Blog06_Relationship_Between_Executive_Compensation_Obligations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blog 6 Medium Series on Gaining an Analytics Edge using USAspending.gov Data\n",
    "\n",
    "Exploration of the top 5 highest paid executives data in the USAspending.gov files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "# previous blog post - https://medium.com/@lulstrup/5-is-federal-spending-on-ai-growing-cbb2881cf0d3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# highly_compensated employee reporting - https://usaspending-help.zendesk.com/hc/en-us/articles/360001252274-Award-Data-Question-What-entites-are-required-to-include-Executive-Compensation-data-\n",
    "# https://www.law.cornell.edu/cfr/text/48/52.204-10\n",
    "\n",
    "# From USAspending.gov FAQs\n",
    "\n",
    "# Per FFATA, entities must report the executive compensation of their five most highly compensated officers when the following three conditions are met:\n",
    "# During the preceding fiscal year, the entity received 80 percent or more of its annual gross revenues from federal awards.\n",
    "# The entity received $25M or more in annual gross revenues from federal awards.\n",
    "# The public does not have access to the compensation information through periodic reports filed under the Securities and Exchange Act of 1934 or the Internal Revenue Code of 1986. \n",
    "# If these conditions aren’t met, recipients aren’t required to provide executive compensation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, glob, pathlib\n",
    "import math\n",
    "import shutil\n",
    "from datetime import datetime\n",
    "from collections import Counter\n",
    "import zipfile\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import dask\n",
    "import dask.dataframe as dd\n",
    "from dask.distributed import Client, progress\n",
    "import psutil\n",
    "import requests\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# use import below to interactively select the folder where the USAspending Archives are located\n",
    "# https://pypi.org/project/ipyfilechooser/\n",
    "from ipyfilechooser import FileChooser\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ono Jdhe'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use this to hide the names of the companies and people for display purposes\n",
    "def Name_Scrambler(text):\n",
    "    random_letters = random.sample(list(text), len(text))\n",
    "    random_text = \"\".join(random_letters)\n",
    "    random_text = \" \".join([aWord.capitalize() for aWord in random_text.split()])\n",
    "    return random_text.strip()\n",
    "\n",
    "def Scramble_Company_Exec_Names(df):\n",
    "    df_copy = df.copy()\n",
    "    df_copy['recipient_parent_name'] = df_copy['recipient_parent_name'].apply(Name_Scrambler)\n",
    "    for n in range(5):\n",
    "        field_name = 'highly_compensated_officer_' + str(n+1) + '_name'\n",
    "        if field_name in df_copy.columns:\n",
    "            df_copy[field_name] = df_copy[field_name].apply(Name_Scrambler)\n",
    "    return df_copy\n",
    "\n",
    "Name_Scrambler(\"John Doe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Physical CPU Cores: 8, RAM available: 42 GB, total RAM: 69 GB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'CPU_Cores_Physical': 8,\n",
       " 'RAM_available': 41946275840,\n",
       " 'RAM_total_installed': 68719476736}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Display_System_Info(): # https://psutil.readthedocs.io/en/latest/#psutil.virtual_memory\n",
    "    physical_cores = psutil.cpu_count(logical=False) #, psutil.cpu_count(logical=True)\n",
    "    RAM_total_installed = psutil.virtual_memory()[0] #, psutil.swap_memory()\n",
    "    RAM_available = psutil.virtual_memory().available #['available']\n",
    "    dask_workers = int(physical_cores/2)\n",
    "    print(f\"Physical CPU Cores: {physical_cores}, RAM available: {round(RAM_available/1e9)} GB, total RAM: {round(RAM_total_installed/1e9)} GB\")\n",
    "    result_record = {'CPU_Cores_Physical' : physical_cores,\n",
    "                    'RAM_available' : RAM_available,\n",
    "                     'RAM_total_installed' : RAM_total_installed,\n",
    "                    }\n",
    "    return result_record\n",
    "\n",
    "Display_System_Info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'12/10/2020 11:09:26'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Get_Current_Time():\n",
    "    return datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n",
    "\n",
    "Get_Current_Time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8950fc4952164c4b9c3b86fa436be6b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileChooser(path='/Users/leifulstrup/Documents/Primehook/Marketing/Medium Blog Posts', filename='', title='HTM…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this assumes the jupyter notebook is running in the same higher level directory\n",
    "# where the USAspending.gov zip archives and the decompressed Zip files are stored\n",
    "\n",
    "original_cwd = os.getcwd() # use this if  files are located in local directory where jupyter is running\n",
    "# \n",
    "folder_choice = FileChooser(os.getcwd(),\n",
    "                           title='<b>Choose Folder with USAspending.gov GFY Archive Expanded CSV Files:</b>',\n",
    "                            show_hidden=False,\n",
    "                            select_default=True,\n",
    "                            use_dir_icons=True,\n",
    "                            show_only_dirs=True)\n",
    "display(folder_choice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users/YourName/YourTopFolder_Documents/YourUSAspendingFolderName/USAspending_Archive_Downloads/Expanded_CSV_Files\n"
     ]
    }
   ],
   "source": [
    "download_file_path_CSV = str(folder_choice.selected_path)\n",
    "\n",
    "your_path_dummy = 'Users/YourName/YourTopFolder_Documents/YourUSAspendingFolderName'\n",
    "#print(original_cwd)\n",
    "print(os.path.join(your_path_dummy, \"/\".join(download_file_path_CSV.split('/')[-2:])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CurrentGFY():\n",
    "    if datetime.now().month >= 10: \n",
    "        return datetime.now().year + 1\n",
    "    else:\n",
    "        return datetime.now().year\n",
    "    \n",
    "def Check_Archive_Filename_Format(filename_complete):\n",
    "    base_filename = os.path.basename(filename_complete)\n",
    "    # rule checks\n",
    "    if not base_filename.startswith(\"FY\"):\n",
    "        return False\n",
    "    if not base_filename.endswith(\".zip\") and not base_filename.endswith(\".csv\"):\n",
    "        return False\n",
    "    if \" \" in base_filename:\n",
    "        return False\n",
    "    if len(base_filename.split(\".\")) > 2:\n",
    "        return False\n",
    "    if \"copy\" in base_filename:\n",
    "        return False\n",
    "    \n",
    "    return True\n",
    "\n",
    "def Get_GFY_from_file_path(filename_to_check_complete_path):\n",
    "    if os.path.isdir(filename_to_check_complete_path):\n",
    "        #print(f\"{filename_to_check_complete_path} is a directory. Ignore\")\n",
    "        return\n",
    "    filename_GFY = os.path.basename(filename_to_check_complete_path)[:6]\n",
    "    assert filename_GFY[:2] == 'FY' # check this\n",
    "    return filename_GFY\n",
    "\n",
    "def Get_ArchiveDate_from_file_path(filename_to_check_complete_path):\n",
    "    if os.path.isdir(filename_to_check_complete_path):\n",
    "        #print(f\"{filename_to_check_complete_path} is a directory. Ignore\")\n",
    "        return\n",
    "    basefilename = os.path.basename(filename_to_check_complete_path)\n",
    "    if filename_to_check_complete_path.endswith(\".zip\"):\n",
    "        filename_latest_update = basefilename.split(\"_\")[4][0:8]\n",
    "    elif filename_to_check_complete_path.endswith(\".csv\"):\n",
    "        filename_latest_update = basefilename.split(\"_\")[4][0:8]\n",
    "    else:\n",
    "        filename_latest_update =\"Filename_Format_Issue\"\n",
    "    # assert TBD\n",
    "    return filename_latest_update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean up the Data and add some fields\n",
    "\n",
    "### function to create a recipient common name using common fields\n",
    "### addresses problem with multiple versions of names, M&A, and misspellings\n",
    "\n",
    "#### Please read this link on the USAspending.gov site about use of \"D&B Open Data\" - https://www.usaspending.gov/db_info\n",
    "#### \"D&B Open Data\" is embedded in these records\n",
    "\n",
    "#TODO - placeholder function - enhance this to find the most common name for the same company in the records \n",
    "def Add_Common_Recipient_Names(df): #placeholder for future data validation using a common key(s) or hash value\n",
    "    # add transformation and validation code to select most common name for an entity-firm-contractor\n",
    "    df['recipient_parent_name_common'] = df['recipient_parent_name']\n",
    "    df['recipient_name_common'] = df['recipient_name']\n",
    "    return\n",
    "\n",
    "# !!! possible use in the future cage_codes are missing for most records prior to GFY19\n",
    "# def Add_Common_Recipient_Names_Using_CAGE_Code(df): #works ok starting in FY19 (missing from ~200M $s in GFY19) but is unreliable prior to that - missing\n",
    "#     # Build cage_code to recipient_parent_name, recipient_name mapping and collect the most common names\n",
    "#     # There are naming inconsistencies in the USAspending.gov data for recipient_names\n",
    "#     # Create code to build a unique identifier that can be used in groupby for recipient_parent_name when there are spelling and naming inconsistencies\n",
    "\n",
    "#     cage_name_mapping = df[['action_date_fiscal_year','cage_code','recipient_parent_name', 'recipient_name']].drop_duplicates()\n",
    "#     #cage_name_mapping.shape\n",
    "\n",
    "#     #TODO add code to properyly handle how the recipient_parent_name and the recipient_name may change across GFY\n",
    "#     # get the most common recipient_name for each cage_code\n",
    "#     cage_name_mapping_recipient_parent = cage_name_mapping.groupby(['cage_code'])['recipient_parent_name'].apply(list).apply(Counter).apply(lambda x: x.most_common()[0][0])\n",
    "#     cage_name_mapping_recipient = cage_name_mapping.groupby(['cage_code'])['recipient_name'].apply(list).apply(Counter).apply(lambda x: x.most_common()[0][0])\n",
    "\n",
    "#     #cage_name_mapping_recipient_parent['00026'] # assert in FY2019 == 'MMC INTERNATIONAL CORP.'\n",
    "\n",
    "#     # add a column with the most common recipient_name for downstream groupby actions\n",
    "#     df['recipient_parent_name_common'] = df['cage_code'].apply(lambda x: cage_name_mapping_recipient_parent[x])\n",
    "#     df['recipient_name_common'] = df['cage_code'].apply(lambda x: cage_name_mapping_recipient[x])    \n",
    "#     return df\n",
    "\n",
    "def Fix_Recipient_Name_UNSPECIFIED(df):\n",
    "    #fix blank or UNSPECIFIED recipient_parent_names\n",
    "    \n",
    "#     fix_UNSPECIFIED_lambda = lambda x: x['recipient_name'] + \"_UNSPECIFIED\" if x['recipient_parent_name'] in ['', 'UNSPECIFIED'] else x['recipient_parent_name']\n",
    "#     #pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame\n",
    "#     if type(df) == dask.dataframe.core.DataFrame:\n",
    "#         df['recipient_parent_name'] = df.apply(fix_UNSPECIFIED_lambda, axis = 1, meta=('recipient_parent_name', 'object'))\n",
    "#     else:\n",
    "#         df['recipient_parent_name'] = df.apply(fix_UNSPECIFIED_lambda, axis = 1)\n",
    "    \n",
    "    df['recipient_parent_name'] = df['recipient_parent_name'].mask(df['recipient_parent_name'] == 'UNSPECIFIED', df['recipient_parent_name'] + \"_UNSPECIFIED\") # use of mask instead of other options - https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.mask\n",
    "\n",
    "    return\n",
    "\n",
    "def Fix_Recipient_Names_Known_Issues(df): # MANUAL Fixes needed due to USAspending.gov not updating recipient_parent_name (e.g., after SAIC spun out Leidos)\n",
    "    #TODO - shoould be added at time of read of the files as part of prep\n",
    "    \n",
    "    #fix Naming Issues - Manual Fixes (large firms mostly - edit the external files)\n",
    "    \n",
    "    if glob.glob(\"USAspending_Parent_Name_Fixes_Manual.csv\"):\n",
    "        df_fixes = pd.read_csv(\"USAspending_Parent_Name_Fixes_Manual.csv\")\n",
    "        manual_fixes_lookup = {}\n",
    "        for index, row in df_fixes.iterrows():\n",
    "            manual_fixes_lookup[row['recipient_old_name']] = {'recipient_new_name' :row['recipient_new_name'],\n",
    "                                                              'GFY_When_Switch_Happens' : row['GFY_When_Switch_Happens'],\n",
    "                                                             }\n",
    "    \n",
    "    else:\n",
    "        manual_fixes_lookup = {\n",
    "            'SAIC' : {'recipient_new_name' : 'LEIDOS HOLDINGS  INC.',\n",
    "                                                               'GFY_When_Switch_Happens' : '2014'}, #!!Notes ther is an extra space needed between HOLDINGS and Inc. name in USAspending, common name fix\n",
    "            'NORTHROP GRUMMAN SYSTEMS CORPORATION' : {'recipient_new_name' : 'NORTHROP GRUMMAN CORPORATION',\n",
