARLOTTESVILLE</td>\n",
       "      <td>VA</td>\n",
       "      <td>2014</td>\n",
       "      <td>ESTABLISH AND ADMINISTER AN ENERGENCY MEDICAL ...</td>\n",
       "      <td>1978</td>\n",
       "      <td>22</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>461512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>191521</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>WILKE &amp; ASSOCIATES LLP</td>\n",
       "      <td>WEXFORD</td>\n",
       "      <td>PA</td>\n",
       "      <td>2014</td>\n",
       "      <td>PRIMARY EXEMPT PURPOSES: TO BUILD A FACILITY T...</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4520</td>\n",
       "      <td>102044</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>885128</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>OTIS ATWELL</td>\n",
       "      <td>SOUTH BURLINGTON</td>\n",
       "      <td>VT</td>\n",
       "      <td>2014</td>\n",
       "      <td>1- ADVOCATING FOR IMPROVING SKI RACING SAFETY....</td>\n",
       "      <td>2006</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>81061</td>\n",
       "      <td>286680</td>\n",
       "      <td>59285</td>\n",
       "      <td>0</td>\n",
       "      <td>215426</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>Shafer &amp; MacRae CPAs</td>\n",
       "      <td>TEMECULA</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>SERVING THE HAITIAN COMMUNITY &amp; SURROUNDING AR...</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>557347</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>645836</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>PSK LLP</td>\n",
       "      <td>IRVING</td>\n",
       "      <td>TX</td>\n",
       "      <td>2014</td>\n",
       "      <td>WE PARTNER WITH OTHER ORGANIZATIONS THAT ARE D...</td>\n",
       "      <td>2010</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>302061</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72675</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0</td>\n",
       "      <td>WEBSTER &amp; KIRK PLLC</td>\n",
       "      <td>FRANKFORT</td>\n",
       "      <td>KY</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>76001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25483</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0</td>\n",
       "      <td>CORBETS &amp; ASSOCIATES INC</td>\n",
       "      <td>CLEVELAND</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>BALLET IN CLEVELAND IS REVOLUTIONIZING BALLET ...</td>\n",
       "      <td>2013</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7000</td>\n",
       "      <td>139974</td>\n",
       "      <td>28000</td>\n",
       "      <td>0</td>\n",
       "      <td>6414</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0</td>\n",
       "      <td>Strand &amp; Associates</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>WA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>102791</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>93897</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38440</th>\n",
       "      <td>0</td>\n",
       "      <td>Dwight Nakata CPA CFPR</td>\n",
       "      <td>CERRITOS</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>17019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8226</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38441</th>\n",
       "      <td>0</td>\n",
       "      <td>MARGARET MATTHEWS CPA PS</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>88920</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>86171</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38442</th>\n",
       "      <td>0</td>\n",
       "      <td>JM SOLUTIONS LLC</td>\n",
       "      <td>KLAMATH FALLS</td>\n",
       "      <td>OR</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>92632</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99601</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38483</th>\n",
       "      <td>0</td>\n",
       "      <td>Abhishek R Agrawal</td>\n",
       "      <td>Fairfield</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>The Leaven's mission is to provide an educatio...</td>\n",
       "      <td>2008</td>\n",
       "      <td>60</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>77338</td>\n",
       "      <td>660462</td>\n",
       "      <td>62917</td>\n",
       "      <td>0</td>\n",
       "      <td>58094</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38484</th>\n",
       "      <td>0</td>\n",
       "      <td>OMEGA PSI PHI FRATERNITY NU OMICRON CHAPTER EC...</td>\n",
       "      <td>SOUTH OZONE PARK</td>\n",
       "      <td>NY</td>\n",
       "      <td>2013</td>\n",
       "      <td>To provide day care services to children of wo...</td>\n",
       "      <td>1984</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>974355</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9879</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38485</th>\n",
       "      <td>0</td>\n",
       "      <td>STEPHANIE ZILL</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>Beverly Vermont Community Land Trust is a comm...</td>\n",
       "      <td>2008</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>57644</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>580008</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38486</th>\n",
       "      <td>0</td>\n",
       "      <td>KARL HAISER CPA</td>\n",
       "      <td>FLINT</td>\n",
       "      <td>MI</td>\n",
       "      <td>2014</td>\n",
       "      <td>COMMUNITY STAGE PRODUCTIONS ARE FEATURED FOR A...</td>\n",
       "      <td>1928</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>174390</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>852133</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38487</th>\n",
       "      <td>0</td>\n",
       "      <td>EMILY A DEWALD EA</td>\n",
       "      <td>PORT TREVORTON</td>\n",
       "      <td>PA</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO CREATE A SAFE, LOVING, CARING, SANCTUARY FO...</td>\n",
       "      <td>2004</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>53910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5489</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38488</th>\n",
       "      <td>0</td>\n",
       "      <td>RICHARD V RUDOLPH CPA</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>2014</td>\n",
       "      <td>JOB COUNSELING AND PLACEMENT</td>\n",
       "      <td>1979</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1324816</td>\n",
       "      <td>201730</td>\n",
       "      <td>0</td>\n",
       "      <td>104349</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38489</th>\n",
       "      <td>0</td>\n",
       "      <td>SECHLER CPA PC</td>\n",
       "      <td>SCOTTSDALE</td>\n",
       "      <td>AZ</td>\n",
       "      <td>2014</td>\n",
       "      <td>ACTIVELY PROMOTES AND SECURE GIFTS WHILE EDUCA...</td>\n",
       "      <td>2005</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>67502</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>908704</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38490</th>\n",
       "      <td>0</td>\n",
       "      <td>WICKS BROWN WILLIAMS &amp; CO</td>\n",
       "      <td>SEBRING</td>\n",
       "      <td>FL</td>\n",
       "      <td>2014</td>\n",
       "      <td>ASSIST CHILDREN IN NEED</td>\n",
       "      <td>1993</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4806</td>\n",
       "      <td>564303</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>813884</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38491</th>\n",
       "      <td>0</td>\n",
       "      <td>HIRSCH OELBAUM BRAM HANOVER &amp; LISKER CPA</td>\n",
       "      <td>BROOKLYN</td>\n",
       "      <td>NY</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO SUPPORT ORGANIZATIONS ASSISTING THE POOR,NE...</td>\n",
       "      <td>1950</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>776714</td>\n",
       "      <td>2953759</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>368290</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38492</th>\n",
       "      <td>0</td>\n",
       "      <td>WESSEL &amp; COMPANY CPAS</td>\n",
       "      <td>JOHNSTOWN</td>\n",
       "      <td>PA</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO PROVIDE MEDICAL, THERAPEUTIC, PSYCHOLOGICAL...</td>\n",
       "      <td>1956</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1641105</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1571615</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38493</th>\n",
       "      <td>0</td>\n",
       "      <td>LINDQUIST VON HUSEN &amp; JOYCE LLP</td>\n",
       "      <td>FOSTER CITY</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO PROVIDE HOUSING FOR LOW INCOME PERSONS, WHE...</td>\n",
       "      <td>1998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>609145</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>547941</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38494</th>\n",
       "      <td>0</td>\n",
       "      <td>PDM LLP</td>\n",
       "      <td>LONG BEACH</td>\n",
       "      <td>CA</td>\n",
       "      <td>2014</td>\n",
       "      <td>PROVIDING MENTAL HEALTH CARE TO CHILDREN AND F...</td>\n",
       "      <td>1946</td>\n",
       "      <td>20</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>70882</td>\n",
       "      <td>11934795</td>\n",
       "      <td>882647</td>\n",
       "      <td>5</td>\n",
       "      <td>895420</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38495</th>\n",
       "      <td>0</td>\n",
       "      <td>JOHNSON LAMBERT LLP</td>\n",
       "      <td>RALEIGH</td>\n",
       "      <td>NC</td>\n",
       "      <td>2014</td>\n",
       "      <td>THE MISSION OF THE BOY SCOUTS IS TO PREPARE YO...</td>\n",
       "      <td>1932</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9745</td>\n",
       "      <td>205834</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2498826</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38496</th>\n",
       "      <td>0</td>\n",
       "      <td>ROSEN &amp; FEDERICO</td>\n",
       "      <td>DENVER</td>\n",
       "      <td>CO</td>\n",
       "      <td>2014</td>\n",
       "      <td>THE FOUNDATION FOR ST. MARTIN-IN-THE-FIELDS, L...</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1200</td>\n",
       "      <td>146745</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1621</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38497</th>\n",
       "      <td>0</td>\n",
       "      <td>BOCK &amp; ASSOCIATES LLP</td>\n",
       "      <td>EL PASO</td>\n",
       "      <td>TX</td>\n",
       "      <td>2014</td>\n",
       "      <td>CHRISTIAN EDUCATION</td>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>897</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>143436</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38498</th>\n",
       "      <td>0</td>\n",
       "      <td>Chris Kitchens CPA</td>\n",
       "      <td>Marietta</td>\n",
       "      <td>GA</td>\n",
       "      <td>2014</td>\n",
       "      <td>Alpha Christian Therapy Services provides prof...</td>\n",
       "      <td>1980</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>198363</td>\n",
       "      <td>64800</td>\n",
       "      <td>0</td>\n",
       "      <td>354660</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38499</th>\n",
       "      <td>0</td>\n",
       "      <td>PALMETTO MOLLO MOLINARO &amp; PASSARELLO LLP</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>FL</td>\n",
       "      <td>2014</td>\n",
       "      <td>To produce a fall film festival of internation...</td>\n",
       "      <td>1988</td>\n",
       "      <td>87</td>\n",
       "      <td>7</td>\n",
       "      <td>23680</td>\n",
       "      <td>...</td>\n",
       "      <td>3000</td>\n",
       "      <td>982962</td>\n",
       "      <td>50000</td>\n",
       "      <td>0</td>\n",
       "      <td>355575</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38500</th>\n",
       "      <td>0</td>\n",
       "      <td>Robert J Iracane CPA</td>\n",
       "      <td>PARSIPPANY</td>\n",
       "      <td>NJ</td>\n",
       "      <td>2014</td>\n",
       "      <td>PROVIDED DAY CARE AND MONTESSORI SCHOOL AT AFF...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2451039</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5184604</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38501</th>\n",
       "      <td>0</td>\n",
       "      <td>BERRY DUNN MCNEIL &amp; PARKER LLC</td>\n",
       "      <td>HANOVER</td>\n",
       "      <td>MA</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO OFFER PRIVATE DUTY NURSING AND OTHER AFFORD...</td>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1533444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>456889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38502</th>\n",
       "      <td>0</td>\n",
       "      <td>SMITH DUKES &amp; BUCKALEW LLP</td>\n",
       "      <td>MOBILE</td>\n",
       "      <td>AL</td>\n",
