{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auditing Federal Contractors Part II\n",
    "_By [Leon Yin](leonyin.org) Last Updated 2017-06-11_\n",
    "\n",
    "View this notebook in [NBViewer](http://nbviewer.jupyter.org/github/yinleon/us-spending/blob/master/1_analysis_methods.ipynb) or [Github](https://github.com/yinleon/us-spending/blob/master/1_analysis_methods.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis Methods\n",
    "After downloading data from CCA from the [part 1](http://nbviewer.jupyter.org/github/yinleon/us-spending/blob/master/0_get_data.ipynb) of this module, we can analyze it using Python Pandas, and Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import glob\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_in = 'data_in/spending_corrections_corporation_of_america.tsv.gz'\n",
    "df = pd.read_csv(data_in, sep='\\t', compression='gzip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall, this is what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_transaction_id</th>\n",
       "      <th>transaction_status</th>\n",
       "      <th>dollarsobligated</th>\n",
       "      <th>baseandexercisedoptionsvalue</th>\n",
       "      <th>baseandalloptionsvalue</th>\n",
       "      <th>maj_agency_cat</th>\n",
       "      <th>mod_agency</th>\n",
       "      <th>maj_fund_agency_cat</th>\n",
       "      <th>contractingofficeagencyid</th>\n",
       "      <th>contractingofficeid</th>\n",
       "      <th>...</th>\n",
       "      <th>prime_awardee_executive4</th>\n",
       "      <th>prime_awardee_executive4_compensation</th>\n",
       "      <th>prime_awardee_executive5</th>\n",
       "      <th>prime_awardee_executive5_compensation</th>\n",
       "      <th>interagencycontractingauthority</th>\n",
       "      <th>last_modified_date</th>\n",
       "      <th>lastupdate</th>\n",
       "      <th>contract_year</th>\n",
       "      <th>filename</th>\n",
       "      <th>search_terms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7418eaac867ef6ac12aeafa60195ccd8</td>\n",
       "      <td>active</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1500: JUSTICE, DEPARTMENT OF</td>\n",
       "      <td>1501: OFFICES, BOARDS AND DIVISIONS</td>\n",
       "      <td>1500: JUSTICE, DEPARTMENT OF</td>\n",
       "      <td>1501: OFFICES, BOARDS AND DIVISIONS</td>\n",
       "      <td>POS01: DEPT OF JUSTICE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X: Not Applicable</td>\n",
       "      <td>09/21/2000</td>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>2000</td>\n",
       "      <td>datafeeds\\2000_All_Contracts_Full_20170315.tsv</td>\n",
       "      <td>Corrections Corporation of America|CCA|CoreCiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01df83f4365464a1a069282bbb1fc832</td>\n",
       "      <td>active</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6400: TENNESSEE VALLEY AUTHORITY</td>\n",
       "      <td>6400: TENNESSEE VALLEY AUTHORITY</td>\n",
       "      <td>6400: TENNESSEE VALLEY AUTHORITY</td>\n",
       "      <td>6400: TENNESSEE VALLEY AUTHORITY</td>\n",
       "      <td>PURCH: TENNESSEE VALLEY AUTHORITY</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X: Not Applicable</td>\n",
       "      <td>05/22/2000</td>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>2000</td>\n",
       "      <td>datafeeds\\2000_All_Contracts_Full_20170315.tsv</td>\n",
       "      <td>Corrections Corporation of America|CCA|CoreCiv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>96cbbd9ad0ed6d3e028fae2be8453f24</td>\n",
       "      <td>active</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>1500: JUSTICE, DEPARTMENT OF</td>\n",
       "      <td>1540: FEDERAL PRISON SYSTEM</td>\n",
       "      <td>:</td>\n",
       "      <td>1540: FEDERAL PRISON SYSTEM</td>\n",
       "      <td>70000: CENTRAL OFFICE</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X: Not Applicable</td>\n",
       "      <td>06/07/2009</td>\n",
       "      <td>2017-03-15</td>\n",
       "      <td>2000</td>\n",
       "      <td>datafeeds\\2000_All_Contracts_Full_20170315.tsv</td>\n",
       "      <td>Corrections Corporation of America|CCA|CoreCiv...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 229 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              unique_transaction_id transaction_status  dollarsobligated  \\\n",
       "0  7418eaac867ef6ac12aeafa60195ccd8             active           45000.0   \n",
       "1  01df83f4365464a1a069282bbb1fc832             active           31000.0   \n",
       "2  96cbbd9ad0ed6d3e028fae2be8453f24             active           25000.0   \n",
       "\n",
       "   baseandexercisedoptionsvalue  baseandalloptionsvalue  \\\n",
       "0                           0.0                     0.0   \n",
       "1                           0.0                     0.0   \n",
       "2                       25000.0                 25000.0   \n",
       "\n",
       "                     maj_agency_cat                           mod_agency  \\\n",
       "0      1500: JUSTICE, DEPARTMENT OF  1501: OFFICES, BOARDS AND DIVISIONS   \n",
       "1  6400: TENNESSEE VALLEY AUTHORITY     6400: TENNESSEE VALLEY AUTHORITY   \n",
       "2      1500: JUSTICE, DEPARTMENT OF          1540: FEDERAL PRISON SYSTEM   \n",