    "                                                      'GFY_When_Switch_Happens' : ''\n",
    "                                                     },\n",
    "\n",
    "        }\n",
    "    \n",
    "    for old_name, new_name_record in manual_fixes_lookup.items():\n",
    "        new_name = new_name_record['recipient_new_name']\n",
    "        GFY_When_Switch_Happens = new_name_record['GFY_When_Switch_Happens']\n",
    "        # mask preferred via https://stackoverflow.com/questions/54360549/dask-item-assignment-cannot-use-loc-for-item-assignment\n",
    "        \n",
    "        if not math.isnan(GFY_When_Switch_Happens) and GFY_When_Switch_Happens != '':\n",
    "            selector = (df['recipient_parent_name'] == old_name) & (df['action_date_fiscal_year'].astype('int64') >= int(GFY_When_Switch_Happens))\n",
    "        else:\n",
    "            selector = (df['recipient_parent_name'] == old_name)\n",
    "        \n",
    "        df['recipient_parent_name'] = df['recipient_parent_name'].mask(selector, new_name) # use of mask instead of other options - https://docs.dask.org/en/latest/dataframe-api.html#dask.dataframe.DataFrame.mask\n",
    "        \n",
    "    return\n",
    "\n",
    "def Add_PSC_Cat_Fields(df):\n",
    "    print(f\"{Get_Current_Time()} -> adding PSC_Cat field...\")\n",
    "    df['PSC_Cat'] = df['product_or_service_code'].str[:1]\n",
    "    df['PSC_Cat_2'] = df['product_or_service_code'].str[:2]\n",
    "    return\n",
    "\n",
    "def Fix_High_Compensated_Field(df):\n",
    "    # these fields were read in via pandas as strings (pandas objects), need to convert them to float\n",
    "    # some of the fields contain text inside of values and some had NaN that was repalced with UNSPECIFIED\n",
    "    fields_for_float =[\n",
    "        'highly_compensated_officer_1_amount',\n",
    "           'highly_compensated_officer_2_amount',\n",
    "           'highly_compensated_officer_3_amount',\n",
    "           'highly_compensated_officer_4_amount',\n",
    "           'highly_compensated_officer_5_amount'\n",
    "    ]\n",
    "\n",
    "\n",
    "    for field in fields_for_float:\n",
    "        df[field] = df[field].apply(lambda x: float(x) if x.replace(\".\",\"\").isdigit() else 0.0)\n",
    "    return\n",
    "    \n",
    "\n",
    "def Enhance_Spending_File(df): # this function addresses known shortcomings in the USAspending archive data\n",
    "    print()\n",
    "    print(f\"{Get_Current_Time()} -> replacing NaN with UNSPECIFIED...\")\n",
    "    df = df.fillna(\"UNSPECIFIED\") # IMPORTANT - NA fields can affect groupby sums and other problems\n",
    "    \n",
    "    if 'product_or_service_code' in df.columns:\n",
    "        Add_PSC_Cat_Fields(df)\n",
    "    \n",
    "    print(f\"{Get_Current_Time()} -> fixing missing recipient_parent_name...\")\n",
    "    Fix_Recipient_Name_UNSPECIFIED(df)\n",
    "    print(f\"{Get_Current_Time()} -> fixing recipient_parent_names that are inconsistent...\")\n",
    "    Fix_Recipient_Names_Known_Issues(df)\n",
    "    \n",
    "    #print(f\"{Get_Current_Time()} -> adding recipient_name_common and recipient_parent_name_common fields for groupby\")\n",
    "    #Add_Common_Recipient_Names(df)\n",
    "    \n",
    "    print(f\"{Get_Current_Time()} -> converting highly_compensated field amounts to float\")\n",
    "    Fix_High_Compensated_Field(df)\n",
    "    print(f\"{Get_Current_Time()} Done with fixes.\")\n",
    "    print()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Key code for read CSV files into pandas and Dask\n",
    "def Build_DTypes_Dict(filename_list):\n",
    "    df = pd.read_csv(filename_list[0], nrows=1)\n",
    "    \n",
    "    dtype = dict(zip(sorted(df.columns), ['object'] * len(df.columns)))\n",
    "    \n",
    "    # ! important - may need to change other fields to category, int, date etc for better memory management\n",
    "    dtype['federal_action_obligation'] = 'float64'\n",
    "    #dtype['action_date_fiscal_year'] = 'int'\n",
    "    \n",
    "    return dtype\n",
    "\n",
    "def Load_CSV_Files_Into_DF(filename_list, usecols = 'ALL'): #best approach if you have enough RAM memory on your machine\n",
    "    \n",
    "    dtype = Build_DTypes_Dict(filename_list) # ! important - may need to change other fields to category, int, date etc for better memory management\n",
    "    \n",
    "#     print(f\"Reading file: {filename_list[0]}\")\n",
    "#     df = pd.read_csv(filename_list[0], dtype=dtype, low_memory=False) # read the first file\n",
    "    \n",
    "#     for filename in filename_list[1:]: #read the remaining files and append\n",
    "#         print(f\"Reading file: {filename}\")\n",
    "#         df = df.append(pd.read_csv(filename, dtype=dtype, low_memory=False))\n",
    "        \n",
    "    #alternative approach - faster?\n",
    "    dataframe_loads_list = []\n",
    "    for filename in filename_list:\n",
    "        print(f\"Reading file: {os.path.basename(filename)}\")\n",
    "        if usecols == 'ALL':\n",
    "            dataframe_loads_list.append(pd.read_csv(filename, dtype=dtype, low_memory=False))\n",
    "        else:\n",
    "            dataframe_loads_list.append(pd.read_csv(filename, dtype=dtype, usecols = usecols, low_memory=False))\n",
    "    df = pd.concat(dataframe_loads_list)\n",
    "    print(\"Files loaded into pandas dataframe.\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 FY2010_All_Contracts_Full_20200807_1.csv\n",
      "2 FY2010_All_Contracts_Full_20200807_2.csv\n",
      "3 FY2010_All_Contracts_Full_20200807_3.csv\n",
      "4 FY2010_All_Contracts_Full_20200807_4.csv\n",
      "5 FY2011_All_Contracts_Full_20200807_1.csv\n",
      "6 FY2011_All_Contracts_Full_20200807_2.csv\n",
      "7 FY2011_All_Contracts_Full_20200807_3.csv\n",
      "8 FY2011_All_Contracts_Full_20200807_4.csv\n",
      "9 FY2012_All_Contracts_Full_20200807_1.csv\n",
      "10 FY2012_All_Contracts_Full_20200807_2.csv\n",
      "11 FY2012_All_Contracts_Full_20200807_3.csv\n",
      "12 FY2012_All_Contracts_Full_20200807_4.csv\n",
      "13 FY2013_All_Contracts_Full_20200807_1.csv\n",
      "14 FY2013_All_Contracts_Full_20200807_2.csv\n",
      "15 FY2013_All_Contracts_Full_20200807_3.csv\n",
      "16 FY2014_All_Contracts_Full_20200808_1.csv\n",
      "17 FY2014_All_Contracts_Full_20200808_2.csv\n",
      "18 FY2014_All_Contracts_Full_20200808_3.csv\n",
      "19 FY2015_All_Contracts_Full_20200808_1.csv\n",
      "20 FY2015_All_Contracts_Full_20200808_2.csv\n",
      "21 FY2015_All_Contracts_Full_20200808_3.csv\n",
      "22 FY2015_All_Contracts_Full_20200808_4.csv\n",
      "23 FY2015_All_Contracts_Full_20200808_5.csv\n",
      "24 FY2016_All_Contracts_Full_20200808_1.csv\n",
      "25 FY2016_All_Contracts_Full_20200808_2.csv\n",
      "26 FY2016_All_Contracts_Full_20200808_3.csv\n",
      "27 FY2016_All_Contracts_Full_20200808_4.csv\n",
      "28 FY2016_All_Contracts_Full_20200808_5.csv\n",
      "29 FY2017_All_Contracts_Full_20200808_1.csv\n",
      "30 FY2017_All_Contracts_Full_20200808_2.csv\n",
      "31 FY2017_All_Contracts_Full_20200808_3.csv\n",
      "32 FY2017_All_Contracts_Full_20200808_4.csv\n",
      "33 FY2017_All_Contracts_Full_20200808_5.csv\n",
      "34 FY2018_All_Contracts_Full_20200808_1.csv\n",
      "35 FY2018_All_Contracts_Full_20200808_2.csv\n",
      "36 FY2018_All_Contracts_Full_20200808_3.csv\n",
      "37 FY2018_All_Contracts_Full_20200808_4.csv\n",
      "38 FY2018_All_Contracts_Full_20200808_5.csv\n",
      "39 FY2018_All_Contracts_Full_20200808_6.csv\n",
      "40 FY2019_All_Contracts_Full_20200914_1.csv\n",
      "41 FY2019_All_Contracts_Full_20200914_2.csv\n",
      "42 FY2019_All_Contracts_Full_20200914_3.csv\n",
      "43 FY2019_All_Contracts_Full_20200914_4.csv\n",
      "44 FY2019_All_Contracts_Full_20200914_5.csv\n",
      "45 FY2019_All_Contracts_Full_20200914_6.csv\n",
      "46 FY2019_All_Contracts_Full_20200914_7.csv\n",
      "47 FY2020_All_Contracts_Full_20200914_1.csv\n",
      "48 FY2020_All_Contracts_Full_20200914_2.csv\n",
      "49 FY2020_All_Contracts_Full_20200914_3.csv\n",
      "50 FY2020_All_Contracts_Full_20200914_4.csv\n",
      "51 FY2020_All_Contracts_Full_20200914_5.csv\n",
      "\n",
      "There are 51 CSV files. They use 85.358 GB of storage.\n",
      "Covering GFY: ['FY2010', 'FY2011', 'FY2012', 'FY2013', 'FY2014', 'FY2015', 'FY2016', 'FY2017', 'FY2018', 'FY2019', 'FY2020']\n",
      "Current GFY: FY2021\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# do a quick check to make sure the CSV files are there\n",
    "csv_files_list = sorted(glob.glob(os.path.join(download_file_path_CSV, \"*.csv\")))\n",
    "\n",
    "gfy_list = set()\n",
    "size_of_files_GB = 0\n",
    "for n, filename in enumerate(csv_files_list):\n",
    "    filename_basename = os.path.basename(filename)\n",
    "    print(n+1, filename_basename)\n",
    "    gfy_list.add(filename_basename[:6])\n",
    "    size_of_files_GB += os.path.getsize(filename)\n",
    "\n",
    "all_GFY_list = sorted(set([Get_GFY_from_file_path(filename) for filename in csv_files_list]))\n",
    "    \n",
    "print()\n",
    "print(f\"There are {len(csv_files_list)} CSV files. They use {round(size_of_files_GB/1e9, 3)} GB of storage.\")\n",
    "print(f\"Covering GFY: {sorted(gfy_list)}\")\n",
    "print(f\"Current GFY: {'FY' + str(CurrentGFY())}\")\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load GFY2010-GFY2019 with limited Columns even though we will mostly use GFY19 (demonstrate usecols feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have selected these GFY for analysis:['FY2019', 'FY2018', 'FY2017', 'FY2016', 'FY2015', 'FY2014', 'FY2013', 'FY2012', 'FY2011', 'FY2010']\n",
      "Missing GFY: [] -> if GFY missing, confirm you have decompressed the GFY zip file archive\n"
     ]
    }
   ],
   "source": [
    "# You can limit the selection to a subset of the GFY to speed processing\n",
    "\n",
    "ALL_GFY = False # switch to True to override the subset\n",
    "\n",
    "user_select_GFY = ['FY2019', 'FY2018', 'FY2017', 'FY2016', 'FY2015', 'FY2014', 'FY2013', 'FY2012', 'FY2011', 'FY2010']\n",
    "\n",
    "if ALL_GFY:\n",
    "    user_select_GFY = all_GFY_list\n",
    "    \n",
    "# print(f\"GFY to read for analysis: {sorted(user_select_GFY)}\")\n",
    "# print()\n",
    "\n",
    "# Create csv_files_list_restricted to streamline downstream data processing\n",
    "\n",
    "csv_files_list = sorted(glob.glob(os.path.join(download_file_path_CSV, \"*.csv\")))\n",
    "csv_files_list_restricted = [filename for filename in csv_files_list for GFY in user_select_GFY if GFY in filename]\n",
    "gfy_found_in_files = [Get_GFY_from_file_path(filename) for filename in csv_files_list_restricted]\n",
    "csv_GFY_missing = sorted(set(user_select_GFY) - set(gfy_found_in_files))\n",
    "\n",
    "assert set(user_select_GFY) == set(user_select_GFY) # check to make sure this is working\n",
    "\n",
    "print(f\"You have selected these GFY for analysis:{user_select_GFY}\")\n",
    "print(f\"Missing GFY: {csv_GFY_missing} -> if GFY missing, confirm you have decompressed the GFY zip file archive\")\n",
    "\n",
    "#[(os.path.basename(filename), round(os.path.getsize(filename)/1e9, 3)) for filename in csv_files_list_restricted]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.2 ms, sys: 5.23 ms, total: 34.4 ms\n",
      "Wall time: 30.1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Dask uses pandas but operates in a 'lazy' mode by constructing a graph of operations to perform\n",
    "# unlike pandas which immediately processes the data in memory, Dask requires you to add a '.compute()'\n",
    "# to specify that you want to compute the values at that point. Dask then begins processing the files on disk.\n",
    "\n",
    "dtype = Build_DTypes_Dict(csv_files_list_restricted) # this handles reduces ambiguity for dask interpreting data type to infer on read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Since we only need a few fields from the ~280 fields, we can radically reduce the memory needed and use pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fields = sorted(dtype.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we do not need all of the 280+ fields from the records\n",
    "# usecols = in pd.read_csv and Dask allows one to be selective on the fields and save memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['highly_compensated_officer_1_amount',\n",
       " 'highly_compensated_officer_1_name',\n",
       " 'highly_compensated_officer_2_amount',\n",
       " 'highly_compensated_officer_2_name',\n",
       " 'highly_compensated_officer_3_amount',\n",
       " 'highly_compensated_officer_3_name',\n",
       " 'highly_compensated_officer_4_amount',\n",
       " 'highly_compensated_officer_4_name',\n",
       " 'highly_compensated_officer_5_amount',\n",
       " 'highly_compensated_officer_5_name']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[column for column in fields if 'highly_compensated' in column]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "usecols = ['action_date_fiscal_year',\n",
    "           'recipient_parent_name',\n",
    "           'recipient_name',\n",
    "           'federal_action_obligation',\n",
    "           'funding_agency_name',\n",
    "           'funding_sub_agency_name',\n",
    "           'product_or_service_code_description',\n",