       "      <td>2014</td>\n",
       "      <td>HOUSING FOR LOW INCOME ELDERLY PERSONS</td>\n",
       "      <td>1988</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>537214</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-563586</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38503</th>\n",
       "      <td>0</td>\n",
       "      <td>FUST CHARLES CHAMBERS LLP</td>\n",
       "      <td>NEW HARTFORD</td>\n",
       "      <td>NY</td>\n",
       "      <td>2014</td>\n",
       "      <td>SITRIN STRIVES TO MEET THE NEEDS OF ITS COMMUN...</td>\n",
       "      <td>1991</td>\n",
       "      <td>364</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>22691272</td>\n",
       "      <td>1176208</td>\n",
       "      <td>7</td>\n",
       "      <td>12843485</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38504</th>\n",
       "      <td>0</td>\n",
       "      <td>United Church Residences of Moundsville</td>\n",
       "      <td>Marion</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>To provide safe and affordable housing for low...</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>283079</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1285845</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38505</th>\n",
       "      <td>0</td>\n",
       "      <td>IRIZARRY RODRIGUEZ &amp; CO CPA PSC</td>\n",
       "      <td>BAYAMON</td>\n",
       "      <td>PR</td>\n",
       "      <td>2014</td>\n",
       "      <td>MISSIONARY WORK</td>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>200771</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7031</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38506</th>\n",
       "      <td>0</td>\n",
       "      <td>Dittrick &amp; Associates Inc</td>\n",
       "      <td>Chagrin Falls</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>To provide assistance to law enforcement offic...</td>\n",
       "      <td>1997</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>89664</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1006260</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38507</th>\n",
       "      <td>0</td>\n",
       "      <td>MATTHEWS CARTER &amp; BOYCE</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>DC</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO ADVOCATE AND PROMOTE TRANSPORTATION DEVELOP...</td>\n",
       "      <td>1985</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>38074</td>\n",
       "      <td>3205233</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>699188</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38508</th>\n",
       "      <td>0</td>\n",
       "      <td>WARNER &amp; WARNER CPA'S INC</td>\n",
       "      <td>CARROLLTON</td>\n",
       "      <td>OH</td>\n",
       "      <td>2014</td>\n",
       "      <td>TO PROVIDE SKILLED NURSING, PHYSICAL THERAPY, ...</td>\n",
       "      <td>1977</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>719063</td>\n",
       "      <td>128846</td>\n",
       "      <td>0</td>\n",
       "      <td>167084</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38509</th>\n",
       "      <td>0</td>\n",
       "      <td>Brown and Company</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>2014</td>\n",
       "      <td>The mission of Family and Medical Counseling S...</td>\n",
       "      <td>1976</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7637066</td>\n",
       "      <td>884242</td>\n",
       "      <td>5</td>\n",
       "      <td>1532884</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25244 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status                                           org_name  \\\n",
       "1               0                                            KBL LLP   \n",
       "2               0                                  Davis & Deal CPAs   \n",
       "3               0                                        CBIZ Tofias   \n",
       "4               0                     RAYMOND F BOOK & ASSOCIATES PA   \n",
       "5               0                            Larry D Sturgill CPA PC   \n",
       "6               0                       MORGENSTERN WAXMAN ELLERSHAW   \n",
       "7               0                   Douglass Mischley and Associates   \n",
       "8               0                               Chek Tan and Company   \n",
       "9               0                       RUBINO AND COMPANY CHARTERED   \n",
       "11              0    NEW HORIZON ACADEMY FOR EXCEPTIONAL STUDENTSINC   \n",
       "12              0                        ROBERTS ALEXONIS GROUP PLLC   \n",
       "13              0                                 MYTEAM TRIUMPH INC   \n",
       "14              0                         Dittrich & Associates PLLC   \n",
       "15              0                                   MITCHELL & CO PC   \n",
       "16              0                           ERICKSON DEMEL & CO PLLC   \n",
       "17              0                            MURPHY & MURPHY CPA LLC   \n",
       "18              0                     SCHEULEN PATCHETT & EDWARDS PC   \n",
       "19              0                       Grace Tax Advisory Group LLC   \n",
       "20              0                        BEREA ROTARY FOUNDATION INC   \n",
       "21              0                  Parmelee Poirier & Associates LLP   \n",
       "22              0                             ROBERT C ALARIO CPA PC   \n",
       "23              0             HENDERSON HUTCHERSON & MCCULLOUGH PLLC   \n",
       "24              0                              GARRIS AND COMPANY PC   \n",
       "25              0                             WILKE & ASSOCIATES LLP   \n",
       "26              0                                        OTIS ATWELL   \n",
       "28              0                               Shafer & MacRae CPAs   \n",
       "29              0                                            PSK LLP   \n",
       "33              0                                WEBSTER & KIRK PLLC   \n",
       "34              0                           CORBETS & ASSOCIATES INC   \n",
       "35              0                                Strand & Associates   \n",
       "...           ...                                                ...   \n",
       "38440           0                             Dwight Nakata CPA CFPR   \n",
       "38441           0                           MARGARET MATTHEWS CPA PS   \n",
       "38442           0                                   JM SOLUTIONS LLC   \n",
       "38483           0                                 Abhishek R Agrawal   \n",
       "38484           0  OMEGA PSI PHI FRATERNITY NU OMICRON CHAPTER EC...   \n",
       "38485           0                                     STEPHANIE ZILL   \n",
       "38486           0                                    KARL HAISER CPA   \n",
       "38487           0                                  EMILY A DEWALD EA   \n",
       "38488           0                              RICHARD V RUDOLPH CPA   \n",
       "38489           0                                     SECHLER CPA PC   \n",
       "38490           0                          WICKS BROWN WILLIAMS & CO   \n",
       "38491           0           HIRSCH OELBAUM BRAM HANOVER & LISKER CPA   \n",
       "38492           0                              WESSEL & COMPANY CPAS   \n",
       "38493           0                    LINDQUIST VON HUSEN & JOYCE LLP   \n",
       "38494           0                                            PDM LLP   \n",
       "38495           0                                JOHNSON LAMBERT LLP   \n",
       "38496           0                                   ROSEN & FEDERICO   \n",
       "38497           0                              BOCK & ASSOCIATES LLP   \n",
       "38498           0                                 Chris Kitchens CPA   \n",
       "38499           0           PALMETTO MOLLO MOLINARO & PASSARELLO LLP   \n",
       "38500           0                               Robert J Iracane CPA   \n",
       "38501           0                     BERRY DUNN MCNEIL & PARKER LLC   \n",
       "38502           0                         SMITH DUKES & BUCKALEW LLP   \n",
       "38503           0                          FUST CHARLES CHAMBERS LLP   \n",
       "38504           0            United Church Residences of Moundsville   \n",
       "38505           0                    IRIZARRY RODRIGUEZ & CO CPA PSC   \n",
       "38506           0                          Dittrick & Associates Inc   \n",
       "38507           0                            MATTHEWS CARTER & BOYCE   \n",
       "38508           0                          WARNER & WARNER CPA'S INC   \n",
       "38509           0                                  Brown and Company   \n",
       "\n",
       "                   city state  tax_year  \\\n",
       "1              BROOKLYN    NY      2014   \n",
       "2              GLENDORA    CA      2014   \n",
       "3               NEWPORT    RI      2014   \n",
       "4                 DOVER    DE      2014   \n",
       "5                  WISE    VA      2014   \n",
       "6               DETROIT    MI      2014   \n",
       "7             ELK GROVE    CA      2014   \n",
       "8         SAN FRANCISCO    CA      2014   \n",
       "9             ROCKVILLE    MD      2014   \n",
       "11                Ocala    FL      2013   \n",
       "12               Tucson    AZ      2014   \n",
       "13                  ADA    MI      2014   \n",
       "14           Cincinnati    OH      2014   \n",
       "15             LEESBURG    VA      2014   \n",
       "16               AUSTIN    TX      2014   \n",
       "17           WASHINGTON    DC      2014   \n",
       "18            WARRENTON    VA      2014   \n",
       "19     North Fort Myers    FL      2014   \n",
       "20                BEREA    OH      2014   \n",
       "21              NEWPORT    RI      2014   \n",
       "22            WORCESTER    MA      2014   \n",
       "23          CHATTANOOGA    TN      2014   \n",
       "24      CHARLOTTESVILLE    VA      2014   \n",
       "25              WEXFORD    PA      2014   \n",
       "26     SOUTH BURLINGTON    VT      2014   \n",
       "28             TEMECULA    CA      2014   \n",
       "29               IRVING    TX      2014   \n",
       "33            FRANKFORT    KY      2014   \n",
       "34            CLEVELAND    OH      2014   \n",
       "35               Tacoma    WA      2014   \n",
       "...                 ...   ...       ...   \n",
       "38440          CERRITOS    CA      2014   \n",
       "38441           Seattle    WA      2014   \n",
       "38442     KLAMATH FALLS    OR      2014   \n",
       "38483         Fairfield    CA      2014   \n",
       "38484  SOUTH OZONE PARK    NY      2013   \n",
       "38485       Los Angeles    CA      2014   \n",
       "38486             FLINT    MI      2014   \n",
       "38487    PORT TREVORTON    PA      2014   \n",
       "38488          NEW YORK    NY      2014   \n",
       "38489        SCOTTSDALE    AZ      2014   \n",
       "38490           SEBRING    FL      2014   \n",
       "38491          BROOKLYN    NY      2014   \n",
       "38492         JOHNSTOWN    PA      2014   \n",
       "38493       FOSTER CITY    CA      2014   \n",
       "38494        LONG BEACH    CA      2014   \n",
       "38495           RALEIGH    NC      2014   \n",
       "38496            DENVER    CO      2014   \n",
       "38497           EL PASO    TX      2014   \n",
       "38498          Marietta    GA      2014   \n",
       "38499   Fort Lauderdale    FL      2014   \n",
       "38500        PARSIPPANY    NJ      2014   \n",
       "38501           HANOVER    MA      2014   \n",
       "38502            MOBILE    AL      2014   \n",
       "38503      NEW HARTFORD    NY      2014   \n",
       "38504            Marion    OH      2014   \n",
       "38505           BAYAMON    PR      2014   \n",
       "38506     Chagrin Falls    OH      2014   \n",
       "38507        WASHINGTON    DC      2014   \n",
       "38508        CARROLLTON    OH      2014   \n",
       "38509        Washington    DC      2014   \n",
       "\n",
       "                                                activity  year_formed  \\\n",
       "1                                                      0            0   \n",
       "2                                                      0            0   \n",
       "3                                                      0            0   \n",
       "4      TO PROVIDE EMERGENCY SHELTER FOR HOMELESS FAMI...         1988   \n",
       "5      TO PROVIDE SUPPORT TO THE LOCAL FIRE DEPARTMEN...         2007   \n",
       "6      HELPS FAITH GROUPS WHO CARE FOR THE HOMELESS R...         2009   \n",
       "7                                                      0            0   \n",
       "8                                                      0            0   \n",
       "9                       TRANSPORTATION RELATED EDUCATION         1987   \n",
       "11                                                     0            0   \n",