       "\n",
       "                maj_fund_agency_cat            contractingofficeagencyid  \\\n",
       "0      1500: JUSTICE, DEPARTMENT OF  1501: OFFICES, BOARDS AND DIVISIONS   \n",
       "1  6400: TENNESSEE VALLEY AUTHORITY     6400: TENNESSEE VALLEY AUTHORITY   \n",
       "2                                 :          1540: FEDERAL PRISON SYSTEM   \n",
       "\n",
       "                 contractingofficeid  \\\n",
       "0             POS01: DEPT OF JUSTICE   \n",
       "1  PURCH: TENNESSEE VALLEY AUTHORITY   \n",
       "2              70000: CENTRAL OFFICE   \n",
       "\n",
       "                         ...                         prime_awardee_executive4  \\\n",
       "0                        ...                                              NaN   \n",
       "1                        ...                                              NaN   \n",
       "2                        ...                                              NaN   \n",
       "\n",
       "  prime_awardee_executive4_compensation prime_awardee_executive5  \\\n",
       "0                                   0.0                      NaN   \n",
       "1                                   0.0                      NaN   \n",
       "2                                   0.0                      NaN   \n",
       "\n",
       "  prime_awardee_executive5_compensation interagencycontractingauthority  \\\n",
       "0                                   0.0               X: Not Applicable   \n",
       "1                                   0.0               X: Not Applicable   \n",
       "2                                   0.0               X: Not Applicable   \n",
       "\n",
       "  last_modified_date  lastupdate contract_year  \\\n",
       "0         09/21/2000  2017-03-15          2000   \n",
       "1         05/22/2000  2017-03-15          2000   \n",
       "2         06/07/2009  2017-03-15          2000   \n",
       "\n",
       "                                         filename  \\\n",
       "0  datafeeds\\2000_All_Contracts_Full_20170315.tsv   \n",
       "1  datafeeds\\2000_All_Contracts_Full_20170315.tsv   \n",
       "2  datafeeds\\2000_All_Contracts_Full_20170315.tsv   \n",
       "\n",
       "                                        search_terms  \n",
       "0  Corrections Corporation of America|CCA|CoreCiv...  \n",
       "1  Corrections Corporation of America|CCA|CoreCiv...  \n",
       "2  Corrections Corporation of America|CCA|CoreCiv...  \n",
       "\n",
       "[3 rows x 229 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have access to fast counting anf crunching in Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CoreCivic recieved $14,143,641,618.04 since 2000\n"
     ]
    }
   ],
   "source": [
    "print(\"CoreCivic recieved ${:,.2f} since 2000\".format(\n",
    "    df['dollarsobligated'].sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can perform aggragate funcions to get annual dollars obligated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lu5lZgVzczcwK5OJuZlagtsVd0kmS/i7pll7GT5b0hKSZ+XF4/WmamVkVIzqY5mTgx8Ap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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11072c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('contract_year')['dollarsobligated'].sum().plot(\n",
    "    kind='bar', title='Annual USD Contract Obligations to CoreCivic'\n",
    ").set_xlabel('Contract Year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make these plots Seaborn-cool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D6XGvAfaEAQCwhBAGAMASQhgAAEsIYQAALCGEAQCwhBAGAMASQhgAAEsIYQAA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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113df5610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('contract_year')['dollarsobligated'].sum().plot(\n",
    "    kind='bar', title='Annual USD Contract Obligations to CoreCivic'\n",
    ").set_xlabel('Contract Year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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38bBxaawv9XopdHojQm04LcvE1UUChZsLs76JiKhpBEHAf769AK3eWOfz9wzp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\nFGohhBBCw6RQCyGEEBomhVoIIYTQsP8HYCVgfr72vfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114784850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['dollarsobligated'] > 0].groupby('contract_year')['dollarsobligated'].count().plot( \n",
    "    kind='line', marker= 'o', title='DOJ Number of Annual Contracts to CoreCivic'\n",
    ").set_xlabel('Contract Year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What does this look like state-to-state?\n",
    "We can create a chloropleth with metadata hover-ables with Plotly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import us\n",
    "import numpy as np\n",
    "import plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set up plotly\n",
    "[get started](https://plot.ly/python/getting-started/) on their website to get an API key."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# aset up plotly creds\n",
    "plotly.tools.set_credentials_file(\n",
    "    username='leonyin', api_key='<get it from the site!>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def group_df(df):\n",
    "    df_t = df.copy()\n",