    "           'product_or_service_code',\n",
    "           'highly_compensated_officer_1_amount',\n",
    " 'highly_compensated_officer_1_name',\n",
    " 'highly_compensated_officer_2_amount',\n",
    " 'highly_compensated_officer_2_name',\n",
    " 'highly_compensated_officer_3_amount',\n",
    " 'highly_compensated_officer_3_name',\n",
    " 'highly_compensated_officer_4_amount',\n",
    " 'highly_compensated_officer_4_name',\n",
    " 'highly_compensated_officer_5_amount',\n",
    " 'highly_compensated_officer_5_name'\n",
    "          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading file: FY2010_All_Contracts_Full_20200807_1.csv\n",
      "Reading file: FY2010_All_Contracts_Full_20200807_2.csv\n",
      "Reading file: FY2010_All_Contracts_Full_20200807_3.csv\n",
      "Reading file: FY2010_All_Contracts_Full_20200807_4.csv\n",
      "Reading file: FY2011_All_Contracts_Full_20200807_1.csv\n",
      "Reading file: FY2011_All_Contracts_Full_20200807_2.csv\n",
      "Reading file: FY2011_All_Contracts_Full_20200807_3.csv\n",
      "Reading file: FY2011_All_Contracts_Full_20200807_4.csv\n",
      "Reading file: FY2012_All_Contracts_Full_20200807_1.csv\n",
      "Reading file: FY2012_All_Contracts_Full_20200807_2.csv\n",
      "Reading file: FY2012_All_Contracts_Full_20200807_3.csv\n",
      "Reading file: FY2012_All_Contracts_Full_20200807_4.csv\n",
      "Reading file: FY2013_All_Contracts_Full_20200807_1.csv\n",
      "Reading file: FY2013_All_Contracts_Full_20200807_2.csv\n",
      "Reading file: FY2013_All_Contracts_Full_20200807_3.csv\n",
      "Reading file: FY2014_All_Contracts_Full_20200808_1.csv\n",
      "Reading file: FY2014_All_Contracts_Full_20200808_2.csv\n",
      "Reading file: FY2014_All_Contracts_Full_20200808_3.csv\n",
      "Reading file: FY2015_All_Contracts_Full_20200808_1.csv\n",
      "Reading file: FY2015_All_Contracts_Full_20200808_2.csv\n",
      "Reading file: FY2015_All_Contracts_Full_20200808_3.csv\n",
      "Reading file: FY2015_All_Contracts_Full_20200808_4.csv\n",
      "Reading file: FY2015_All_Contracts_Full_20200808_5.csv\n",
      "Reading file: FY2016_All_Contracts_Full_20200808_1.csv\n",
      "Reading file: FY2016_All_Contracts_Full_20200808_2.csv\n",
      "Reading file: FY2016_All_Contracts_Full_20200808_3.csv\n",
      "Reading file: FY2016_All_Contracts_Full_20200808_4.csv\n",
      "Reading file: FY2016_All_Contracts_Full_20200808_5.csv\n",
      "Reading file: FY2017_All_Contracts_Full_20200808_1.csv\n",
      "Reading file: FY2017_All_Contracts_Full_20200808_2.csv\n",
      "Reading file: FY2017_All_Contracts_Full_20200808_3.csv\n",
      "Reading file: FY2017_All_Contracts_Full_20200808_4.csv\n",
      "Reading file: FY2017_All_Contracts_Full_20200808_5.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_1.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_2.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_3.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_4.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_5.csv\n",
      "Reading file: FY2018_All_Contracts_Full_20200808_6.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_1.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_2.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_3.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_4.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_5.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_6.csv\n",
      "Reading file: FY2019_All_Contracts_Full_20200914_7.csv\n",
      "Files loaded into pandas dataframe.\n",
      "CPU times: user 9min 13s, sys: 1min 56s, total: 11min 9s\n",
      "Wall time: 11min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = Load_CSV_Files_Into_DF(csv_files_list_restricted, usecols = usecols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas DataFrame memory usage: 6.278858712 GB\n"
     ]
    }
   ],
   "source": [
    "print(f\"pandas DataFrame memory usage: {df.memory_usage().sum()/1e9} GB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dask related routines for those w/o enough RAM\n",
    "# def Close_Dask_Client_Robust():\n",
    "#     global client\n",
    "#     try:\n",
    "#         client\n",
    "#     except NameError:\n",
    "#         client = None\n",
    "\n",
    "#     if client:\n",
    "#         client.close() # you can use this command to close the Dask processes and then repeat the command above to launch it again\n",
    "\n",
    "# global client\n",
    "\n",
    "# Close_Dask_Client_Robust()\n",
    "\n",
    "# # psutil.cpu_count(logical=True)/2 => 1/2 of 'logical' cores available\n",
    "# dask_workers = int(psutil.cpu_count(logical=True)/2) # use half of the available cores to be safe\n",
    "# client = Client(n_workers=dask_workers) # if you have an NVIDIA card and CUDA installed you can take advantage of that here\n",
    "\n",
    "# client # open the Dashboard link in a separate browser window that you can observe during the computations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # for pandas DataFrames it is typical to see df and for Dask DataFrames ddf\n",
    "# # note that dask operates above the pandas level and depends on pandas making it easy to \n",
    "# #    convert ddf files into pandas df if there is enough memory on your machine\n",
    "# # Working with pandas df in memory is super fast and ideal.\n",
    "# # If you can reduce the data to a smaller set that fits in RAM, you should do that early and use pandas for analysis\n",
    "# # See https://docs.dask.org/en/latest/best-practices.html for more advice on streamlining your computations\n",
    "# # here is a great overview of the advantages of Dask (vs PySpark) - https://www.youtube.com/watch?v=tx7qTHSlHKw presentation by https://www.linkedin.com/in/vaibhavs10/?originalSubdomain=in of Deloitte\n",
    "\n",
    "\n",
    "# ddf = dd.read_csv(csv_files_list_restricted, dtype=dtype) #!!! note the .compute() which executes the task in a distributed way. If it fits in memory it will operate as a pandas dataframe.\n",
    "\n",
    "# # Clean up the Data and add some fields\n",
    "# ddf = Enhance_Spending_File(ddf)\n",
    "\n",
    "# ddf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "12/10/2020 11:24:18 -> replacing NaN with UNSPECIFIED...\n",
      "12/10/2020 11:24:52 -> adding PSC_Cat field...\n",
      "12/10/2020 11:25:05 -> fixing missing recipient_parent_name...\n",
      "12/10/2020 11:25:15 -> fixing recipient_parent_names that are inconsistent...\n",
      "12/10/2020 11:25:24 -> converting highly_compensated field amounts to float\n",
      "12/10/2020 11:26:35 Done with fixes.\n",
      "\n"
     ]
    },
    {
     "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>federal_action_obligation</th>\n",
       "      <th>action_date_fiscal_year</th>\n",
       "      <th>funding_agency_name</th>\n",
       "      <th>funding_sub_agency_name</th>\n",
       "      <th>recipient_name</th>\n",
       "      <th>recipient_parent_name</th>\n",
       "      <th>product_or_service_code</th>\n",
       "      <th>product_or_service_code_description</th>\n",
       "      <th>highly_compensated_officer_1_name</th>\n",
       "      <th>highly_compensated_officer_1_amount</th>\n",
       "      <th>highly_compensated_officer_2_name</th>\n",
       "      <th>highly_compensated_officer_2_amount</th>\n",
       "      <th>highly_compensated_officer_3_name</th>\n",
       "      <th>highly_compensated_officer_3_amount</th>\n",
       "      <th>highly_compensated_officer_4_name</th>\n",
       "      <th>highly_compensated_officer_4_amount</th>\n",
       "      <th>highly_compensated_officer_5_name</th>\n",
       "      <th>highly_compensated_officer_5_amount</th>\n",
       "      <th>PSC_Cat</th>\n",
       "      <th>PSC_Cat_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2010</td>\n",
       "      <td>DEPARTMENT OF DEFENSE (DOD)</td>\n",
       "      <td>DEPT OF THE ARMY</td>\n",
       "      <td>BOOZ ALLEN HAMILTON INC.</td>\n",
       "      <td>BOOZ ALLEN HAMILTON HOLDING CORPORATION</td>\n",
       "      <td>R499</td>\n",
       "      <td>OTHER PROFESSIONAL SERVICES</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2010</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>ALLSTEEL INC.</td>\n",
       "      <td>HNI CORPORATION</td>\n",
       "      <td>7110</td>\n",
       "      <td>OFFICE FURNITURE</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>840000.00</td>\n",
       "      <td>2010</td>\n",
       "      <td>NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ...</td>\n",
       "      <td>NATIONAL AERONAUTICS AND SPACE ADMINISTRATION</td>\n",
       "      <td>THE PENNSYLVANIA STATE UNIVERS</td>\n",
       "      <td>PENNSYLVANIA STATE UNIVERSITY, THE</td>\n",
       "      <td>AR21</td>\n",
       "      <td>R&amp;D-SPACE SCIENCE &amp; APPL-B RES</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>186174.39</td>\n",
       "      <td>2010</td>\n",
       "      <td>DEPARTMENT OF DEFENSE (DOD)</td>\n",
       "      <td>DEPT OF THE ARMY</td>\n",
       "      <td>BOOZ ALLEN HAMILTON INC.</td>\n",
       "      <td>BOOZ ALLEN HAMILTON HOLDING CORPORATION</td>\n",
       "      <td>R499</td>\n",
       "      <td>OTHER PROFESSIONAL SERVICES</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>R</td>\n",
       "      <td>R4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2010</td>\n",
       "      <td>DEPARTMENT OF DEFENSE (DOD)</td>\n",
       "      <td>DEPT OF THE NAVY</td>\n",
       "      <td>VELAN INC</td>\n",
       "      <td>SOCIETE HOLDING VELAN LTEE</td>\n",
       "      <td>4820</td>\n",
       "      <td>VALVES, NONPOWERED</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNSPECIFIED</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   federal_action_obligation action_date_fiscal_year  \\\n",
       "0                       0.00                    2010   \n",
       "1                       0.00                    2010   \n",
       "2                  840000.00                    2010   \n",
       "3                  186174.39                    2010   \n",
       "4                       0.00                    2010   \n",
       "\n",
       "                                 funding_agency_name  \\\n",
       "0                        DEPARTMENT OF DEFENSE (DOD)   \n",
       "1                                        UNSPECIFIED   \n",
       "2  NATIONAL AERONAUTICS AND SPACE ADMINISTRATION ...   \n",
       "3                        DEPARTMENT OF DEFENSE (DOD)   \n",
       "4                        DEPARTMENT OF DEFENSE (DOD)   \n",
       "\n",
       "                         funding_sub_agency_name  \\\n",
       "0                               DEPT OF THE ARMY   \n",
       "1                                    UNSPECIFIED   \n",
       "2  NATIONAL AERONAUTICS AND SPACE ADMINISTRATION   \n",
       "3                               DEPT OF THE ARMY   \n",
       "4                               DEPT OF THE NAVY   \n",
       "\n",
       "                   recipient_name                    recipient_parent_name  \\\n",
       "0        BOOZ ALLEN HAMILTON INC.  BOOZ ALLEN HAMILTON HOLDING CORPORATION   \n",
       "1                   ALLSTEEL INC.                          HNI CORPORATION   \n",
       "2  THE PENNSYLVANIA STATE UNIVERS       PENNSYLVANIA STATE UNIVERSITY, THE   \n",
       "3        BOOZ ALLEN HAMILTON INC.  BOOZ ALLEN HAMILTON HOLDING CORPORATION   \n",
       "4                       VELAN INC               SOCIETE HOLDING VELAN LTEE   \n",
       "\n",
       "  product_or_service_code product_or_service_code_description  \\\n",
       "0                    R499         OTHER PROFESSIONAL SERVICES   \n",
       "1                    7110                    OFFICE FURNITURE   \n",
       "2                    AR21      R&D-SPACE SCIENCE & APPL-B RES   \n",
       "3                    R499         OTHER PROFESSIONAL SERVICES   \n",
       "4                    4820                  VALVES, NONPOWERED   \n",
       "\n",
       "  highly_compensated_officer_1_name  highly_compensated_officer_1_amount  \\\n",
       "0                       UNSPECIFIED                                  0.0   \n",
       "1                       UNSPECIFIED                                  0.0   \n",
       "2                       UNSPECIFIED                                  0.0   \n",
       "3                       UNSPECIFIED                                  0.0   \n",
       "4                       UNSPECIFIED                                  0.0   \n",
       "\n",
       "  highly_compensated_officer_2_name  highly_compensated_officer_2_amount  \\\n",
       "0                       UNSPECIFIED                                  0.0   \n",
       "1                       UNSPECIFIED                                  0.0   \n",
       "2                       UNSPECIFIED                                  0.0   \n",
       "3                       UNSPECIFIED                                  0.0   \n",
       "4                       UNSPECIFIED                                  0.0   \n",
       "\n",
       "  highly_compensated_officer_3_name  highly_compensated_officer_3_amount  \\\n",
       "0                       UNSPECIFIED                                  0.0   \n",
       "1                       UNSPECIFIED                                  0.0   \n",