       "12                                                     0            0   \n",
       "13                                                     0            0   \n",
       "14                                                     0            0   \n",
       "15                                                     0            0   \n",
       "16                                                     0            0   \n",
       "17                                                     0            0   \n",
       "18                                                     0            0   \n",
       "19                                                     0            0   \n",
       "20                                                     0            0   \n",
       "21     TO ENCOURAGE CHARACTER BUILDING THROUGH SAIL T...         1973   \n",
       "22     MISSIONARIES OF OUR LADY OF HOPE IS A COMMUNIT...         1994   \n",
       "23     THE RIVERCITY COMPANY'S MISSION IS TO ASSIST A...         1986   \n",
       "24     ESTABLISH AND ADMINISTER AN ENERGENCY MEDICAL ...         1978   \n",
       "25     PRIMARY EXEMPT PURPOSES: TO BUILD A FACILITY T...         2000   \n",
       "26     1- ADVOCATING FOR IMPROVING SKI RACING SAFETY....         2006   \n",
       "28     SERVING THE HAITIAN COMMUNITY & SURROUNDING AR...         2000   \n",
       "29     WE PARTNER WITH OTHER ORGANIZATIONS THAT ARE D...         2010   \n",
       "33                                                     0            0   \n",
       "34     BALLET IN CLEVELAND IS REVOLUTIONIZING BALLET ...         2013   \n",
       "35                                                     0            0   \n",
       "...                                                  ...          ...   \n",
       "38440                                                  0            0   \n",
       "38441                                                  0            0   \n",
       "38442                                                  0            0   \n",
       "38483  The Leaven's mission is to provide an educatio...         2008   \n",
       "38484  To provide day care services to children of wo...         1984   \n",
       "38485  Beverly Vermont Community Land Trust is a comm...         2008   \n",
       "38486  COMMUNITY STAGE PRODUCTIONS ARE FEATURED FOR A...         1928   \n",
       "38487  TO CREATE A SAFE, LOVING, CARING, SANCTUARY FO...         2004   \n",
       "38488                       JOB COUNSELING AND PLACEMENT         1979   \n",
       "38489  ACTIVELY PROMOTES AND SECURE GIFTS WHILE EDUCA...         2005   \n",
       "38490                            ASSIST CHILDREN IN NEED         1993   \n",
       "38491  TO SUPPORT ORGANIZATIONS ASSISTING THE POOR,NE...         1950   \n",
       "38492  TO PROVIDE MEDICAL, THERAPEUTIC, PSYCHOLOGICAL...         1956   \n",
       "38493  TO PROVIDE HOUSING FOR LOW INCOME PERSONS, WHE...         1998   \n",
       "38494  PROVIDING MENTAL HEALTH CARE TO CHILDREN AND F...         1946   \n",
       "38495  THE MISSION OF THE BOY SCOUTS IS TO PREPARE YO...         1932   \n",
       "38496  THE FOUNDATION FOR ST. MARTIN-IN-THE-FIELDS, L...         2003   \n",
       "38497                                CHRISTIAN EDUCATION         2005   \n",
       "38498  Alpha Christian Therapy Services provides prof...         1980   \n",
       "38499  To produce a fall film festival of internation...         1988   \n",
       "38500  PROVIDED DAY CARE AND MONTESSORI SCHOOL AT AFF...            0   \n",
       "38501  TO OFFER PRIVATE DUTY NURSING AND OTHER AFFORD...         2010   \n",
       "38502             HOUSING FOR LOW INCOME ELDERLY PERSONS         1988   \n",
       "38503  SITRIN STRIVES TO MEET THE NEEDS OF ITS COMMUN...         1991   \n",
       "38504  To provide safe and affordable housing for low...         1994   \n",
       "38505                                    MISSIONARY WORK         2013   \n",
       "38506  To provide assistance to law enforcement offic...         1997   \n",
       "38507  TO ADVOCATE AND PROMOTE TRANSPORTATION DEVELOP...         1985   \n",
       "38508  TO PROVIDE SKILLED NURSING, PHYSICAL THERAPY, ...         1977   \n",
       "38509  The mission of Family and Medical Counseling S...         1976   \n",
       "\n",
       "       volunteer_ct  employee_ct  rev_campaigns       ...        \\\n",
       "1                 0            0              0       ...         \n",
       "2                 0            0              0       ...         \n",
       "3                 0            0              0       ...         \n",
       "4               250           15              0       ...         \n",
       "5                 6            0              0       ...         \n",
       "6               100            0              0       ...         \n",
       "7                 0            0              0       ...         \n",
       "8                 0            0              0       ...         \n",
       "9                26            0              0       ...         \n",
       "11                0            0              0       ...         \n",
       "12                0            0              0       ...         \n",
       "13                0            0              0       ...         \n",
       "14                0            0              0       ...         \n",
       "15                0            0              0       ...         \n",
       "16                0            0              0       ...         \n",
       "17                0            0              0       ...         \n",
       "18                0            0              0       ...         \n",
       "19                0            0              0       ...         \n",
       "20                0            0              0       ...         \n",
       "21               34            6              0       ...         \n",
       "22              300            4              0       ...         \n",
       "23               70            9              0       ...         \n",
       "24               22            8              0       ...         \n",
       "25                5            0              0       ...         \n",
       "26              100            1              0       ...         \n",
       "28                0            0              0       ...         \n",
       "29               35            0              0       ...         \n",
       "33                0            0              0       ...         \n",
       "34               15            0              0       ...         \n",
       "35                0            0              0       ...         \n",
       "...             ...          ...            ...       ...         \n",
       "38440             0            0              0       ...         \n",
       "38441             0            0              0       ...         \n",
       "38442             0            0              0       ...         \n",
       "38483            60           23              0       ...         \n",
       "38484             9           19              0       ...         \n",
       "38485            20            0              0       ...         \n",
       "38486             0            2              0       ...         \n",
       "38487            10            0              0       ...         \n",
       "38488             0           13              0       ...         \n",
       "38489             9            0              0       ...         \n",
       "38490            23           11              0       ...         \n",
       "38491             0            9              0       ...         \n",
       "38492             0            0              0       ...         \n",
       "38493             0            0              0       ...         \n",
       "38494            20          157              0       ...         \n",
       "38495           125            0              0       ...         \n",
       "38496             0            0              0       ...         \n",
       "38497             0            0              0       ...         \n",
       "38498             0            2              0       ...         \n",
       "38499            87            7          23680       ...         \n",
       "38500             0            0              0       ...         \n",
       "38501             0          145              0       ...         \n",
       "38502            14            0              0       ...         \n",
       "38503           364          608              0       ...         \n",
       "38504             0            0              0       ...         \n",
       "38505             0            0              0       ...         \n",
       "38506           100            0              0       ...         \n",
       "38507             9            0              0       ...         \n",
       "38508             0           13              0       ...         \n",
       "38509             0            0              0       ...         \n",
       "\n",
       "       exp_fundraising  total_expenses  total_compensations  comp_more100k  \\\n",
       "1                    0          115662                    0              0   \n",
       "2                    0           33918                    0              0   \n",
       "3                    0            7832                    0              0   \n",
       "4                    0          464318                44864              0   \n",
       "5                    0           27439                    0              0   \n",
       "6                 6107          384828                    0              0   \n",
       "7                    0           52857                    0              0   \n",
       "8                    0           19116                    0              0   \n",
       "9                 1673           50912                    0              0   \n",
       "11                   0          162890                    0              0   \n",
       "12                   0          137300                    0              0   \n",
       "13                   0           51143                    0              0   \n",
       "14                   0           42650                    0              0   \n",
       "15                   0           34981                    0              0   \n",
       "16                   0          173337                    0              0   \n",
       "17                   0            3863                    0              0   \n",
       "18                   0           34657                    0              0   \n",
       "19                   0          102701                    0              0   \n",
       "20                   0             250                    0              0   \n",
       "21               31205          608126                80000              0   \n",
       "22                   0          212968                39750              0   \n",
       "23                   0         1426413               265316              1   \n",
       "24                   0          461512                    0              0   \n",
       "25                4520          102044                    0              0   \n",
       "26               81061          286680                59285              0   \n",
       "28                   0          557347                    0              0   \n",
       "29                   0          302061                    0              0   \n",
       "33                   0           76001                    0              0   \n",
       "34                7000          139974                28000              0   \n",
       "35                   0          102791                    0              0   \n",
       "...                ...             ...                  ...            ...   \n",
       "38440                0           17019                    0              0   \n",
       "38441                0           88920                    0              0   \n",
       "38442                0           92632                    0              0   \n",
       "38483            77338          660462                62917              0   \n",
       "38484                0          974355                    0              0   \n",
       "38485                0           57644                    0              0   \n",
       "38486                0          174390                    0              0   \n",
       "38487                0           53910                    0              0   \n",
       "38488                0         1324816               201730              0   \n",
       "38489                0           67502                    0              0   \n",
       "38490             4806          564303                    0              0   \n",
       "38491           776714         2953759                    0              1   \n",
       "38492                0         1641105                    0              0   \n",