    "    df_t['state_code'] = df_t['statecode'].str.split(':').str.get(0)\n",
    "    \n",
    "    df_merge = pd.merge(\n",
    "        pd.merge(df_t.groupby('state_code')['unique_transaction_id'].count().reset_index(), \n",
    "                 df_t.groupby('state_code')['dollarsobligated'].sum().reset_index()),\n",
    "        df_t.groupby('state_code')['dollarsobligated'].mean().reset_index(),\n",
    "        on='state_code')\n",
    "    \n",
    "    df_merge.columns = ['state_code', 'contracts_count', 'total_dollars', 'mean_contract_amount']\n",
    "    \n",
    "    return df_merge\n",
    "\n",
    "def build_annotation(df):\n",
    "    S = [str(us.states.lookup(unicode(s))) for s in df['state_code'].tolist()]\n",
    "    A = [\"Avg. Contract Val: ${:,.2f}\".format(int(a)) for a in df['mean_contract_amount'].tolist()]\n",
    "    C = [\"Num. of Contracts: {}\".format(str(c)) for c in df['contracts_count'].tolist()]\n",
    "    \n",
    "    return ['<br>'.join([s,a,c]) for (s,a,c) in zip(S, A, C)]\n",
    "\n",
    "def chloropleth(df):\n",
    "    df3 = group_df(df)\n",
    "\n",
    "    scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\n",
    "           [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n",
    "\n",
    "    data = [dict(\n",
    "            type='choropleth',\n",
    "            colorscale = scl,\n",
    "            autocolorscale = False,\n",
    "            locations = df3['state_code'],\n",
    "            z = df3['total_dollars'],\n",
    "            locationmode = 'USA-states',\n",
    "            text = build_annotation(df3),\n",
    "            marker = dict(\n",
    "                line = dict (\n",
    "                    color = 'rgb(255,255,255)',\n",
    "                    width = 2)),\n",
    "            colorbar = dict(\n",
    "                title = \"USD\"))]\n",
    "\n",
    "    layout = dict(\n",
    "            title = ('2000-2017 DOJ Total Contract Value with CoreCivic<br>'\n",
    "                     '(Hover for breakdown)'),\n",
    "            geo = dict(\n",
    "                scope='usa',\n",
    "                projection=dict(type='albers usa'),\n",
    "                showlakes=True,\n",
    "                lakecolor='rgb(255, 255, 255)'))\n",
    "\n",
    "    fig = dict( data=data, layout=layout )\n",
    "    return plotly.plotly.iplot( fig, filename='d3-cloropleth-map-corecivic' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~leonyin/60.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chloropleth(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-contractor analysis\n",
    "We can compare the contract data between two contractors.\n",
    "Using the methods from the previous section, we can download GEO group, and compare the number of contracts and dollars obligated over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def annual_spending():\n",
    "    fig, ax = plt.subplots(2, figsize=[10,6])\n",
    "\n",
    "    df_1[df_1['dollarsobligated'] > 0].groupby(\n",
    "        'contract_year')['dollarsobligated'].sum().plot( \n",
    "        kind='line', marker='o', label='CoreCivic', ax=ax[0],\n",
    "        title='DOJ Annual Contracts Obligations USD'\n",
    "    )\n",
    "\n",
    "    df_2[df_2['dollarsobligated'] > 0].groupby(\n",
    "        'contract_year')['dollarsobligated'].sum().plot( \n",
    "        kind='line',  marker='o', label='GEO',\n",
    "        color='lightblue', ax=ax[0]\n",
    "    ).get_xaxis().set_visible(False)\n",
    "    \n",
    "    ax[0].xaxis.set_ticks(np.arange(2000, 2017, 2))\n",
    "\n",
    "    df_1[df_1['dollarsobligated'] > 0].groupby(\n",
    "        'contract_year')['dollarsobligated'].count().plot( \n",
    "        kind='line', marker='o', ax=ax[1],\n",
    "        title='DOJ Number of Annual Contracts'\n",
    "    )\n",
    "\n",
    "    df_2[df_2['dollarsobligated'] > 0].groupby(\n",
    "        'contract_year')['dollarsobligated'].count().plot( \n",
    "        kind='line', marker='o', color='lightblue', ax=ax[1]\n",
    "    ).set_xlabel('Contract Year')\n",
    "\n",
    "    ax[0].legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CCA recieved $5,427,754,832.01 since 2000\n",
      "GEO Group recieved $5,216,148,631.71 since 2000\n"
     ]
    }
   ],
   "source": [
    "cca = 'data_in/spending_corrections_corporation_of_america.tsv.gz'\n",
    "df_1 = pd.read_csv(cca, sep='\\t', compression='gzip')\n",
    "\n",
    "geo = 'data_in/spending_geo_group.tsv.gz'\n",
    "df_2 = pd.read_csv(geo, sep='\\t', compression='gzip')\n",
    "\n",
    "print(\"CCA recieved ${:,.2f} since 2000\".format(\n",
    "    df_1['dollarsobligated'].sum()))\n",
    "print(\"GEO Group recieved ${:,.2f} since 2000\".format(\n",
    "    df_2['dollarsobligated'].sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DBx98AMCaNWuYOXMmV199NZmZmQC8/fbbTJ48maysLO69915cLhc5OTls2bKFJ598Eo3G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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138b4750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "annual_spending()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these methods you can dig deeper, and present some nice graphs!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}