       "2                       UNSPECIFIED                                  0.0   \n",
       "3                       UNSPECIFIED                                  0.0   \n",
       "4                       UNSPECIFIED                                  0.0   \n",
       "\n",
       "  highly_compensated_officer_4_name  highly_compensated_officer_4_amount  \\\n",
       "0                       UNSPECIFIED                                  0.0   \n",
       "1                       UNSPECIFIED                                  0.0   \n",
       "2                       UNSPECIFIED                                  0.0   \n",
       "3                       UNSPECIFIED                                  0.0   \n",
       "4                       UNSPECIFIED                                  0.0   \n",
       "\n",
       "  highly_compensated_officer_5_name  highly_compensated_officer_5_amount  \\\n",
       "0                       UNSPECIFIED                                  0.0   \n",
       "1                       UNSPECIFIED                                  0.0   \n",
       "2                       UNSPECIFIED                                  0.0   \n",
       "3                       UNSPECIFIED                                  0.0   \n",
       "4                       UNSPECIFIED                                  0.0   \n",
       "\n",
       "  PSC_Cat PSC_Cat_2  \n",
       "0       R        R4  \n",
       "1       7        71  \n",
       "2       A        AR  \n",
       "3       R        R4  \n",
       "4       4        48  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Enhance_Spending_File(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['federal_action_obligation', 'action_date_fiscal_year',\n",
       "       'funding_agency_name', 'funding_sub_agency_name', 'recipient_name',\n",
       "       'recipient_parent_name', 'product_or_service_code',\n",
       "       'product_or_service_code_description',\n",
       "       'highly_compensated_officer_1_name',\n",
       "       'highly_compensated_officer_1_amount',\n",
       "       'highly_compensated_officer_2_name',\n",
       "       'highly_compensated_officer_2_amount',\n",
       "       'highly_compensated_officer_3_name',\n",
       "       'highly_compensated_officer_3_amount',\n",
       "       'highly_compensated_officer_4_name',\n",
       "       'highly_compensated_officer_4_amount',\n",
       "       'highly_compensated_officer_5_name',\n",
       "       'highly_compensated_officer_5_amount', 'PSC_Cat', 'PSC_Cat_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "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>action_date_fiscal_year</th>\n",
       "      <th>federal_action_obligation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>5.610369e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>5.398079e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>5.208065e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>4.633853e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>4.461897e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>4.397271e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>4.754231e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>5.105917e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>5.553379e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>5.898982e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  action_date_fiscal_year  federal_action_obligation\n",
       "0                    2010               5.610369e+11\n",
       "1                    2011               5.398079e+11\n",
       "2                    2012               5.208065e+11\n",
       "3                    2013               4.633853e+11\n",
       "4                    2014               4.461897e+11\n",
       "5                    2015               4.397271e+11\n",
       "6                    2016               4.754231e+11\n",
       "7                    2017               5.105917e+11\n",
       "8                    2018               5.553379e+11\n",
       "9                    2019               5.898982e+11"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### If you launch Dask computations but want to stop it, select the square jupyter button \"interrupt the kernel\"\n",
    "\n",
    "##This can be used as a QC check to make sure the GFY requested were read\n",
    "##this is a slow computation since Dask must read every record in all of the files\n",
    "# action_date_fiscal_yearS = set(sorted(ddf['action_date_fiscal_year'].unique()))\n",
    "# assert action_date_fiscal_yearS == user_select_GFY\n",
    "\n",
    "# Without the command .compute() at the end of the next statement, Dask adds the task to a graph of operations.\n",
    "# when Dask encounters a .compute(), the preceding graph operations are performed\n",
    "\n",
    "df_GFY_obligations = df.groupby(['action_date_fiscal_year'])['federal_action_obligation'].sum().reset_index()\n",
    "\n",
    "# You can check the computations against these articles:\n",
    "# https://about.bgov.com/news/federal-contract-spending-five-trends-in-five-charts/\n",
    "df_GFY_obligations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8e8f1c950>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_GFY_obligations.plot(x='action_date_fiscal_year', y='federal_action_obligation',title=\"Federal Contractor Obligations $s\",grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8e8bc5b90>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['action_date_fiscal_year'])['federal_action_obligation'].count().plot(title='Obligation Transactions Count', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.48 s, sys: 271 ms, total: 8.75 s\n",
      "Wall time: 8.76 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_firm_obs = df.groupby(['recipient_parent_name', 'action_date_fiscal_year'])['federal_action_obligation'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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>recipient_parent_name</th>\n",
       "      <th>action_date_fiscal_year</th>\n",
       "      <th>federal_action_obligation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!SCITAMEHTAM</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"A\" PLUS CLEANING &amp; RESTORATION, INC.</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"A\" STREET COLLISION INC.</td>\n",
       "      <td>2017</td>\n",
       "      <td>3475.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"A\" STREET COLLISION INC.</td>\n",
       "      <td>2018</td>\n",
       "      <td>3791.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"A\" STREET COLLISION INC.</td>\n",
       "      <td>2019</td>\n",
       "      <td>7638.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   recipient_parent_name action_date_fiscal_year  \\\n",
       "0                           !SCITAMEHTAM                    2013   \n",
       "1  \"A\" PLUS CLEANING & RESTORATION, INC.                    2015   \n",
       "2              \"A\" STREET COLLISION INC.                    2017   \n",
       "3              \"A\" STREET COLLISION INC.                    2018   \n",
       "4              \"A\" STREET COLLISION INC.                    2019   \n",
       "\n",
       "   federal_action_obligation  \n",
       "0                       0.00  \n",
       "1                       0.00  \n",
       "2                    3475.00  \n",
       "3                    3791.44  \n",
       "4                    7638.48  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_firm_obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "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>action_date_fiscal_year</th>\n",
       "      <th>recipient_parent_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>179567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011</td>\n",
       "      <td>177791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012</td>\n",
       "      <td>163406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>153683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>152455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>170433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016</td>\n",
       "      <td>134453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017</td>\n",
       "      <td>138507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018</td>\n",
       "      <td>124803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019</td>\n",
       "      <td>114678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  action_date_fiscal_year  recipient_parent_name\n",
       "0                    2010                 179567\n",
       "1                    2011                 177791\n",
       "2                    2012                 163406\n",
       "3                    2013                 153683\n",
       "4                    2014                 152455\n",
       "5                    2015                 170433\n",
       "6                    2016                 134453\n",
       "7                    2017                 138507\n",
       "8                    2018                 124803\n",
       "9                    2019                 114678"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recipient_per_GFY = df_firm_obs.groupby(['action_date_fiscal_year'])['recipient_parent_name'].count().reset_index()\n",
    "df_recipient_per_GFY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8e8ab0450>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_recipient_per_GFY.plot(x='action_date_fiscal_year', y='recipient_parent_name', title='Unique recipient_parent_name Counts',grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore 'Highly Compendated Officer' Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['federal_action_obligation', 'action_date_fiscal_year',\n",
       "       'funding_agency_name', 'funding_sub_agency_name', 'recipient_name',\n",
       "       'recipient_parent_name', 'product_or_service_code',\n",
       "       'product_or_service_code_description',\n",
       "       'highly_compensated_officer_1_name',\n",
       "       'highly_compensated_officer_1_amount',\n",
       "       'highly_compensated_officer_2_name',\n",
       "       'highly_compensated_officer_2_amount',\n",
       "       'highly_compensated_officer_3_name',\n",
       "       'highly_compensated_officer_3_amount',\n",
       "       'highly_compensated_officer_4_name',\n",
       "       'highly_compensated_officer_4_amount',\n",
       "       'highly_compensated_officer_5_name',\n",
       "       'highly_compensated_officer_5_amount', 'PSC_Cat', 'PSC_Cat_2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9506528485172259"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['highly_compensated_officer_1_amount'] == 0.0]['highly_compensated_officer_1_amount'].count()/df['highly_compensated_officer_1_amount'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df[df['highly_compensated_officer_1_amount'] < 1e5]['highly_compensated_officer_1_amount'].count()/df['highly_compensated_officer_1_amount'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for exec_n in range(1,5):\n",
    "    numerator_field_name = 'highly_compensated_officer_' + str(exec_n+1) + '_amount'\n",
    "    new_field_name = 'highly_compensated_officer_1_multiple_vs_officer_n_' + str(exec_n+1)\n",
    "    df[new_field_name] = df['highly_compensated_officer_1_amount']/df[numerator_field_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_date_fiscal_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>3541570.0</td>\n",
       "      <td>28063.884248</td>\n",
       "      <td>2.035951e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3406407.0</td>\n",
       "      <td>21162.657477</td>\n",
       "      <td>1.127029e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>3126397.0</td>\n",
       "      <td>21442.493925</td>\n",
       "      <td>1.040783e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2512001.0</td>\n",
       "      <td>28002.421462</td>\n",
       "      <td>1.290767e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2526925.0</td>\n",
       "      <td>30500.695024</td>\n",
       "      <td>1.535733e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>4373442.0</td>\n",
       "      <td>35604.172796</td>\n",
       "      <td>1.346277e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4819816.0</td>\n",
       "      <td>40381.158937</td>\n",
       "      <td>1.214812e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4910006.0</td>\n",
       "      <td>29592.454360</td>\n",
       "      <td>1.036584e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>5613990.0</td>\n",
       "      <td>126179.635783</td>\n",
       "      <td>2.471249e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.063365e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6477727.0</td>\n",
       "      <td>98184.465587</td>\n",
       "      <td>1.960602e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.908123e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             count           mean           std  min  25%  \\\n",
       "action_date_fiscal_year                                                     \n",
       "2010                     3541570.0   28063.884248  2.035951e+06  0.0  0.0   \n",
       "2011                     3406407.0   21162.657477  1.127029e+06  0.0  0.0   \n",
       "2012                     3126397.0   21442.493925  1.040783e+06  0.0  0.0   \n",