       "38493                0          609145                    0              0   \n",
       "38494            70882        11934795               882647              5   \n",
       "38495             9745          205834                    0              0   \n",
       "38496             1200          146745                    0              0   \n",
       "38497                0             897                    0              0   \n",
       "38498                0          198363                64800              0   \n",
       "38499             3000          982962                50000              0   \n",
       "38500                0         2451039                    0              0   \n",
       "38501                0         1533444                    0              0   \n",
       "38502                0          537214                    0              0   \n",
       "38503                0        22691272              1176208              7   \n",
       "38504                0          283079                    0              0   \n",
       "38505                0          200771                    0              0   \n",
       "38506                0           89664                    0              0   \n",
       "38507            38074         3205233                    0              0   \n",
       "38508                0          719063               128846              0   \n",
       "38509                0         7637066               884242              5   \n",
       "\n",
       "       net_assets  pol_act  lob_act  foreign_office  foreign_fundraising  \\\n",
       "1            1843        1        1               1                    2   \n",
       "2           54443        1        1               1                    2   \n",
       "3           15049        1        1               1                    2   \n",
       "4          731151        1        1               1                    1   \n",
       "5          107296        1        1               1                    1   \n",
       "6          252121        1        1               1                    1   \n",
       "7            6299        1        1               1                    2   \n",
       "8           15794        1        1               1                    2   \n",
       "9         1547356        1        1               1                    1   \n",
       "11          22472        1        1               1                    2   \n",
       "12          17389        1        1               1                    2   \n",
       "13          22380        1        1               1                    2   \n",
       "14          65349        1        1               1                    2   \n",
       "15          31675        1        1               1                    2   \n",
       "16          22880        1        1               1                    2   \n",
       "17          70748        1        1               1                    2   \n",
       "18          97838        1        1               1                    2   \n",
       "19           4100        1        1               1                    2   \n",
       "20          59413        1        1               1                    2   \n",
       "21         112584        1        1               1                    1   \n",
       "22          80628        1        1               1                    1   \n",
       "23       20559123        1        1               1                    1   \n",
       "24         191521        1        1               1                    1   \n",
       "25         885128        1        1               1                    1   \n",
       "26         215426        1        1               1                    1   \n",
       "28         645836        1        1               1                    1   \n",
       "29          72675        1        1               1                    1   \n",
       "33          25483        1        1               1                    2   \n",
       "34           6414        1        1               1                    1   \n",
       "35          93897        1        1               1                    2   \n",
       "...           ...      ...      ...             ...                  ...   \n",
       "38440        8226        1        1               1                    2   \n",
       "38441       86171        1        1               1                    2   \n",
       "38442       99601        1        1               2                    2   \n",
       "38483       58094        1        1               1                    1   \n",
       "38484        9879        1        1               1                    1   \n",
       "38485      580008        1        1               1                    1   \n",
       "38486      852133        1        1               1                    1   \n",
       "38487        5489        1        1               1                    1   \n",
       "38488      104349        1        1               1                    1   \n",
       "38489      908704        1        1               1                    1   \n",
       "38490      813884        1        1               1                    1   \n",
       "38491      368290        1        1               1                    1   \n",
       "38492     1571615        1        1               1                    1   \n",
       "38493      547941        1        1               1                    1   \n",
       "38494      895420        1        1               1                    1   \n",
       "38495     2498826        1        1               1                    1   \n",
       "38496        1621        1        1               1                    1   \n",
       "38497      143436        1        1               1                    1   \n",
       "38498      354660        1        1               1                    1   \n",
       "38499      355575        1        1               1                    1   \n",
       "38500     5184604        1        1               1                    1   \n",
       "38501      456889        1        1               1                    1   \n",
       "38502     -563586        1        1               1                    1   \n",
       "38503    12843485        1        1               1                    1   \n",
       "38504    -1285845        1        1               1                    1   \n",
       "38505        7031        1        1               1                    1   \n",
       "38506     1006260        1        1               1                    1   \n",
       "38507      699188        1        1               1                    1   \n",
       "38508      167084        1        1               1                    1   \n",
       "38509     1532884        1        1               1                    1   \n",
       "\n",
       "       foreign_assist  \n",
       "1                   2  \n",
       "2                   2  \n",
       "3                   2  \n",
       "4                   1  \n",
       "5                   1  \n",
       "6                   1  \n",
       "7                   2  \n",
       "8                   2  \n",
       "9                   1  \n",
       "11                  2  \n",
       "12                  2  \n",
       "13                  2  \n",
       "14                  2  \n",
       "15                  2  \n",
       "16                  2  \n",
       "17                  2  \n",
       "18                  2  \n",
       "19                  2  \n",
       "20                  2  \n",
       "21                  1  \n",
       "22                  1  \n",
       "23                  1  \n",
       "24                  1  \n",
       "25                  1  \n",
       "26                  1  \n",
       "28                  1  \n",
       "29                  1  \n",
       "33                  2  \n",
       "34                  1  \n",
       "35                  2  \n",
       "...               ...  \n",
       "38440               2  \n",
       "38441               2  \n",
       "38442               2  \n",
       "38483               1  \n",
       "38484               1  \n",
       "38485               1  \n",
       "38486               1  \n",
       "38487               1  \n",
       "38488               1  \n",
       "38489               1  \n",
       "38490               1  \n",
       "38491               1  \n",
       "38492               1  \n",
       "38493               1  \n",
       "38494               1  \n",
       "38495               1  \n",
       "38496               1  \n",
       "38497               1  \n",
       "38498               1  \n",
       "38499               1  \n",
       "38500               1  \n",
       "38501               1  \n",
       "38502               1  \n",
       "38503               1  \n",
       "38504               1  \n",
       "38505               1  \n",
       "38506               1  \n",
       "38507               1  \n",
       "38508               1  \n",
       "38509               1  \n",
       "\n",
       "[25244 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# DATA IMPORT                                                    #\n",
    "#=================================================================#\n",
    "meta = pd.read_csv('input/NPO_meta_38k.csv')\n",
    "meta.columns = ['EIN','contract_term','tax_status','org_name','city','state','tax_year',\n",
    "                'activity','year_formed','volunteer_ct','employee_ct','rev_campaigns',\n",
    "                'rev_membership', 'rev_fundraising','rev_govgrants','rev_other','rev_progserv',\n",
    "                'rev_netfundraising','total_revenue','total_revenuePY','exp_grants','exp_progserv',\n",
    "                'exp_management','exp_fundraising','total_expenses','total_compensations',\n",
    "                'comp_more100k', 'net_assets','pol_act','lob_act','foreign_office',\n",
    "                'foreign_fundraising','foreign_assist']\n",
    "del meta['EIN'],  meta['contract_term']# meta['activity'],meta['year_formed'],\n",
    "print(u\"\\u0011\",'Cleaned data, removed NaN')\n",
    "\n",
    "\n",
    "# I'm removing any organization that is not a 501(c)(3) and any orgs with NaN in a row\n",
    "meta = meta.dropna(axis=0,how='any')\n",
    "meta_501c3 = meta.loc[meta['tax_status'] == 0]\n",
    "del meta; meta = meta_501c3\n",
    "meta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Descriptive statistics, summarizing central tendency, dispersion\n",
      "  and shape of dataset's distribution\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
       "      <th>employee_ct</th>\n",
       "      <th>rev_campaigns</th>\n",
       "      <th>rev_membership</th>\n",
       "      <th>rev_fundraising</th>\n",
       "      <th>rev_govgrants</th>\n",
       "      <th>rev_other</th>\n",
       "      <th>...</th>\n",
       "      <th>exp_fundraising</th>\n",
       "      <th>total_expenses</th>\n",
       "      <th>total_compensations</th>\n",
       "      <th>comp_more100k</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>pol_act</th>\n",
       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25244.0</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>2.524400e+04</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>25244.000000</td>\n",
       "      <td>25244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.980986</td>\n",
       "      <td>1220.854183</td>\n",
       "      <td>2.920713e+02</td>\n",
       "      <td>51.585723</td>\n",
       "      <td>8.171230e+03</td>\n",
       "      <td>1.752094e+04</td>\n",
       "      <td>3.250311e+04</td>\n",
       "      <td>2.637343e+05</td>\n",
       "      <td>5.857663e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>5.191314e+04</td>\n",
       "      <td>5.480698e+06</td>\n",
       "      <td>1.235607e+05</td>\n",
       "      <td>2.677706</td>\n",
       "      <td>5.150527e+06</td>\n",
       "      <td>1.003209</td>\n",
       "      <td>1.009190</td>\n",
       "      <td>1.006338</td>\n",
       "      <td>1.341745</td>\n",
       "      <td>1.341745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136578</td>\n",
       "      <td>967.141939</td>\n",
       "      <td>1.449340e+04</td>\n",
       "      <td>477.381651</td>\n",
       "      <td>1.450932e+05</td>\n",
       "      <td>5.741884e+05</td>\n",
       "      <td>4.474750e+05</td>\n",
       "      <td>3.956428e+06</td>\n",
       "      <td>7.973029e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>7.604113e+05</td>\n",
       "      <td>6.642740e+07</td>\n",
       "      <td>7.334543e+05</td>\n",
       "      <td>45.066658</td>\n",
       "      <td>6.115887e+07</td>\n",
       "      <td>0.056555</td>\n",
       "      <td>0.095426</td>\n",
       "      <td>0.079361</td>\n",
       "      <td>0.474304</td>\n",
       "      <td>0.474304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-8.310000e+02</td>\n",