       "2013                     2512001.0   28002.421462  1.290767e+06  0.0  0.0   \n",
       "2014                     2526925.0   30500.695024  1.535733e+06  0.0  0.0   \n",
       "2015                     4373442.0   35604.172796  1.346277e+06  0.0  0.0   \n",
       "2016                     4819816.0   40381.158937  1.214812e+06  0.0  0.0   \n",
       "2017                     4910006.0   29592.454360  1.036584e+06  0.0  0.0   \n",
       "2018                     5613990.0  126179.635783  2.471249e+06  0.0  0.0   \n",
       "2019                     6477727.0   98184.465587  1.960602e+06  0.0  0.0   \n",
       "\n",
       "                         50%  75%           max  \n",
       "action_date_fiscal_year                          \n",
       "2010                     0.0  0.0  3.063365e+08  \n",
       "2011                     0.0  0.0  3.063365e+08  \n",
       "2012                     0.0  0.0  3.063365e+08  \n",
       "2013                     0.0  0.0  3.063365e+08  \n",
       "2014                     0.0  0.0  3.063365e+08  \n",
       "2015                     0.0  0.0  3.063365e+08  \n",
       "2016                     0.0  0.0  3.063365e+08  \n",
       "2017                     0.0  0.0  3.063365e+08  \n",
       "2018                     0.0  0.0  3.063365e+08  \n",
       "2019                     0.0  0.0  1.908123e+09  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Note: the count field is the number of records; NOT the number of unique firms\n",
    "df.groupby(['action_date_fiscal_year'])['highly_compensated_officer_1_amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe271eb4690>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note addition of , showfliers=False to remove outliers - https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.boxplot.html\n",
    "df[(df['highly_compensated_officer_1_amount'] > 0.0)].boxplot(column = 'highly_compensated_officer_1_amount', by = 'action_date_fiscal_year', showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2154 firms in GFY2019 with executive 1 compensation reported.\n",
      "Fraction of GFY19 recipient_parent_firms with executive 1 compensation: 0.01878302725893371\n",
      "Fraction of GFY19 obligation transactions with executive 1 compensation: 0.10382345535710288\n"
     ]
    }
   ],
   "source": [
    "# Identify the most common (aka mode) compensation figure for the executive_1 amount\n",
    "def Get_Mode(compensation_list):\n",
    "    counts = Counter(compensation_list)\n",
    "    return counts.most_common(1)[0][0]\n",
    "\n",
    "query = \"action_date_fiscal_year == '2019' and highly_compensated_officer_1_amount > 0.0\"\n",
    "df_exec_1_mode = df.query(query).groupby(['recipient_parent_name'])['highly_compensated_officer_1_amount'].apply(list).apply(Get_Mode).reset_index()\n",
    "\n",
    "print(f\"There are {df_exec_1_mode.shape[0]} firms in GFY2019 with executive 1 compensation reported.\")\n",
    "fraction_of_GFY19_with_exec_1_compensation = df_exec_1_mode.shape[0]/df.query(\"action_date_fiscal_year == '2019'\")['recipient_parent_name'].unique().shape[0]\n",
    "print(f\"Fraction of GFY19 recipient_parent_firms with executive 1 compensation: {fraction_of_GFY19_with_exec_1_compensation}\")\n",
    "fraction_of_GFY19_obligations_actions_with_exec_1_compensation =df.query(query).shape[0]/df.query(\"action_date_fiscal_year == '2019'\").shape[0]\n",
    "print(f\"Fraction of GFY19 obligation transactions with executive 1 compensation: {fraction_of_GFY19_obligations_actions_with_exec_1_compensation}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Caveats: There are errors in the data\n",
    "\n",
    "Companies self report this information. There are data entry errors and likely some rounding of values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "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>recipient_parent_name</th>\n",
       "      <th>highly_compensated_officer_1_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>BANK OF NEW YORK MELLON CORPORATION  THE</td>\n",
       "      <td>1.908123e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        recipient_parent_name  \\\n",
       "256  BANK OF NEW YORK MELLON CORPORATION  THE   \n",
       "\n",
       "     highly_compensated_officer_1_amount  \n",
       "256                         1.908123e+09  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example case of the highest compensation listed in the data\n",
    "df_exec_1_mode[df_exec_1_mode['highly_compensated_officer_1_amount'] == df_exec_1_mode['highly_compensated_officer_1_amount'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# it is a publicly traded company so one can look up the compensation in SEC reporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33133     1.916560e+07\n",
       "945542    1.916560e+07\n",
       "945621    1.348243e+07\n",
       "945865    1.916560e+07\n",
       "946227    1.916560e+07\n",
       "946515    1.916560e+07\n",
       "243146    1.908123e+09\n",
       "243869    1.908123e+09\n",
       "Name: highly_compensated_officer_1_amount, dtype: float64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_name = df_exec_1_mode[df_exec_1_mode['highly_compensated_officer_1_amount'] == df_exec_1_mode['highly_compensated_officer_1_amount'].max()]['recipient_parent_name'].iloc[0]\n",
    "\n",
    "df[(df['recipient_parent_name'] == company_name) & (df['highly_compensated_officer_1_amount'] > 0.0)]['highly_compensated_officer_1_amount']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The 2018 SEC filing shows the CEO compensation for 2017 closer to $17m (1.7e7)\n",
    "# SEC document - https://www.sec.gov/Archives/edgar/data/1390777/000119312519069151/d638623ddef14a.htm#toc638623_12\n",
    "# The 1.9e9 is clearly a typo.\n",
    "# Also, many of the entries showed no data for the compensation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select subset of Firms with work in PSC_Cat D and R with Exec_1 between 100k and 10M in compensation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Which companies have work in PSC_Cat D (IT and Telecom) and R (Professional Services)?\n",
    "recipient_parent_names_with_PSC_Cat_D_R_obligations = df[df['PSC_Cat'].isin(['D','R'])]['recipient_parent_name'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>action_date_fiscal_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>26359.0</td>\n",
       "      <td>1.263430e+06</td>\n",
       "      <td>1.165081e+06</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>325000.0</td>\n",
       "      <td>724551.0</td>\n",
       "      <td>1752711.0</td>\n",
       "      <td>9579597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>28298.0</td>\n",
       "      <td>1.293969e+06</td>\n",
       "      <td>1.176382e+06</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>324423.0</td>\n",
       "      <td>756106.0</td>\n",
       "      <td>1952016.0</td>\n",
       "      <td>9548116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>28630.0</td>\n",
       "      <td>1.153685e+06</td>\n",
       "      <td>1.096390e+06</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>272099.0</td>\n",
       "      <td>648022.0</td>\n",
       "      <td>1752711.0</td>\n",
       "      <td>9579597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>29359.0</td>\n",
       "      <td>1.140280e+06</td>\n",
       "      <td>1.078814e+06</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>648022.0</td>\n",
       "      <td>1752711.0</td>\n",
       "      <td>7763213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>31843.0</td>\n",
       "      <td>1.051055e+06</td>\n",
       "      <td>9.722971e+05</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>637001.0</td>\n",
       "      <td>1709906.0</td>\n",
       "      <td>9548116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>103227.0</td>\n",
       "      <td>9.371178e+05</td>\n",
       "      <td>8.149423e+05</td>\n",
       "      <td>106000.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>1476987.0</td>\n",
       "      <td>7763213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>142565.0</td>\n",
       "      <td>9.508760e+05</td>\n",
       "      <td>8.139162e+05</td>\n",
       "      <td>107030.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>1476987.0</td>\n",
       "      <td>7763213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>148186.0</td>\n",
       "      <td>7.047720e+05</td>\n",
       "      <td>6.524904e+05</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>712000.0</td>\n",
       "      <td>9548116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>410235.0</td>\n",
       "      <td>9.809565e+05</td>\n",
       "      <td>1.027561e+06</td>\n",
       "      <td>100293.0</td>\n",
       "      <td>418212.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>1113568.0</td>\n",
       "      <td>9548116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>447156.0</td>\n",
       "      <td>8.503470e+05</td>\n",
       "      <td>9.177216e+05</td>\n",
       "      <td>100670.0</td>\n",
       "      <td>324423.0</td>\n",
       "      <td>486635.0</td>\n",
       "      <td>939475.0</td>\n",
       "      <td>9633001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count          mean           std       min  \\\n",
       "action_date_fiscal_year                                                   \n",
       "2010                      26359.0  1.263430e+06  1.165081e+06  106000.0   \n",
       "2011                      28298.0  1.293969e+06  1.176382e+06  106000.0   \n",
       "2012                      28630.0  1.153685e+06  1.096390e+06  106000.0   \n",
       "2013                      29359.0  1.140280e+06  1.078814e+06  110000.0   \n",
       "2014                      31843.0  1.051055e+06  9.722971e+05  106000.0   \n",
       "2015                     103227.0  9.371178e+05  8.149423e+05  106000.0   \n",
       "2016                     142565.0  9.508760e+05  8.139162e+05  107030.0   \n",
       "2017                     148186.0  7.047720e+05  6.524904e+05  110000.0   \n",
       "2018                     410235.0  9.809565e+05  1.027561e+06  100293.0   \n",
       "2019                     447156.0  8.503470e+05  9.177216e+05  100670.0   \n",
       "\n",
       "                              25%       50%        75%        max  \n",
       "action_date_fiscal_year                                            \n",
       "2010                     325000.0  724551.0  1752711.0  9579597.0  \n",
       "2011                     324423.0  756106.0  1952016.0  9548116.0  \n",
       "2012                     272099.0  648022.0  1752711.0  9579597.0  \n",
       "2013                     273000.0  648022.0  1752711.0  7763213.0  \n",
       "2014                     320000.0  637001.0  1709906.0  9548116.0  \n",
       "2015                     486635.0  486635.0  1476987.0  7763213.0  \n",
       "2016                     486635.0  486635.0  1476987.0  7763213.0  \n",
       "2017                     486635.0  486635.0   712000.0  9548116.0  \n",
       "2018                     418212.0  486635.0  1113568.0  9548116.0  \n",
       "2019                     324423.0  486635.0   939475.0  9633001.0  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector = (df['highly_compensated_officer_1_amount'] > 1e5) & (df['highly_compensated_officer_1_amount'] < 10e6) & (df['recipient_parent_name'].isin(recipient_parent_names_with_PSC_Cat_D_R_obligations))\n",
    "df_study_subset = df[selector]\n",
    "df_study_subset.groupby(['action_date_fiscal_year'])['highly_compensated_officer_1_amount'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ff8ffc50>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_study_subset.groupby(['action_date_fiscal_year'])['highly_compensated_officer_1_amount'].describe()['count'].plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(447156, 24)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_subset_GFY19 = df_study_subset[df_study_subset['action_date_fiscal_year'] == '2019']\n",
    "df_study_subset_GFY19.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1410,)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_subset_GFY19['recipient_parent_name'].unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our subset has 1410 parent firms of the 2154 total firms that reported executive 1 compensation in GFY19.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Our subset has {df_study_subset_GFY19['recipient_parent_name'].unique().shape[0]} parent firms of the {df_exec_1_mode.shape[0]} total firms that reported executive 1 compensation in GFY19.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ed13fa50>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note addition of , showfliers=False to remove outliers - https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.boxplot.html\n",
    "df_study_subset_GFY19.boxplot(column = 'highly_compensated_officer_1_amount', by = 'action_date_fiscal_year', showfliers=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "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>federal_action_obligation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.410000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.771901e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.340230e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.546343e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.955113e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.981077e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.050977e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.971788e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       federal_action_obligation\n",
       "count               1.410000e+03\n",
       "mean                9.771901e+07\n",
       "std                 6.340230e+08\n",
       "min                -5.546343e+08\n",
       "25%                 1.955113e+06\n",
       "50%                 1.981077e+07\n",
       "75%                 5.050977e+07\n",
       "max                 1.971788e+10"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_subset_GFY19.groupby(['recipient_parent_name'])['federal_action_obligation'].sum().reset_index().describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# df_study_subset_GFY19_recipient_obs = df_study_subset_GFY19.groupby(['recipient_parent_name'])['federal_action_obligation'].sum().reset_index().sort_values(by='federal_action_obligation', ascending=False)\n",
    "# df_study_subset_GFY19_recipient_obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>index</th>\n",
       "      <th>recipient_parent_name</th>\n",
       "      <th>federal_action_obligation</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Cnpnaedart Oesylrricmo Agnio</td>\n",
       "      <td>1.971788e+10</td>\n",
       "      <td>12710.0</td>\n",
       "      <td>2.629377e+06</td>\n",
       "      <td>8.360443e+05</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>1927593.00</td>\n",
       "      <td>2741945.0</td>\n",
       "      <td>3181559.0</td>\n",
       "      <td>5316668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Huncanmi .a</td>\n",
       "      <td>6.796949e+09</td>\n",
       "      <td>285.0</td>\n",
       "      <td>4.194422e+05</td>\n",
       "      <td>8.257792e+04</td>\n",
       "      <td>361021.0</td>\n",
       "      <td>361021.00</td>\n",
       "      <td>361021.0</td>\n",
       "      <td>458379.0</td>\n",
       "      <td>567725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Semasepslt C By</td>\n",
       "      <td>6.090681e+09</td>\n",
       "      <td>7393.0</td>\n",
       "      <td>3.109920e+06</td>\n",
       "      <td>4.622841e+05</td>\n",
       "      <td>205659.0</td>\n",
       "      <td>2986014.00</td>\n",
       "      <td>2986014.0</td>\n",
       "      <td>3647075.0</td>\n",
       "      <td>4087027.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ninlwlhno Oree.ttna Cynieial</td>\n",
       "      <td>3.729625e+09</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.620716e+06</td>\n",
       "      <td>7.109492e+04</td>\n",
       "      <td>1475000.0</td>\n",
       "      <td>1643717.00</td>\n",
       "      <td>1643717.0</td>\n",
       "      <td>1643717.0</td>\n",
       "      <td>1766554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Otgeicieiea Engtrcronlpona Carlso Oohmt</td>\n",
       "      <td>3.458976e+09</td>\n",
       "      <td>832.0</td>\n",
       "      <td>2.106536e+06</td>\n",
       "      <td>3.751620e+05</td>\n",
       "      <td>196863.0</td>\n",
       "      <td>2273957.00</td>\n",
       "      <td>2306758.0</td>\n",
       "      <td>2306758.0</td>\n",
       "      <td>2306758.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Brpo Euhcc.eln Tg I</td>\n",
       "      <td>3.219545e+09</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.452079e+06</td>\n",
       "      <td>7.833689e+05</td>\n",
       "      <td>474620.0</td>\n",
       "      <td>753632.00</td>\n",
       "      <td>2151675.0</td>\n",
       "      <td>2151675.0</td>\n",
       "      <td>2976650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Nnlagp .tcpcy S Uidiivlntia</td>\n",
       "      <td>3.206749e+09</td>\n",
       "      <td>47587.0</td>\n",
       "      <td>2.067218e+06</td>\n",
       "      <td>5.154583e+05</td>\n",
       "      <td>1476987.0</td>\n",
       "      <td>1752711.00</td>\n",
       "      <td>1752711.0</td>\n",
       "      <td>2811112.0</td>\n",
       "      <td>2811112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Cp Al.cwoeartearhetiancslitr Hle</td>\n",
       "      <td>2.869991e+09</td>\n",
       "      <td>69.0</td>\n",
       "      <td>6.340032e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6340032.0</td>\n",
       "      <td>6340032.00</td>\n",
       "      <td>6340032.0</td>\n",
       "      <td>6340032.0</td>\n",
       "      <td>6340032.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Crnkib .</td>\n",
       "      <td>2.550350e+09</td>\n",
       "      <td>1899.0</td>\n",
       "      <td>1.847404e+06</td>\n",
       "      <td>7.426255e+05</td>\n",
       "      <td>351590.0</td>\n",
       "      <td>1688065.00</td>\n",
       "      <td>1695749.0</td>\n",
       "      <td>1695749.0</td>\n",
       "      <td>3771119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Intacoreeonenrtopc</td>\n",
       "      <td>2.309316e+09</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.286329e+06</td>\n",
       "      <td>1.395931e+06</td>\n",
       "      <td>960489.0</td>\n",
       "      <td>4962844.00</td>\n",
       "      <td>4962844.0</td>\n",
       "      <td>4962844.0</td>\n",
       "      <td>4962844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Cnereclyll Uaic Tsrvnomoer Lw Iaeieatlnr</td>\n",
       "      <td>2.122479e+09</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.117466e+05</td>\n",
       "      <td>2.854866e+04</td>\n",
       "      <td>670069.0</td>\n",
       "      <td>670069.00</td>\n",
       "      <td>730691.0</td>\n",
       "      <td>730691.0</td>\n",
       "      <td>730691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>-alt Ttllue Ebcl</td>\n",
       "      <td>1.983382e+09</td>\n",
       "      <td>53.0</td>\n",
       "      <td>9.895084e+05</td>\n",
       "      <td>2.167329e+05</td>\n",
       "      <td>873367.0</td>\n",
       "      <td>873367.00</td>\n",
       "      <td>873367.0</td>\n",
       "      <td>873367.0</td>\n",
       "      <td>1386325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Aceccutnnier.</td>\n",
       "      <td>1.792626e+09</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>2.901408e+06</td>\n",
       "      <td>7.593687e+05</td>\n",
       "      <td>182070.0</td>\n",
       "      <td>2748074.00</td>\n",
       "      <td>2748074.0</td>\n",
       "      <td>3575005.0</td>\n",
       "      <td>3575005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Haaoncnreti Meinlncicsto Ni</td>\n",
       "      <td>1.497845e+09</td>\n",
       "      <td>257.0</td>\n",
       "      <td>4.907567e+05</td>\n",
       "      <td>7.520956e+03</td>\n",
       "      <td>479692.0</td>\n",
       "      <td>479692.00</td>\n",
       "      <td>495849.0</td>\n",
       "      <td>495849.0</td>\n",
       "      <td>495849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>N Rev Sanroieiodrrpcaatoa</td>\n",
       "      <td>1.483600e+09</td>\n",
       "      <td>964.0</td>\n",
       "      <td>9.163021e+05</td>\n",
       "      <td>1.875638e+05</td>\n",
       "      <td>487000.0</td>\n",
       "      <td>1000000.00</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>1009023.0</td>\n",
       "      <td>1009223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>L U Lsuatalsac</td>\n",
       "      <td>1.413032e+09</td>\n",
       "      <td>285.0</td>\n",
       "      <td>7.002311e+05</td>\n",
       "      <td>4.173496e+04</td>\n",
       "      <td>695934.0</td>\n",
       "      <td>695934.00</td>\n",
       "      <td>695934.0</td>\n",
       "      <td>695934.0</td>\n",
       "      <td>1104156.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Eshnuo Sri Ysjponvtkniih</td>\n",
       "      <td>1.236978e+09</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>1.604946e+06</td>\n",
       "      <td>2.604099e+05</td>\n",
       "      <td>972979.0</td>\n",
       "      <td>1678389.00</td>\n",
       "      <td>1739894.0</td>\n",
       "      <td>1739894.0</td>\n",
       "      <td>1739894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Msefaaipcaicn Nc</td>\n",
       "      <td>1.116291e+09</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>1.916938e+06</td>\n",
       "      <td>6.702901e+05</td>\n",
       "      <td>763862.0</td>\n",
       "      <td>1709950.00</td>\n",
       "      <td>1912538.0</td>\n",
       "      <td>1912538.0</td>\n",
       "      <td>4076014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>O Aareoprnshicotr Tposn</td>\n",
       "      <td>1.060051e+09</td>\n",
       "      <td>998.0</td>\n",
       "      <td>9.078359e+05</td>\n",
       "      <td>2.548302e+05</td>\n",
       "      <td>294560.0</td>\n",
       "      <td>658000.00</td>\n",
       "      <td>1020475.0</td>\n",
       "      <td>1020475.0</td>\n",
       "      <td>1291486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Nverirrlseuu Oaalnsvnlsi Cnaatsh L O</td>\n",
       "      <td>9.474382e+08</td>\n",
       "      <td>57.0</td>\n",
       "      <td>7.553853e+05</td>\n",
       "      <td>3.542939e+04</td>\n",
       "      <td>492592.0</td>\n",
       "      <td>760078.00</td>\n",
       "      <td>760078.0</td>\n",
       "      <td>760078.0</td>\n",
       "      <td>760078.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Prv.o Ps.puts R1m Csi Eel</td>\n",
       "      <td>9.045873e+08</td>\n",
       "      <td>183.0</td>\n",
       "      <td>2.487432e+05</td>\n",
       "      <td>1.301118e+04</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>240000.00</td>\n",
       "      <td>248000.0</td>\n",
       "      <td>248000.0</td>\n",
       "      <td>280000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Di Idghonealc Nlin</td>\n",
       "      <td>8.832092e+08</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>2.165082e+06</td>\n",
       "      <td>7.764453e+05</td>\n",
       "      <td>1698067.0</td>\n",
       "      <td>1724619.00</td>\n",
       "      <td>1724619.0</td>\n",
       "      <td>1724619.0</td>\n",
       "      <td>3511765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Nongad Rcoypo Ti Ltolrseennacnao Iihcoce</td>\n",
       "      <td>8.780075e+08</td>\n",
       "      <td>907.0</td>\n",
       "      <td>6.691426e+05</td>\n",
       "      <td>7.789480e+04</td>\n",
       "      <td>650338.0</td>\n",
       "      <td>650338.00</td>\n",
       "      <td>650338.0</td>\n",
       "      <td>650338.0</td>\n",
       "      <td>991453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Ilootig Hdacs Olt Esni Cceognsnnui.</td>\n",
       "      <td>8.653055e+08</td>\n",
       "      <td>878.0</td>\n",
       "      <td>7.718851e+05</td>\n",
       "      <td>3.022409e+05</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>500000.00</td>\n",
       "      <td>500000.0</td>\n",
       "      <td>1113568.0</td>\n",
       "      <td>1113568.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Tnc.eiapc Pesr</td>\n",
       "      <td>8.648470e+08</td>\n",
       "      <td>469.0</td>\n",
       "      <td>2.023336e+05</td>\n",
       "      <td>1.822094e+05</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>161000.00</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>161000.0</td>\n",
       "      <td>1003847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Ymt S Eoisnhsigosisne Vle T.oncic</td>\n",
       "      <td>8.395517e+08</td>\n",
       "      <td>62.0</td>\n",
       "      <td>2.439731e+05</td>\n",
       "      <td>7.809028e+04</td>\n",
       "      <td>160095.0</td>\n",
       "      <td>250000.00</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>250000.0</td>\n",
       "      <td>545000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Hwginsccn Lrm &amp;sensoer I.da Ofbda</td>\n",
       "      <td>8.088969e+08</td>\n",
       "      <td>1162.0</td>\n",
       "      <td>1.308823e+06</td>\n",
       "      <td>1.337593e+06</td>\n",
       "      <td>1036500.0</td>\n",
       "      <td>1036500.00</td>\n",
       "      <td>1036500.0</td>\n",
       "      <td>1061663.0</td>\n",
       "      <td>8048149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Crceu Spoplg Ro</td>\n",
       "      <td>7.367867e+08</td>\n",
       "      <td>921.0</td>\n",
       "      <td>1.008050e+06</td>\n",
       "      <td>8.493901e+04</td>\n",
       "      <td>966435.0</td>\n",
       "      <td>991453.00</td>\n",
       "      <td>991453.0</td>\n",
       "      <td>991453.0</td>\n",
       "      <td>1324481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>Oarrtoplfiurnco O</td>\n",
       "      <td>7.115264e+08</td>\n",
       "      <td>135.0</td>\n",
       "      <td>1.221771e+06</td>\n",
       "      <td>1.104122e+06</td>\n",
       "      <td>299876.0</td>\n",
       "      <td>681099.00</td>\n",
       "      <td>998298.0</td>\n",
       "      <td>998298.0</td>\n",
       "      <td>4052849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Iciansc.</td>\n",
       "      <td>6.599270e+08</td>\n",
       "      <td>89.0</td>\n",
       "      <td>7.422730e+05</td>\n",
       "      <td>2.479146e+05</td>\n",
       "      <td>601012.0</td>\n",
       "      <td>601012.00</td>\n",
       "      <td>601012.0</td>\n",
       "      <td>601012.0</td>\n",