       "      <td>-2.623100e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.856000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.372700e+04</td>\n",
       "      <td>-2.581900e+04</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.380901e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.825950e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.728050e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1972.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.858470e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.528275e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.362500e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.551825e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.620250e+03</td>\n",
       "      <td>7.495922e+05</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.176578e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>2.000000e+06</td>\n",
       "      <td>36394.000000</td>\n",
       "      <td>1.203888e+07</td>\n",
       "      <td>6.755227e+07</td>\n",
       "      <td>5.801993e+07</td>\n",
       "      <td>5.147672e+08</td>\n",
       "      <td>7.624086e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>8.391429e+07</td>\n",
       "      <td>4.232123e+09</td>\n",
       "      <td>3.254510e+07</td>\n",
       "      <td>2776.000000</td>\n",
       "      <td>5.057351e+09</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status      tax_year   year_formed  volunteer_ct   employee_ct  \\\n",
       "count     25244.0  25244.000000  25244.000000  2.524400e+04  25244.000000   \n",
       "mean          0.0   2013.980986   1220.854183  2.920713e+02     51.585723   \n",
       "std           0.0      0.136578    967.141939  1.449340e+04    477.381651   \n",
       "min           0.0   2013.000000      0.000000  0.000000e+00      0.000000   \n",
       "25%           0.0   2014.000000      0.000000  0.000000e+00      0.000000   \n",
       "50%           0.0   2014.000000   1972.000000  0.000000e+00      0.000000   \n",
       "75%           0.0   2014.000000   1997.000000  2.100000e+01      8.000000   \n",
       "max           0.0   2014.000000   2015.000000  2.000000e+06  36394.000000   \n",
       "\n",
       "       rev_campaigns  rev_membership  rev_fundraising  rev_govgrants  \\\n",
       "count   2.524400e+04    2.524400e+04     2.524400e+04   2.524400e+04   \n",
       "mean    8.171230e+03    1.752094e+04     3.250311e+04   2.637343e+05   \n",
       "std     1.450932e+05    5.741884e+05     4.474750e+05   3.956428e+06   \n",
       "min     0.000000e+00   -8.310000e+02    -2.623100e+04   0.000000e+00   \n",
       "25%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "50%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "75%     0.000000e+00    0.000000e+00     2.362500e+03   0.000000e+00   \n",
       "max     1.203888e+07    6.755227e+07     5.801993e+07   5.147672e+08   \n",
       "\n",
       "          rev_other       ...        exp_fundraising  total_expenses  \\\n",
       "count  2.524400e+04       ...           2.524400e+04    2.524400e+04   \n",
       "mean   5.857663e+05       ...           5.191314e+04    5.480698e+06   \n",
       "std    7.973029e+06       ...           7.604113e+05    6.642740e+07   \n",
       "min   -1.856000e+03       ...          -3.372700e+04   -2.581900e+04   \n",
       "25%    0.000000e+00       ...           0.000000e+00    5.825950e+04   \n",
       "50%    2.500000e+02       ...           0.000000e+00    1.858470e+05   \n",
       "75%    1.551825e+05       ...           1.620250e+03    7.495922e+05   \n",
       "max    7.624086e+08       ...           8.391429e+07    4.232123e+09   \n",
       "\n",
       "       total_compensations  comp_more100k    net_assets       pol_act  \\\n",
       "count         2.524400e+04   25244.000000  2.524400e+04  25244.000000   \n",
       "mean          1.235607e+05       2.677706  5.150527e+06      1.003209   \n",
       "std           7.334543e+05      45.066658  6.115887e+07      0.056555   \n",
       "min          -1.000000e+00       0.000000 -4.380901e+08      1.000000   \n",
       "25%           0.000000e+00       0.000000  2.728050e+04      1.000000   \n",
       "50%           0.000000e+00       0.000000  1.528275e+05      1.000000   \n",
       "75%           5.000000e+04       0.000000  9.176578e+05      1.000000   \n",
       "max           3.254510e+07    2776.000000  5.057351e+09      2.000000   \n",
       "\n",
       "            lob_act  foreign_office  foreign_fundraising  foreign_assist  \n",
       "count  25244.000000    25244.000000         25244.000000    25244.000000  \n",
       "mean       1.009190        1.006338             1.341745        1.341745  \n",
       "std        0.095426        0.079361             0.474304        0.474304  \n",
       "min        1.000000        1.000000             1.000000        1.000000  \n",
       "25%        1.000000        1.000000             1.000000        1.000000  \n",
       "50%        1.000000        1.000000             1.000000        1.000000  \n",
       "75%        1.000000        1.000000             2.000000        2.000000  \n",
       "max        2.000000        2.000000             2.000000        2.000000  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# DESCRIPTIVE STATISTICS                                          #\n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",'Descriptive statistics, summarizing central tendency, dispersion')\n",
    "print('  and shape of dataset\\'s distribution')\n",
    "meta.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#=================================================================#\n",
    "# PROCESS DATA: Categorical conversions, OHE, features            #\n",
    "#=================================================================#\n",
    "# Cities and States will get categorical codes\n",
    "meta['city'] = meta['city'].str.upper() # all upper case\n",
    "cities = sorted(meta['city'].unique())  # sort by unique names\n",
    "meta['city_int'] = meta['city'].map(lambda x: cities.index(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 It is interesting that in average, most NPOs have almost zero profitability (Income minus Expense)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Note that most NPOs' income comes from Program Services (23%) followed closely by income from Other sources (Gifts, Donations,etc) at 21%.  Government Grants only account for 8.8% of the total revenue\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# VISUALIZATION                                                   #\n",
    "#=================================================================#\n",
    "# Visualizations of the sources of income for 501(c)(3) NPOs, \n",
    "# based on fundraising, campaign, membership, government grants, \n",
    "# gifts, assets and service revenues. This can provide insights into the \n",
    "# income nature of the NPOs. \n",
    "print(u\"\\u0011\",\"It is interesting that in average, most NPOs have almost zero profitability (Income minus Expense)\")\n",
    "fig = plt.figure()\n",
    "rev_df = meta[['total_revenue', 'total_expenses', 'net_assets']].copy()\n",
    "ax = sns.barplot(data=rev_df)\n",
    "ax.set(xlabel='Revenue', ylabel='Dollars (US$)')\n",
    "ax.set_xticklabels(['Total Revenue','Total Expenses','Net Assets'], rotation=30)\n",
    "ax.set_title('Total Revenue, Expenses and Net Assets', fontsize=16)\n",
    "plt.show()\n",
    "\n",
    "del rev_df\n",
    "# Revenue Plot, normalized by total revenue\n",
    "print(u\"\\u0011\",\"Note that most NPOs' income comes from Program Services (23%)\", \n",
    "      \"followed closely by income from Other sources (Gifts, Donations,etc) at 21%. \",\n",
    "      \"Government Grants only account for 8.8% of the total revenue\")\n",
    "fig = plt.figure()\n",
    "rev_df = meta[['rev_campaigns','rev_membership','rev_fundraising','rev_netfundraising',\n",
    "               'rev_govgrants','rev_progserv','rev_other']].copy()\n",
    "\n",
    "rev_df=(rev_df.div(meta['total_revenue'], axis=0)).fillna(0)\n",
    "ax = sns.barplot(data=rev_df)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"%.2f\" % (p.get_height()*100),\n",
    "                (p.get_x() + p.get_width() / 2., .02), \n",
    "                fontsize=16,ha='center', va='bottom')\n",
    "ax.set_xlabel('Revenue', fontsize=14)\n",
    "ax.set_xticklabels(['Campaigns','Membership','Fundraising','Other Fundraising',\n",
    "                    'Government Grants', 'Program Services','Other'], rotation=30, fontsize=14)\n",
    "ax.set_title('Percentage of Total Revenue for each source', fontsize=16)\n",
    "ax.set_ylabel('Dollars (US$)',fontsize=14)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Here, we see that, while Grants and Fundraising constitute only 8% of the expenses, Management/Functional and Compensations costs account, in average, for 22% of expenses.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations of the expenses, based on functional, service, \n",
    "# management, and fundraising expenses. \n",
    "# Normalized by total expenses\n",
    "print(u\"\\u0011\",\"Here, we see that, while Grants and Fundraising constitute only 8% of the expenses,\",\n",
    "      \"Management/Functional and Compensations costs account, in average, for 22% of expenses.\")\n",
    "fig = plt.figure();\n",
    "exp_df = meta[['exp_grants','exp_progserv','exp_management',\n",
    "               'exp_fundraising', 'total_compensations']].copy()\n",
    "exp_df=(exp_df.div(meta['total_expenses'], axis=0)).fillna(0)\n",
    "\n",
    "ax = sns.barplot(data=exp_df)\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"%.2f\" % (p.get_height()*100),\n",
    "                (p.get_x() + p.get_width() / 2., .02), \n",
    "                fontsize=16,ha='center', va='bottom')\n",
    "ax.set_xlabel('Expenses', fontsize=14)\n",
    "ax.set_xticklabels(['Grants','Program Services','Management/Functional','Fundraising', 'Compensations'], rotation=20, fontsize=14)\n",
    "ax.set_title('Percentage of Total Expenses for each source', fontsize=16)\n",
    "ax.set_ylabel('Dollars (US$)',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# VISUALIZATION                                                   #\n",
    "#=================================================================#\n",
    "## Create array for K-means\n",
    "# Standarize (z-score) array (zi = xi-xmean/std)\n",
    "meta_ = (meta[['net_assets', 'total_revenue']].copy()).apply(stats.zscore)\n",
    "meta_zscored = meta_[(np.abs(stats.zscore(meta_)) < 3).all(axis=1)]\n",
    "\n",
    "## Visualizations\n",
    "plt.scatter(meta_['net_assets'], meta_['total_revenue'], s=80);\n",
    "plt.title('Scatter plot of Employee Count vs Total Revenue', fontsize=16)\n",
    "plt.xlabel('Net Assets (US$)', fontsize=14); plt.ylabel('Total Revenue (US$)', fontsize=14); \n",
    "plt.show()\n",
    "\n",
    "plt.scatter(meta_zscored['net_assets'], meta_zscored['total_revenue'], s=80);\n",
    "plt.title('[Z-score Normalized] Scatter plot of Employee Count vs Total Revenue', fontsize=16)\n",
    "plt.xlabel('Net Assets (US$)', fontsize=14); plt.ylabel('Total Revenue (US$)', fontsize=14); \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# 3D VISUALIZATION                                                #\n",
    "#=================================================================#\n",
    "# Add columns City_int and State_int to processed data\n",
    "meta_ = (meta[['total_compensations', 'total_revenue', 'total_expenses']].copy()).apply(stats.zscore)\n",
    "meta_zscored = meta_[(np.abs(stats.zscore(meta_)) < 2).all(axis=1)]\n",
    "\n",
    "# 3D Plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(meta_zscored['total_compensations'],meta_zscored['total_revenue'],meta_zscored['total_expenses'], s=80)\n",
    "ax.set_xlabel('Total Compensation (z-scored)', fontsize=14)\n",
    "ax.set_ylabel('Total Revenue (zscore)', fontsize=14)\n",
    "ax.set_zlabel('Total Expenses (zscore)', fontsize=14)\n",
    "ax.set_title('Revenue, Compensation and Expenses', fontsize=16)\n",
    "ax.view_init(elev=5., azim=60)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Ideally, we would use a more stringent criterion determination method, such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC)\n",
      ".\n",
      "\u0011 For k=4:           total_compensations  total_revenue  total_expenses\n",
      "Clusters                                                    \n",
      "0                   -0.142374      -0.072098       -0.073396\n",
      "1                    1.227261       0.164052        0.171487\n",
      "2                    0.289131       0.032820        0.031737\n",
      "3                    1.096792       1.097198        1.059560\n"
     ]
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# K-Means Clustering                                              #\n",
    "#=================================================================#\n",
    "# Elbow method to determine K\n",
    "distortions = []\n",
    "K = range(1,20)\n",
    "for k in K:\n",
    "    kmeanModel = KMeans(n_clusters=k).fit(meta_zscored)\n",
    "    kmeanModel.fit(meta_zscored)\n",
    "    distortions.append(sum(np.min(cdist(meta_zscored, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / meta_zscored.shape[0])\n",
    "plt.plot(K, distortions, 'bx-')\n",
    "plt.xlabel('k', fontsize=14)\n",
    "plt.ylabel('Distortion', fontsize=14)\n",
    "plt.title('Elbow Method for Optimal k', fontsize=20)\n",
    "plt.show()\n",
    " \n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",\"Ideally, we would use a more stringent criterion determination method, such as\",\n",
    "      \"the Akaike information criterion (AIC) or Bayesian information criterion (BIC)\\n.\")\n",
    "# KMeans \n",
    "kmeans = KMeans(n_clusters=4, random_state=0).fit(meta_zscored)\n",
    "labels = kmeans.labels_\n",
    "## Add labels to original data\n",
    "meta_zscored = meta_zscored.assign(Clusters = labels)\n",
    "columns = (meta_zscored.columns.get_values()).tolist()\n",
    "print(u\"\\u0011\",\"For k=4:\",meta_zscored[columns].groupby(['Clusters']).mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# VISUALIZATION K-Means Clustering                                #\n",
    "#=================================================================#\n",
    "# Generate cluster groupings\n",
    "cluster1=meta_zscored.loc[meta_zscored['Clusters'] == 0]\n",
    "cluster2=meta_zscored.loc[meta_zscored['Clusters'] == 1]\n",
    "cluster3=meta_zscored.loc[meta_zscored['Clusters'] == 2]\n",
    "cluster4=meta_zscored.loc[meta_zscored['Clusters'] == 3]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(cluster1['total_compensations'],cluster1['total_revenue'],cluster1['total_expenses'], \n",
    "           c='blue', s=40, cmap=\"RdBu\")\n",
    "ax.scatter(cluster2['total_compensations'],cluster2['total_revenue'],cluster2['total_expenses'], \n",
    "           c='red', s=40, cmap=\"RdBu\")\n",
    "ax.scatter(cluster3['total_compensations'],cluster3['total_revenue'],cluster3['total_expenses'], \n",
    "           c='green', s=40, cmap=\"RdBu\")\n",
    "ax.scatter(cluster4['total_compensations'],cluster4['total_revenue'],cluster4['total_expenses'], \n",
    "           c='yellow', s=40, cmap=\"RdBu\")\n",
    "ax.set_xlabel('Total Compensation (z-scored)', fontsize=14)\n",
    "ax.set_ylabel('Total Revenue (zscore)', fontsize=14)\n",
    "ax.set_zlabel('Total Expenses (zscore)', fontsize=14)\n",
    "ax.set_title('KMeans Clustering for Revenue, Compensation and Expenses', fontsize=16)\n",
    "ax.view_init(elev=5., azim=60)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Retrieve original data and clean NaNs due to zscore outlier removal\n",
    "meta = meta.assign(Clusters = meta_zscored['Clusters'].loc[meta_zscored.index.get_values()])\n",
    "meta = meta.dropna(axis=0,how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Cluster 1 contains  22960 companies with an average of 9.84 Employees and Average Compensation (Total) of US$19,137.95, with a mean of 0.049 employees receiving salaries above U$100k. \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
       "      <th>employee_ct</th>\n",
       "      <th>rev_campaigns</th>\n",
       "      <th>rev_membership</th>\n",
       "      <th>rev_fundraising</th>\n",
       "      <th>rev_govgrants</th>\n",
       "      <th>rev_other</th>\n",
       "      <th>...</th>\n",
       "      <th>total_compensations</th>\n",
       "      <th>comp_more100k</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>pol_act</th>\n",
       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
       "      <th>city_int</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>22960.0</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>2.296000e+04</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.000000</td>\n",
       "      <td>22960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.981533</td>\n",
       "      <td>1148.911237</td>\n",
       "      <td>83.683972</td>\n",
       "      <td>9.842030</td>\n",
       "      <td>5.527306e+03</td>\n",
       "      <td>7.269235e+03</td>\n",
       "      <td>1.674521e+04</td>\n",
       "      <td>8.791334e+04</td>\n",
       "      <td>1.936645e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>19137.953876</td>\n",
       "      <td>0.049260</td>\n",
       "      <td>1.206294e+06</td>\n",
       "      <td>1.003528</td>\n",
       "      <td>1.010105</td>\n",
       "      <td>1.006969</td>\n",
       "      <td>1.375740</td>\n",
       "      <td>1.375740</td>\n",
       "      <td>2535.775174</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.134635</td>\n",
       "      <td>982.559518</td>\n",
       "      <td>1172.347520</td>\n",
       "      <td>38.282475</td>\n",
       "      <td>1.050532e+05</td>\n",
       "      <td>5.796567e+04</td>\n",
       "      <td>1.488328e+05</td>\n",
       "      <td>5.637834e+05</td>\n",
       "      <td>8.336638e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>38767.347103</td>\n",
       "      <td>0.306391</td>\n",
       "      <td>7.863654e+06</td>\n",
       "      <td>0.059292</td>\n",
       "      <td>0.100014</td>\n",
       "      <td>0.083189</td>\n",
       "      <td>0.484324</td>\n",
       "      <td>0.484324</td>\n",
       "      <td>1495.537796</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-8.310000e+02</td>\n",
       "      <td>-2.623100e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.856000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.367646e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.375450e+04</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1136.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.201860e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2564.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.270000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.112815e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.060080e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3867.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>132183.000000</td>\n",
       "      <td>966.000000</td>\n",
       "      <td>6.705084e+06</td>\n",
       "      <td>2.559245e+06</td>\n",
       "      <td>1.574590e+07</td>\n",
       "      <td>2.621824e+07</td>\n",
       "      <td>4.789726e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>194962.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.236827e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5142.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status      tax_year   year_formed   volunteer_ct   employee_ct  \\\n",
       "count     22960.0  22960.000000  22960.000000   22960.000000  22960.000000   \n",
       "mean          0.0   2013.981533   1148.911237      83.683972      9.842030   \n",
       "std           0.0      0.134635    982.559518    1172.347520     38.282475   \n",
       "min           0.0   2013.000000      0.000000       0.000000      0.000000   \n",
       "25%           0.0   2014.000000      0.000000       0.000000      0.000000   \n",
       "50%           0.0   2014.000000   1969.000000       0.000000      0.000000   \n",
       "75%           0.0   2014.000000   1998.000000      15.000000      4.000000   \n",
       "max           0.0   2014.000000   2015.000000  132183.000000    966.000000   \n",
       "\n",
       "       rev_campaigns  rev_membership  rev_fundraising  rev_govgrants  \\\n",
       "count   2.296000e+04    2.296000e+04     2.296000e+04   2.296000e+04   \n",
       "mean    5.527306e+03    7.269235e+03     1.674521e+04   8.791334e+04   \n",
       "std     1.050532e+05    5.796567e+04     1.488328e+05   5.637834e+05   \n",
       "min     0.000000e+00   -8.310000e+02    -2.623100e+04   0.000000e+00   \n",
       "25%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "50%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "75%     0.000000e+00    0.000000e+00     6.270000e+02   0.000000e+00   \n",
       "max     6.705084e+06    2.559245e+06     1.574590e+07   2.621824e+07   \n",
       "\n",
       "          rev_other    ...     total_compensations  comp_more100k  \\\n",
       "count  2.296000e+04    ...            22960.000000   22960.000000   \n",
       "mean   1.936645e+05    ...            19137.953876       0.049260   \n",
       "std    8.336638e+05    ...            38767.347103       0.306391   \n",
       "min   -1.856000e+03    ...               -1.000000       0.000000   \n",
       "25%    0.000000e+00    ...                0.000000       0.000000   \n",
       "50%    0.000000e+00    ...                0.000000       0.000000   \n",
       "75%    1.112815e+05    ...            10000.000000       0.000000   \n",
       "max    4.789726e+07    ...           194962.000000      21.000000   \n",
       "\n",
       "         net_assets       pol_act       lob_act  foreign_office  \\\n",
       "count  2.296000e+04  22960.000000  22960.000000    22960.000000   \n",
       "mean   1.206294e+06      1.003528      1.010105        1.006969   \n",
       "std    7.863654e+06      0.059292      0.100014        0.083189   \n",
       "min   -9.367646e+07      1.000000      1.000000        1.000000   \n",
       "25%    2.375450e+04      1.000000      1.000000        1.000000   \n",
       "50%    1.201860e+05      1.000000      1.000000        1.000000   \n",
       "75%    6.060080e+05      1.000000      1.000000        1.000000   \n",
       "max    7.236827e+08      2.000000      2.000000        2.000000   \n",
       "\n",
       "       foreign_fundraising  foreign_assist      city_int  Clusters  \n",
       "count         22960.000000    22960.000000  22960.000000   22960.0  \n",
       "mean              1.375740        1.375740   2535.775174       0.0  \n",
       "std               0.484324        0.484324   1495.537796       0.0  \n",
       "min               1.000000        1.000000      0.000000       0.0  \n",
       "25%               1.000000        1.000000   1136.000000       0.0  \n",
       "50%               1.000000        1.000000   2564.500000       0.0  \n",
       "75%               2.000000        2.000000   3867.000000       0.0  \n",
       "max               2.000000        2.000000   5142.000000       0.0  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# CLUSTER 1 ANALYSIS                                              #\n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",\"Cluster 1 contains \",len(cluster1), \"companies\", \"with an average of 9.84 Employees and Average Compensation (Total) of US$19,137.95, with a mean of 0.049 employees receiving salaries above U$100k. \")\n",
    "sns.jointplot(x=\"comp_more100k\", y=\"employee_ct\", data=meta.loc[meta['Clusters'] == 0]); \n",
    "plt.title('Cluster 1', fontsize=20); plt.show()\n",
    "(meta.loc[meta['Clusters'] == 0]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Cluster 2 contains 418 companies with an average of 246 Employees and Average Compensation (Total) of US$1,023,683.00, with a mean of 7.96 employees receiving salaries above U$100k. \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
       "      <th>employee_ct</th>\n",
       "      <th>rev_campaigns</th>\n",
       "      <th>rev_membership</th>\n",
       "      <th>rev_fundraising</th>\n",
       "      <th>rev_govgrants</th>\n",
       "      <th>rev_other</th>\n",
       "      <th>...</th>\n",
       "      <th>total_compensations</th>\n",
       "      <th>comp_more100k</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>pol_act</th>\n",
       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
       "      <th>city_int</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>4.180000e+02</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.0</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.973684</td>\n",
       "      <td>1969.983254</td>\n",
       "      <td>718.490431</td>\n",
       "      <td>246.612440</td>\n",
       "      <td>1.525686e+04</td>\n",
       "      <td>1.317189e+05</td>\n",
       "      <td>2.055975e+05</td>\n",
       "      <td>1.440420e+06</td>\n",
       "      <td>2.717314e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.023683e+06</td>\n",
       "      <td>7.964115</td>\n",
       "      <td>1.939036e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2673.593301</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.160265</td>\n",
       "      <td>101.174812</td>\n",
       "      <td>5800.562456</td>\n",
       "      <td>295.751999</td>\n",
       "      <td>1.068849e+05</td>\n",
       "      <td>7.371264e+05</td>\n",
       "      <td>6.932482e+05</td>\n",
       "      <td>3.192756e+06</td>\n",
       "      <td>5.816264e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.535961e+05</td>\n",
       "      <td>6.886274</td>\n",