       "      <td>1172477.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>1380</td>\n",
       "      <td>Et Ro Uoyocnoft</td>\n",
       "      <td>-1.996137e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.369050e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136905.0</td>\n",
       "      <td>136905.00</td>\n",
       "      <td>136905.0</td>\n",
       "      <td>136905.0</td>\n",
       "      <td>136905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1381</th>\n",
       "      <td>1381</td>\n",
       "      <td>Oe Rruar Spn O Slgtedcacnneohonisimurtcu</td>\n",
       "      <td>-2.283993e+05</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.725000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>172500.0</td>\n",
       "      <td>172500.00</td>\n",
       "      <td>172500.0</td>\n",
       "      <td>172500.0</td>\n",
       "      <td>172500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>1382</td>\n",
       "      <td>Cao Ros Enaufp Ansec Ds&amp;ewpr Ee.grte I</td>\n",
       "      <td>-2.393217e+05</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.465769e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1465769.0</td>\n",
       "      <td>1465769.00</td>\n",
       "      <td>1465769.0</td>\n",
       "      <td>1465769.0</td>\n",
       "      <td>1465769.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1383</th>\n",
       "      <td>1383</td>\n",
       "      <td>.goshegrnvilia Ld Cyeichnchtesn O</td>\n",
       "      <td>-2.502538e+05</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.200000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>220000.00</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>220000.0</td>\n",
       "      <td>220000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384</th>\n",
       "      <td>1384</td>\n",
       "      <td>Ein Rtettr Eidnsuscsim.</td>\n",
       "      <td>-2.959346e+05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.881250e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>388125.0</td>\n",
       "      <td>388125.00</td>\n",
       "      <td>388125.0</td>\n",
       "      <td>388125.0</td>\n",
       "      <td>388125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1385</th>\n",
       "      <td>1385</td>\n",
       "      <td>Tn Asyokisace. Sai Ac</td>\n",
       "      <td>-3.040635e+05</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.521520e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>152152.0</td>\n",
       "      <td>152152.00</td>\n",
       "      <td>152152.0</td>\n",
       "      <td>152152.0</td>\n",
       "      <td>152152.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>1386</td>\n",
       "      <td>S.nclhin Sesiesbam Nlu Iac</td>\n",
       "      <td>-3.307249e+05</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.500000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>150000.00</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>150000.0</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1387</th>\n",
       "      <td>1387</td>\n",
       "      <td>M Laadtlbeaopoci</td>\n",
       "      <td>-3.372966e+05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.471676e+06</td>\n",
       "      <td>5.078465e+05</td>\n",
       "      <td>1709906.0</td>\n",
       "      <td>2471675.75</td>\n",
       "      <td>2725599.0</td>\n",
       "      <td>2725599.0</td>\n",
       "      <td>2725599.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>1388</td>\n",
       "      <td>Eitusnorepgrcad Ropoar</td>\n",
       "      <td>-4.901457e+05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.200000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>320000.00</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>320000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>1389</td>\n",
       "      <td>Le C Clpchnslorgse Ueiraacuog</td>\n",
       "      <td>-5.131001e+05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.480000e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>348000.0</td>\n",
       "      <td>348000.00</td>\n",
       "      <td>348000.0</td>\n",
       "      <td>348000.0</td>\n",
       "      <td>348000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1390</th>\n",
       "      <td>1390</td>\n",
       "      <td>Mnjcasyoe Ero Psit Aio.t Rhnc &amp;ss</td>\n",
       "      <td>-5.841982e+05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.985010e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>198501.0</td>\n",
       "      <td>198501.00</td>\n",
       "      <td>198501.0</td>\n",
       "      <td>198501.0</td>\n",
       "      <td>198501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1391</th>\n",
       "      <td>1391</td>\n",
       "      <td>Jscc Iileoe.m Tn</td>\n",
       "      <td>-6.444268e+05</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.188080e+05</td>\n",
       "      <td>6.012110e+04</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>267318.0</td>\n",
       "      <td>296424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1392</th>\n",
       "      <td>1392</td>\n",
       "      <td>Psi Oroco Vegce Pe Lalpendefr.nsuanimtrnmroef Op</td>\n",
       "      <td>-6.667395e+05</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.973056e+05</td>\n",
       "      <td>3.460139e+03</td>\n",
       "      <td>194363.0</td>\n",
       "      <td>194363.00</td>\n",
       "      <td>194363.0</td>\n",
       "      <td>201229.0</td>\n",
       "      <td>201229.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1393</th>\n",
       "      <td>1393</td>\n",
       "      <td>Srjnci .</td>\n",
       "      <td>-7.169918e+05</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.872000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>187200.0</td>\n",
       "      <td>187200.00</td>\n",
       "      <td>187200.0</td>\n",
       "      <td>187200.0</td>\n",
       "      <td>187200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>1394</td>\n",
       "      <td>&amp;m Gutdzimri P Ynorehn Mnac. Ae</td>\n",
       "      <td>-8.238876e+05</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2.368518e+05</td>\n",
       "      <td>1.831224e+04</td>\n",
       "      <td>228800.0</td>\n",
       "      <td>228800.00</td>\n",
       "      <td>228800.0</td>\n",
       "      <td>228800.0</td>\n",
       "      <td>277111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1395</th>\n",
       "      <td>1395</td>\n",
       "      <td>Ccntavopie .</td>\n",
       "      <td>-8.314529e+05</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.499201e+05</td>\n",
       "      <td>1.553159e+04</td>\n",
       "      <td>125632.0</td>\n",
       "      <td>144534.00</td>\n",
       "      <td>144534.0</td>\n",
       "      <td>164904.0</td>\n",
       "      <td>164904.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>1396</td>\n",
       "      <td>Corines.s Cvesriec</td>\n",
       "      <td>-8.396416e+05</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.866877e+05</td>\n",
       "      <td>1.006659e+04</td>\n",
       "      <td>966435.0</td>\n",
       "      <td>991453.00</td>\n",
       "      <td>991453.0</td>\n",
       "      <td>991453.0</td>\n",
       "      <td>991453.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>1397</td>\n",
       "      <td>Evtasisilliisn Lo &amp; Lncasiooutm</td>\n",
       "      <td>-1.058932e+06</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.812587e+05</td>\n",
       "      <td>1.203033e+05</td>\n",
       "      <td>200000.0</td>\n",
       "      <td>617569.00</td>\n",
       "      <td>617569.0</td>\n",
       "      <td>617569.0</td>\n",
       "      <td>617569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1398</th>\n",
       "      <td>1398</td>\n",
       "      <td>Ebvu Lroo Pl Tlcscsrpilgsea</td>\n",
       "      <td>-1.382919e+06</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.671590e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>167159.0</td>\n",
       "      <td>167159.00</td>\n",
       "      <td>167159.0</td>\n",
       "      <td>167159.0</td>\n",
       "      <td>167159.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1399</th>\n",
       "      <td>1399</td>\n",
       "      <td>Cerssrcocieap N Faprl-lcaint</td>\n",
       "      <td>-1.422085e+06</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.980000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>198000.00</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>198000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1400</th>\n",
       "      <td>1400</td>\n",
       "      <td>Ct Lrnee Sniaaeoo&amp;c G.ck Nsies</td>\n",
       "      <td>-1.695312e+06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.380890e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>338089.0</td>\n",
       "      <td>338089.00</td>\n",
       "      <td>338089.0</td>\n",
       "      <td>338089.0</td>\n",
       "      <td>338089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>1401</td>\n",
       "      <td>Reyeepvnu R I Sc.trimctcsiesc Su</td>\n",
       "      <td>-2.103080e+06</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.750000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>175000.00</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>175000.0</td>\n",
       "      <td>175000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1402</th>\n",
       "      <td>1402</td>\n",
       "      <td>Eescsm Iglalc Cteiseyvolenr Vtpnr</td>\n",
       "      <td>-2.262690e+06</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.587050e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>158705.0</td>\n",
       "      <td>158705.00</td>\n",
       "      <td>158705.0</td>\n",
       "      <td>158705.0</td>\n",
       "      <td>158705.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1403</th>\n",
       "      <td>1403</td>\n",
       "      <td>Hlrce Dpwo Psblgupoarout</td>\n",
       "      <td>-2.495159e+06</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.146480e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>114648.0</td>\n",
       "      <td>114648.00</td>\n",
       "      <td>114648.0</td>\n",
       "      <td>114648.0</td>\n",
       "      <td>114648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1404</th>\n",
       "      <td>1404</td>\n",
       "      <td>Aetrirfhienns S Ce Tpi</td>\n",
       "      <td>-2.628104e+06</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.117740e+05</td>\n",
       "      <td>3.807898e+04</td>\n",
       "      <td>392584.0</td>\n",
       "      <td>392584.00</td>\n",
       "      <td>392584.0</td>\n",
       "      <td>392584.0</td>\n",
       "      <td>478939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1405</th>\n",
       "      <td>1405</td>\n",
       "      <td>Tlennrgcrnt.ieispa</td>\n",
       "      <td>-3.569302e+06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.815679e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1815679.0</td>\n",
       "      <td>1815679.00</td>\n",
       "      <td>1815679.0</td>\n",
       "      <td>1815679.0</td>\n",
       "      <td>1815679.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406</th>\n",
       "      <td>1406</td>\n",
       "      <td>Vnpnrnscoria Itl Entoiaaatritonora</td>\n",
       "      <td>-4.030587e+06</td>\n",
       "      <td>54.0</td>\n",
       "      <td>4.312580e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>431258.0</td>\n",
       "      <td>431258.00</td>\n",
       "      <td>431258.0</td>\n",
       "      <td>431258.0</td>\n",
       "      <td>431258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1407</th>\n",
       "      <td>1407</td>\n",
       "      <td>Anciaeawvokltr Meritenmn Nr</td>\n",
       "      <td>-8.964559e+06</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2.390500e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>239050.0</td>\n",
       "      <td>239050.00</td>\n",
       "      <td>239050.0</td>\n",
       "      <td>239050.0</td>\n",
       "      <td>239050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>1408</td>\n",
       "      <td>Efeepdrmro Aoiopntccmm</td>\n",
       "      <td>-1.580711e+07</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.418245e+05</td>\n",
       "      <td>3.307947e+05</td>\n",
       "      <td>202141.0</td>\n",
       "      <td>202141.00</td>\n",
       "      <td>202141.0</td>\n",
       "      <td>886951.0</td>\n",
       "      <td>886951.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1409</th>\n",
       "      <td>1409</td>\n",
       "      <td>Ic T Lsa Rasaicoyeuno Taslolnmll</td>\n",
       "      <td>-5.546343e+08</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.962180e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>696218.0</td>\n",
       "      <td>696218.00</td>\n",
       "      <td>696218.0</td>\n",
       "      <td>696218.0</td>\n",
       "      <td>696218.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1410 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                             recipient_parent_name  \\\n",
       "0         0                      Cnpnaedart Oesylrricmo Agnio   \n",
       "1         1                                       Huncanmi .a   \n",
       "2         2                                   Semasepslt C By   \n",
       "3         3                      Ninlwlhno Oree.ttna Cynieial   \n",