       "      <td>4.427482e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1496.085608</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>6.299090e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.836858e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1964.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>7.693000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>8.078035e+05</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.353810e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1230.500000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1982.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.497000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3.770885e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>9.733300e+05</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.808044e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2911.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1996.000000</td>\n",
       "      <td>136.000000</td>\n",
       "      <td>337.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.255680e+05</td>\n",
       "      <td>1.074418e+06</td>\n",
       "      <td>2.955950e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.202543e+06</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.864217e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3998.750000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>108383.000000</td>\n",
       "      <td>2152.000000</td>\n",
       "      <td>1.483793e+06</td>\n",
       "      <td>8.436094e+06</td>\n",
       "      <td>9.011134e+06</td>\n",
       "      <td>2.230098e+07</td>\n",
       "      <td>5.123962e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.588716e+06</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>4.182555e+08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5116.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status     tax_year  year_formed   volunteer_ct  employee_ct  \\\n",
       "count       418.0   418.000000   418.000000     418.000000   418.000000   \n",
       "mean          0.0  2013.973684  1969.983254     718.490431   246.612440   \n",
       "std           0.0     0.160265   101.174812    5800.562456   295.751999   \n",
       "min           0.0  2013.000000     0.000000       0.000000     0.000000   \n",
       "25%           0.0  2014.000000  1964.000000       0.000000    47.000000   \n",
       "50%           0.0  2014.000000  1982.000000      17.000000   145.000000   \n",
       "75%           0.0  2014.000000  1996.000000     136.000000   337.500000   \n",
       "max           0.0  2014.000000  2012.000000  108383.000000  2152.000000   \n",
       "\n",
       "       rev_campaigns  rev_membership  rev_fundraising  rev_govgrants  \\\n",
       "count   4.180000e+02    4.180000e+02     4.180000e+02   4.180000e+02   \n",
       "mean    1.525686e+04    1.317189e+05     2.055975e+05   1.440420e+06   \n",
       "std     1.068849e+05    7.371264e+05     6.932482e+05   3.192756e+06   \n",
       "min     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "25%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "50%     0.000000e+00    0.000000e+00     4.497000e+03   0.000000e+00   \n",
       "75%     0.000000e+00    0.000000e+00     1.255680e+05   1.074418e+06   \n",
       "max     1.483793e+06    8.436094e+06     9.011134e+06   2.230098e+07   \n",
       "\n",
       "          rev_other    ...     total_compensations  comp_more100k  \\\n",
       "count  4.180000e+02    ...            4.180000e+02     418.000000   \n",
       "mean   2.717314e+06    ...            1.023683e+06       7.964115   \n",
       "std    5.816264e+06    ...            2.535961e+05       6.886274   \n",
       "min    0.000000e+00    ...            6.299090e+05       0.000000   \n",
       "25%    7.693000e+03    ...            8.078035e+05       5.000000   \n",
       "50%    3.770885e+05    ...            9.733300e+05       6.000000   \n",
       "75%    2.955950e+06    ...            1.202543e+06       8.000000   \n",
       "max    5.123962e+07    ...            1.588716e+06      94.000000   \n",
       "\n",
       "         net_assets  pol_act  lob_act  foreign_office  foreign_fundraising  \\\n",
       "count  4.180000e+02    418.0    418.0           418.0                418.0   \n",
       "mean   1.939036e+07      1.0      1.0             1.0                  1.0   \n",
       "std    4.427482e+07      0.0      0.0             0.0                  0.0   \n",
       "min   -9.836858e+07      1.0      1.0             1.0                  1.0   \n",
       "25%    2.353810e+06      1.0      1.0             1.0                  1.0   \n",
       "50%    7.808044e+06      1.0      1.0             1.0                  1.0   \n",
       "75%    1.864217e+07      1.0      1.0             1.0                  1.0   \n",
       "max    4.182555e+08      1.0      1.0             1.0                  1.0   \n",
       "\n",
       "       foreign_assist     city_int  Clusters  \n",
       "count           418.0   418.000000     418.0  \n",
       "mean              1.0  2673.593301       1.0  \n",
       "std               0.0  1496.085608       0.0  \n",
       "min               1.0     2.000000       1.0  \n",
       "25%               1.0  1230.500000       1.0  \n",
       "50%               1.0  2911.000000       1.0  \n",
       "75%               1.0  3998.750000       1.0  \n",
       "max               1.0  5116.000000       1.0  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# CLUSTER 2 ANALYSIS                                              #\n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",\"Cluster 2 contains\",len(cluster2), \"companies\", \"with an average of 246 Employees and Average Compensation (Total) of US$1,023,683.00, with a mean of 7.96 employees receiving salaries above U$100k. \")\n",
    "sns.jointplot(x=\"comp_more100k\", y=\"employee_ct\", data=meta.loc[meta['Clusters'] == 1]); \n",
    "plt.title('Cluster 2', fontsize=20); plt.show()\n",
    "(meta.loc[meta['Clusters'] == 1]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Cluster 3 contains  1357 companies with an average of 140 Employees and Average Compensation (Total) of US$335,620.96, with a mean of 1.86 employees receiving salaries above U$100k. \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
       "      <th>employee_ct</th>\n",
       "      <th>rev_campaigns</th>\n",
       "      <th>rev_membership</th>\n",
       "      <th>rev_fundraising</th>\n",
       "      <th>rev_govgrants</th>\n",
       "      <th>rev_other</th>\n",
       "      <th>...</th>\n",
       "      <th>total_compensations</th>\n",
       "      <th>comp_more100k</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>pol_act</th>\n",
       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
       "      <th>city_int</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1357.00000</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>1357.000000</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.977155</td>\n",
       "      <td>1937.671334</td>\n",
       "      <td>622.579956</td>\n",
       "      <td>140.24171</td>\n",
       "      <td>4.450973e+04</td>\n",
       "      <td>8.987204e+04</td>\n",
       "      <td>1.384135e+05</td>\n",
       "      <td>1.005672e+06</td>\n",
       "      <td>2.076691e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>335620.968312</td>\n",
       "      <td>1.864407</td>\n",
       "      <td>1.160407e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2584.599116</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.149463</td>\n",
       "      <td>277.662639</td>\n",
       "      <td>3540.320084</td>\n",
       "      <td>232.07243</td>\n",
       "      <td>4.096323e+05</td>\n",
       "      <td>1.552164e+06</td>\n",
       "      <td>6.844131e+05</td>\n",
       "      <td>2.791158e+06</td>\n",
       "      <td>5.343337e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>146062.701403</td>\n",
       "      <td>1.353203</td>\n",
       "      <td>3.110252e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1527.378489</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-2.108000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.920838e+08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.340500e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>217863.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.079231e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1983.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>52.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.643100e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.834470e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>291763.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.448855e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2813.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>163.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.094500e+04</td>\n",
       "      <td>4.890000e+05</td>\n",
       "      <td>1.768125e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>437644.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.012193e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3953.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>85000.000000</td>\n",
       "      <td>2141.00000</td>\n",
       "      <td>1.203888e+07</td>\n",
       "      <td>5.585967e+07</td>\n",
       "      <td>1.845267e+07</td>\n",
       "      <td>2.880526e+07</td>\n",
       "      <td>7.046836e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>707697.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>4.012541e+08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5132.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status     tax_year  year_formed  volunteer_ct  employee_ct  \\\n",
       "count      1357.0  1357.000000  1357.000000   1357.000000   1357.00000   \n",
       "mean          0.0  2013.977155  1937.671334    622.579956    140.24171   \n",
       "std           0.0     0.149463   277.662639   3540.320084    232.07243   \n",
       "min           0.0  2013.000000     0.000000      0.000000      0.00000   \n",
       "25%           0.0  2014.000000  1965.000000      0.000000     15.00000   \n",
       "50%           0.0  2014.000000  1983.000000     25.000000     52.00000   \n",
       "75%           0.0  2014.000000  1997.000000    200.000000    163.00000   \n",
       "max           0.0  2014.000000  2014.000000  85000.000000   2141.00000   \n",
       "\n",
       "       rev_campaigns  rev_membership  rev_fundraising  rev_govgrants  \\\n",
       "count   1.357000e+03    1.357000e+03     1.357000e+03   1.357000e+03   \n",
       "mean    4.450973e+04    8.987204e+04     1.384135e+05   1.005672e+06   \n",
       "std     4.096323e+05    1.552164e+06     6.844131e+05   2.791158e+06   \n",
       "min     0.000000e+00    0.000000e+00    -2.108000e+03   0.000000e+00   \n",
       "25%     0.000000e+00    0.000000e+00     0.000000e+00   0.000000e+00   \n",
       "50%     0.000000e+00    0.000000e+00     1.643100e+04   0.000000e+00   \n",
       "75%     0.000000e+00    0.000000e+00     8.094500e+04   4.890000e+05   \n",
       "max     1.203888e+07    5.585967e+07     1.845267e+07   2.880526e+07   \n",
       "\n",
       "          rev_other    ...     total_compensations  comp_more100k  \\\n",
       "count  1.357000e+03    ...             1357.000000    1357.000000   \n",
       "mean   2.076691e+06    ...           335620.968312       1.864407   \n",
       "std    5.343337e+06    ...           146062.701403       1.353203   \n",
       "min    0.000000e+00    ...                0.000000       0.000000   \n",
       "25%    2.340500e+04    ...           217863.000000       1.000000   \n",
       "50%    4.834470e+05    ...           291763.000000       2.000000   \n",
       "75%    1.768125e+06    ...           437644.000000       2.000000   \n",
       "max    7.046836e+07    ...           707697.000000      17.000000   \n",
       "\n",
       "         net_assets  pol_act  lob_act  foreign_office  foreign_fundraising  \\\n",
       "count  1.357000e+03   1357.0   1357.0          1357.0               1357.0   \n",
       "mean   1.160407e+07      1.0      1.0             1.0                  1.0   \n",
       "std    3.110252e+07      0.0      0.0             0.0                  0.0   \n",
       "min   -1.920838e+08      1.0      1.0             1.0                  1.0   \n",
       "25%    1.079231e+06      1.0      1.0             1.0                  1.0   \n",
       "50%    3.448855e+06      1.0      1.0             1.0                  1.0   \n",
       "75%    1.012193e+07      1.0      1.0             1.0                  1.0   \n",
       "max    4.012541e+08      1.0      1.0             1.0                  1.0   \n",
       "\n",
       "       foreign_assist     city_int  Clusters  \n",
       "count          1357.0  1357.000000    1357.0  \n",
       "mean              1.0  2584.599116       2.0  \n",
       "std               0.0  1527.378489       0.0  \n",
       "min               1.0     2.000000       2.0  \n",
       "25%               1.0  1095.000000       2.0  \n",
       "50%               1.0  2813.000000       2.0  \n",
       "75%               1.0  3953.000000       2.0  \n",
       "max               1.0  5132.000000       2.0  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# CLUSTER 3 ANALYSIS                                              #\n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",\"Cluster 3 contains \",len(cluster3), \"companies\", \"with an average of 140 Employees and Average Compensation (Total) of US$335,620.96, with a mean of 1.86 employees receiving salaries above U$100k. \")\n",
    "sns.jointplot(x=\"comp_more100k\", y=\"employee_ct\", data=meta.loc[meta['Clusters'] == 2]); \n",
    "plt.title('Cluster 3', fontsize=20); plt.show()\n",
    "(meta.loc[meta['Clusters'] == 2]).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u0011 Similar to Cluster 3, Cluster 4 contains  71 companies with an average of 647 Employees and Average Compensation (Total) of US$927,991.6e+05, with a mean of 14.4 employees receiving salaries above U$100k. \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tax_status</th>\n",
       "      <th>tax_year</th>\n",
       "      <th>year_formed</th>\n",
       "      <th>volunteer_ct</th>\n",
       "      <th>employee_ct</th>\n",
       "      <th>rev_campaigns</th>\n",
       "      <th>rev_membership</th>\n",
       "      <th>rev_fundraising</th>\n",
       "      <th>rev_govgrants</th>\n",
       "      <th>rev_other</th>\n",
       "      <th>...</th>\n",
       "      <th>total_compensations</th>\n",
       "      <th>comp_more100k</th>\n",
       "      <th>net_assets</th>\n",
       "      <th>pol_act</th>\n",
       "      <th>lob_act</th>\n",
       "      <th>foreign_office</th>\n",
       "      <th>foreign_fundraising</th>\n",
       "      <th>foreign_assist</th>\n",
       "      <th>city_int</th>\n",
       "      <th>Clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>71.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.971831</td>\n",
       "      <td>1964.281690</td>\n",
       "      <td>1814.338028</td>\n",
       "      <td>647.323944</td>\n",
       "      <td>3.152311e+04</td>\n",
       "      <td>3113.084507</td>\n",
       "      <td>1.846522e+05</td>\n",
       "      <td>6.518481e+06</td>\n",
       "      <td>1.862334e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>9.279916e+05</td>\n",
       "      <td>14.408451</td>\n",
       "      <td>1.057232e+08</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2696.676056</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.166633</td>\n",
       "      <td>37.291586</td>\n",
       "      <td>5377.264302</td>\n",
       "      <td>653.493115</td>\n",
       "      <td>2.057031e+05</td>\n",
       "      <td>19576.843685</td>\n",
       "      <td>5.000800e+05</td>\n",
       "      <td>1.895485e+07</td>\n",
       "      <td>3.488605e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>4.782064e+05</td>\n",
       "      <td>19.266979</td>\n",
       "      <td>2.396360e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1406.318809</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>1828.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4.739637e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1943.500000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.530000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>6.689875e+05</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7.393938e+06</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1876.500000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1972.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>436.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.524770e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>9.953440e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.969605e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2825.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>1992.500000</td>\n",
       "      <td>207.000000</td>\n",
       "      <td>1145.500000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.346700e+05</td>\n",
       "      <td>9.213875e+05</td>\n",
       "      <td>9.464749e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.323695e+06</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>9.688065e+07</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3738.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>28000.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>1.717833e+06</td>\n",
       "      <td>154000.000000</td>\n",
       "      <td>3.341028e+06</td>\n",
       "      <td>1.007522e+08</td>\n",
       "      <td>1.489116e+08</td>\n",
       "      <td>...</td>\n",
       "      <td>1.588105e+06</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>1.621377e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5085.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       tax_status     tax_year  year_formed  volunteer_ct  employee_ct  \\\n",
       "count        71.0    71.000000    71.000000     71.000000    71.000000   \n",
       "mean          0.0  2013.971831  1964.281690   1814.338028   647.323944   \n",
       "std           0.0     0.166633    37.291586   5377.264302   653.493115   \n",
       "min           0.0  2013.000000  1828.000000      0.000000     0.000000   \n",
       "25%           0.0  2014.000000  1943.500000     27.000000    67.500000   \n",
       "50%           0.0  2014.000000  1972.000000     94.000000   436.000000   \n",
       "75%           0.0  2014.000000  1992.500000    207.000000  1145.500000   \n",
       "max           0.0  2014.000000  2014.000000  28000.000000  3148.000000   \n",
       "\n",
       "       rev_campaigns  rev_membership  rev_fundraising  rev_govgrants  \\\n",
       "count   7.100000e+01       71.000000     7.100000e+01   7.100000e+01   \n",
       "mean    3.152311e+04     3113.084507     1.846522e+05   6.518481e+06   \n",
       "std     2.057031e+05    19576.843685     5.000800e+05   1.895485e+07   \n",
       "min     0.000000e+00        0.000000     0.000000e+00   0.000000e+00   \n",
       "25%     0.000000e+00        0.000000     0.000000e+00   0.000000e+00   \n",
       "50%     0.000000e+00        0.000000     0.000000e+00   0.000000e+00   \n",
       "75%     0.000000e+00        0.000000     1.346700e+05   9.213875e+05   \n",
       "max     1.717833e+06   154000.000000     3.341028e+06   1.007522e+08   \n",
       "\n",
       "          rev_other    ...     total_compensations  comp_more100k  \\\n",
       "count  7.100000e+01    ...            7.100000e+01      71.000000   \n",
       "mean   1.862334e+07    ...            9.279916e+05      14.408451   \n",
       "std    3.488605e+07    ...            4.782064e+05      19.266979   \n",
       "min    0.000000e+00    ...            0.000000e+00       0.000000   \n",
       "25%    4.530000e+02    ...            6.689875e+05       4.500000   \n",
       "50%    2.524770e+05    ...            9.953440e+05       8.000000   \n",
       "75%    9.464749e+06    ...            1.323695e+06      14.500000   \n",
       "max    1.489116e+08    ...            1.588105e+06     112.000000   \n",
       "\n",
       "         net_assets  pol_act  lob_act  foreign_office  foreign_fundraising  \\\n",
       "count  7.100000e+01     71.0     71.0            71.0                 71.0   \n",
       "mean   1.057232e+08      1.0      1.0             1.0                  1.0   \n",
       "std    2.396360e+08      0.0      0.0             0.0                  0.0   \n",
       "min   -4.739637e+07      1.0      1.0             1.0                  1.0   \n",
       "25%    7.393938e+06      1.0      1.0             1.0                  1.0   \n",
       "50%    3.969605e+07      1.0      1.0             1.0                  1.0   \n",
       "75%    9.688065e+07      1.0      1.0             1.0                  1.0   \n",
       "max    1.621377e+09      1.0      1.0             1.0                  1.0   \n",
       "\n",
       "       foreign_assist     city_int  Clusters  \n",
       "count            71.0    71.000000      71.0  \n",
       "mean              1.0  2696.676056       3.0  \n",
       "std               0.0  1406.318809       0.0  \n",
       "min               1.0    46.000000       3.0  \n",
       "25%               1.0  1876.500000       3.0  \n",
       "50%               1.0  2825.000000       3.0  \n",
       "75%               1.0  3738.000000       3.0  \n",
       "max               1.0  5085.000000       3.0  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#=================================================================#\n",
    "# CLUSTER 4 ANALYSIS                                              #\n",
    "#=================================================================#\n",
    "print(u\"\\u0011\",\"Similar to Cluster 3, Cluster 4 contains \",len(cluster4), \"companies\", \"with an average of 647 Employees and Average Compensation (Total) of US$927,991.6e+05, with a mean of 14.4 employees receiving salaries above U$100k. \")\n",
    "sns.jointplot(x=\"comp_more100k\", y=\"employee_ct\", data=meta.loc[meta['Clusters'] == 3]); \n",
    "plt.title('Cluster 4', fontsize=20); plt.show()\n",
    "(meta.loc[meta['Clusters'] == 3]).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results from Clustering\n",
    "\n",
    "My analysis identified 4 clusters of companies in database. As shown above, Clusters 1 and 3 contain companies that are relatively small (9.84 and 140 employees in average, respectively), but counted with only, in average 0.049 and 1.86 of its employees receiving salaries over U\\$100,000, respectively. \n",
    "\n",
    "On the other hand, Clusters 2 and 4 show NPOs with a comparatively larger number of employees (246 and 647 in average, respectively), however, its average number of employees receiving remuneration of U$100,000 and higher exceeds Clusters 1 and 3 117-fold (0.095 versus 11.18 average for Clusters 1,3 and Clusters 2,4, respectively). The relationship between the Clusters and their Compensations and Revenues can be seen in Result Figures 1 and 2, below.\n",
    "\n",
    "In Result Figure 2 and 3, I note that although NPOs in Cluster 4 are able to attract a larger amount of income, its number of volunteers varies greatly within the dataset, and is not much different from Clusters 2 and 3.\n",
    "\n",
    "In addition, the three graphs below, show that revenues and expenses are also tied together for these clusters, and that the NPO clusters with highers revenues/expenses also count with the largest number of volunteers. \n",
    "Taken together, this data can help individuals and organizations best analyze the financial resources and its uses by the NPOs analyzed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations of the expenses, based on functional, service, \n",
    "# management, and fundraising expenses. \n",
    "# Normalized by total expenses\n",
    "fig = plt.figure();\n",
    "exp_df = meta[['total_compensations','Clusters']].copy()\n",
    "exp_DF = exp_df.groupby(['Clusters'])\n",
    "ax = sns.barplot(data=exp_df, x=\"Clusters\", y=\"total_compensations\", order=[0,1,2,3])\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"~U$%.0f\" % (p.get_height()),\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()*1.08), \n",
    "                fontsize=16,ha='center', va='bottom')\n",
    "ax.set_xlabel('Clusters', fontsize=14)\n",
    "ax.set_xticklabels(['Cluster 1','Cluster 2', 'Cluster 3 ','Cluster 4'], rotation=20, fontsize=14)\n",
    "ax.set_title('[Result Figure 1] Total Compensations for each cluster', fontsize=16)\n",
    "ax.set_ylabel('Dollars (US$)',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations of the expenses, based on functional, service, \n",
    "# management, and fundraising expenses. \n",
    "# Normalized by total expenses\n",
    "fig = plt.figure();\n",
    "exp_df = meta[['total_revenue','Clusters']].copy()\n",
    "exp_DF = exp_df.groupby(['Clusters'])\n",
    "ax = sns.barplot(data=exp_df, x=\"Clusters\", y=\"total_revenue\", order=[0,1,2,3])\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"~U$%.0f\" % (p.get_height()),\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()*1.08), \n",
    "                fontsize=16,ha='center', va='bottom')\n",
    "ax.set_xlabel('Clusters', fontsize=14)\n",
    "ax.set_xticklabels(['Cluster 1', 'Cluster 2', 'Cluster 3','Cluster 4'], rotation=20, fontsize=14)\n",
    "ax.set_title('[Result Figure 2] Total Revenue for each cluster', fontsize=16)\n",
    "ax.set_ylabel('Dollars (US$)',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizations of the expenses, based on functional, service, \n",
    "# management, and fundraising expenses. \n",
    "# Normalized by total expenses\n",
    "fig = plt.figure();\n",
    "exp_df = meta[['volunteer_ct','Clusters']].copy()\n",
    "exp_DF = exp_df.groupby(['Clusters'])\n",
    "ax = sns.barplot(data=exp_df, x=\"Clusters\", y=\"volunteer_ct\", order=[0,1,2,3])\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"%.0f\" % (p.get_height()),\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()*1.08), \n",
    "                fontsize=16,ha='center', va='bottom')\n",
    "ax.set_xlabel('Clusters', fontsize=14)\n",
    "ax.set_xticklabels(['Cluster 1','Cluster 2', 'Cluster 3','Cluster 4'], rotation=20, fontsize=14)\n",
    "ax.set_title('[Result Figure 3] Volunteer Amounts for each cluster', fontsize=16)\n",
    "ax.set_ylabel('Number of Volunteers',fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}