       "4         4           Otgeicieiea Engtrcronlpona Carlso Oohmt   \n",
       "5         5                               Brpo Euhcc.eln Tg I   \n",
       "6         6                       Nnlagp .tcpcy S Uidiivlntia   \n",
       "7         7                  Cp Al.cwoeartearhetiancslitr Hle   \n",
       "8         8                                          Crnkib .   \n",
       "9         9                                Intacoreeonenrtopc   \n",
       "10       10          Cnereclyll Uaic Tsrvnomoer Lw Iaeieatlnr   \n",
       "11       11                                  -alt Ttllue Ebcl   \n",
       "12       12                                     Aceccutnnier.   \n",
       "13       13                       Haaoncnreti Meinlncicsto Ni   \n",
       "14       14                         N Rev Sanroieiodrrpcaatoa   \n",
       "15       15                                    L U Lsuatalsac   \n",
       "16       16                          Eshnuo Sri Ysjponvtkniih   \n",
       "17       17                                  Msefaaipcaicn Nc   \n",
       "18       18                           O Aareoprnshicotr Tposn   \n",
       "19       19              Nverirrlseuu Oaalnsvnlsi Cnaatsh L O   \n",
       "20       20                         Prv.o Ps.puts R1m Csi Eel   \n",
       "21       21                                Di Idghonealc Nlin   \n",
       "22       22          Nongad Rcoypo Ti Ltolrseennacnao Iihcoce   \n",
       "23       23               Ilootig Hdacs Olt Esni Cceognsnnui.   \n",
       "24       24                                    Tnc.eiapc Pesr   \n",
       "25       25                 Ymt S Eoisnhsigosisne Vle T.oncic   \n",
       "26       26                 Hwginsccn Lrm &sensoer I.da Ofbda   \n",
       "27       27                                   Crceu Spoplg Ro   \n",
       "28       28                                 Oarrtoplfiurnco O   \n",
       "29       29                                          Iciansc.   \n",
       "...     ...                                               ...   \n",
       "1380   1380                                   Et Ro Uoyocnoft   \n",
       "1381   1381          Oe Rruar Spn O Slgtedcacnneohonisimurtcu   \n",
       "1382   1382            Cao Ros Enaufp Ansec Ds&ewpr Ee.grte I   \n",
       "1383   1383                 .goshegrnvilia Ld Cyeichnchtesn O   \n",
       "1384   1384                           Ein Rtettr Eidnsuscsim.   \n",
       "1385   1385                             Tn Asyokisace. Sai Ac   \n",
       "1386   1386                        S.nclhin Sesiesbam Nlu Iac   \n",
       "1387   1387                                  M Laadtlbeaopoci   \n",
       "1388   1388                            Eitusnorepgrcad Ropoar   \n",
       "1389   1389                     Le C Clpchnslorgse Ueiraacuog   \n",
       "1390   1390                 Mnjcasyoe Ero Psit Aio.t Rhnc &ss   \n",
       "1391   1391                                  Jscc Iileoe.m Tn   \n",
       "1392   1392  Psi Oroco Vegce Pe Lalpendefr.nsuanimtrnmroef Op   \n",
       "1393   1393                                          Srjnci .   \n",
       "1394   1394                   &m Gutdzimri P Ynorehn Mnac. Ae   \n",
       "1395   1395                                      Ccntavopie .   \n",
       "1396   1396                                Corines.s Cvesriec   \n",
       "1397   1397                   Evtasisilliisn Lo & Lncasiooutm   \n",
       "1398   1398                       Ebvu Lroo Pl Tlcscsrpilgsea   \n",
       "1399   1399                      Cerssrcocieap N Faprl-lcaint   \n",
       "1400   1400                    Ct Lrnee Sniaaeoo&c G.ck Nsies   \n",
       "1401   1401                  Reyeepvnu R I Sc.trimctcsiesc Su   \n",
       "1402   1402                 Eescsm Iglalc Cteiseyvolenr Vtpnr   \n",
       "1403   1403                          Hlrce Dpwo Psblgupoarout   \n",
       "1404   1404                            Aetrirfhienns S Ce Tpi   \n",
       "1405   1405                                Tlennrgcrnt.ieispa   \n",
       "1406   1406                Vnpnrnscoria Itl Entoiaaatritonora   \n",
       "1407   1407                       Anciaeawvokltr Meritenmn Nr   \n",
       "1408   1408                            Efeepdrmro Aoiopntccmm   \n",
       "1409   1409                  Ic T Lsa Rasaicoyeuno Taslolnmll   \n",
       "\n",
       "      federal_action_obligation    count          mean           std  \\\n",
       "0                  1.971788e+10  12710.0  2.629377e+06  8.360443e+05   \n",
       "1                  6.796949e+09    285.0  4.194422e+05  8.257792e+04   \n",
       "2                  6.090681e+09   7393.0  3.109920e+06  4.622841e+05   \n",
       "3                  3.729625e+09     24.0  1.620716e+06  7.109492e+04   \n",
       "4                  3.458976e+09    832.0  2.106536e+06  3.751620e+05   \n",
       "5                  3.219545e+09     69.0  1.452079e+06  7.833689e+05   \n",
       "6                  3.206749e+09  47587.0  2.067218e+06  5.154583e+05   \n",
       "7                  2.869991e+09     69.0  6.340032e+06  0.000000e+00   \n",
       "8                  2.550350e+09   1899.0  1.847404e+06  7.426255e+05   \n",
       "9                  2.309316e+09     85.0  4.286329e+06  1.395931e+06   \n",
       "10                 2.122479e+09     32.0  7.117466e+05  2.854866e+04   \n",
       "11                 1.983382e+09     53.0  9.895084e+05  2.167329e+05   \n",
       "12                 1.792626e+09   1698.0  2.901408e+06  7.593687e+05   \n",
       "13                 1.497845e+09    257.0  4.907567e+05  7.520956e+03   \n",
       "14                 1.483600e+09    964.0  9.163021e+05  1.875638e+05   \n",
       "15                 1.413032e+09    285.0  7.002311e+05  4.173496e+04   \n",
       "16                 1.236978e+09   1811.0  1.604946e+06  2.604099e+05   \n",
       "17                 1.116291e+09   2100.0  1.916938e+06  6.702901e+05   \n",
       "18                 1.060051e+09    998.0  9.078359e+05  2.548302e+05   \n",
       "19                 9.474382e+08     57.0  7.553853e+05  3.542939e+04   \n",
       "20                 9.045873e+08    183.0  2.487432e+05  1.301118e+04   \n",
       "21                 8.832092e+08   1953.0  2.165082e+06  7.764453e+05   \n",
       "22                 8.780075e+08    907.0  6.691426e+05  7.789480e+04   \n",
       "23                 8.653055e+08    878.0  7.718851e+05  3.022409e+05   \n",
       "24                 8.648470e+08    469.0  2.023336e+05  1.822094e+05   \n",
       "25                 8.395517e+08     62.0  2.439731e+05  7.809028e+04   \n",
       "26                 8.088969e+08   1162.0  1.308823e+06  1.337593e+06   \n",
       "27                 7.367867e+08    921.0  1.008050e+06  8.493901e+04   \n",
       "28                 7.115264e+08    135.0  1.221771e+06  1.104122e+06   \n",
       "29                 6.599270e+08     89.0  7.422730e+05  2.479146e+05   \n",
       "...                         ...      ...           ...           ...   \n",
       "1380              -1.996137e+05      1.0  1.369050e+05           NaN   \n",
       "1381              -2.283993e+05      3.0  1.725000e+05  0.000000e+00   \n",
       "1382              -2.393217e+05     21.0  1.465769e+06  0.000000e+00   \n",
       "1383              -2.502538e+05      9.0  2.200000e+05  0.000000e+00   \n",
       "1384              -2.959346e+05      5.0  3.881250e+05  0.000000e+00   \n",
       "1385              -3.040635e+05     18.0  1.521520e+05  0.000000e+00   \n",
       "1386              -3.307249e+05     44.0  1.500000e+05  0.000000e+00   \n",
       "1387              -3.372966e+05      4.0  2.471676e+06  5.078465e+05   \n",
       "1388              -4.901457e+05      2.0  3.200000e+05  0.000000e+00   \n",
       "1389              -5.131001e+05      1.0  3.480000e+05           NaN   \n",
       "1390              -5.841982e+05      6.0  1.985010e+05  0.000000e+00   \n",
       "1391              -6.444268e+05      6.0  2.188080e+05  6.012110e+04   \n",
       "1392              -6.667395e+05     28.0  1.973056e+05  3.460139e+03   \n",
       "1393              -7.169918e+05     10.0  1.872000e+05  0.000000e+00   \n",
       "1394              -8.238876e+05     30.0  2.368518e+05  1.831224e+04   \n",
       "1395              -8.314529e+05     20.0  1.499201e+05  1.553159e+04   \n",
       "1396              -8.396416e+05     21.0  9.866877e+05  1.006659e+04   \n",
       "1397              -1.058932e+06     23.0  5.812587e+05  1.203033e+05   \n",
       "1398              -1.382919e+06      2.0  1.671590e+05  0.000000e+00   \n",
       "1399              -1.422085e+06      6.0  1.980000e+05  0.000000e+00   \n",
       "1400              -1.695312e+06      4.0  3.380890e+05  0.000000e+00   \n",
       "1401              -2.103080e+06      8.0  1.750000e+05  0.000000e+00   \n",
       "1402              -2.262690e+06     46.0  1.587050e+05  0.000000e+00   \n",
       "1403              -2.495159e+06      8.0  1.146480e+05  0.000000e+00   \n",
       "1404              -2.628104e+06      9.0  4.117740e+05  3.807898e+04   \n",
       "1405              -3.569302e+06      3.0  1.815679e+06  0.000000e+00   \n",
       "1406              -4.030587e+06     54.0  4.312580e+05  0.000000e+00   \n",
       "1407              -8.964559e+06     20.0  2.390500e+05  0.000000e+00   \n",
       "1408              -1.580711e+07     40.0  4.418245e+05  3.307947e+05   \n",
       "1409              -5.546343e+08     11.0  6.962180e+05  0.000000e+00   \n",
       "\n",
       "            min         25%        50%        75%        max  \n",
       "0      275000.0  1927593.00  2741945.0  3181559.0  5316668.0  \n",
       "1      361021.0   361021.00   361021.0   458379.0   567725.0  \n",
       "2      205659.0  2986014.00  2986014.0  3647075.0  4087027.0  \n",
       "3     1475000.0  1643717.00  1643717.0  1643717.0  1766554.0  \n",
       "4      196863.0  2273957.00  2306758.0  2306758.0  2306758.0  \n",
       "5      474620.0   753632.00  2151675.0  2151675.0  2976650.0  \n",
       "6     1476987.0  1752711.00  1752711.0  2811112.0  2811112.0  \n",
       "7     6340032.0  6340032.00  6340032.0  6340032.0  6340032.0  \n",
       "8      351590.0  1688065.00  1695749.0  1695749.0  3771119.0  \n",
       "9      960489.0  4962844.00  4962844.0  4962844.0  4962844.0  \n",
       "10     670069.0   670069.00   730691.0   730691.0   730691.0  \n",
       "11     873367.0   873367.00   873367.0   873367.0  1386325.0  \n",
       "12     182070.0  2748074.00  2748074.0  3575005.0  3575005.0  \n",
       "13     479692.0   479692.00   495849.0   495849.0   495849.0  \n",
       "14     487000.0  1000000.00  1000000.0  1009023.0  1009223.0  \n",
       "15     695934.0   695934.00   695934.0   695934.0  1104156.0  \n",
       "16     972979.0  1678389.00  1739894.0  1739894.0  1739894.0  \n",
       "17     763862.0  1709950.00  1912538.0  1912538.0  4076014.0  \n",
       "18     294560.0   658000.00  1020475.0  1020475.0  1291486.0  \n",
       "19     492592.0   760078.00   760078.0   760078.0   760078.0  \n",
       "20     240000.0   240000.00   248000.0   248000.0   280000.0  \n",
       "21    1698067.0  1724619.00  1724619.0  1724619.0  3511765.0  \n",
       "22     650338.0   650338.00   650338.0   650338.0   991453.0  \n",
       "23     500000.0   500000.00   500000.0  1113568.0  1113568.0  \n",
       "24     161000.0   161000.00   161000.0   161000.0  1003847.0  \n",
       "25     160095.0   250000.00   250000.0   250000.0   545000.0  \n",
       "26    1036500.0  1036500.00  1036500.0  1061663.0  8048149.0  \n",
       "27     966435.0   991453.00   991453.0   991453.0  1324481.0  \n",
       "28     299876.0   681099.00   998298.0   998298.0  4052849.0  \n",
       "29     601012.0   601012.00   601012.0   601012.0  1172477.0  \n",
       "...         ...         ...        ...        ...        ...  \n",
       "1380   136905.0   136905.00   136905.0   136905.0   136905.0  \n",
       "1381   172500.0   172500.00   172500.0   172500.0   172500.0  \n",
       "1382  1465769.0  1465769.00  1465769.0  1465769.0  1465769.0  \n",
       "1383   220000.0   220000.00   220000.0   220000.0   220000.0  \n",
       "1384   388125.0   388125.00   388125.0   388125.0   388125.0  \n",
       "1385   152152.0   152152.00   152152.0   152152.0   152152.0  \n",
       "1386   150000.0   150000.00   150000.0   150000.0   150000.0  \n",
       "1387  1709906.0  2471675.75  2725599.0  2725599.0  2725599.0  \n",
       "1388   320000.0   320000.00   320000.0   320000.0   320000.0  \n",
       "1389   348000.0   348000.00   348000.0   348000.0   348000.0  \n",
       "1390   198501.0   198501.00   198501.0   198501.0   198501.0  \n",
       "1391   180000.0   180000.00   180000.0   267318.0   296424.0  \n",
       "1392   194363.0   194363.00   194363.0   201229.0   201229.0  \n",
       "1393   187200.0   187200.00   187200.0   187200.0   187200.0  \n",
       "1394   228800.0   228800.00   228800.0   228800.0   277111.0  \n",
       "1395   125632.0   144534.00   144534.0   164904.0   164904.0  \n",
       "1396   966435.0   991453.00   991453.0   991453.0   991453.0  \n",
       "1397   200000.0   617569.00   617569.0   617569.0   617569.0  \n",
       "1398   167159.0   167159.00   167159.0   167159.0   167159.0  \n",
       "1399   198000.0   198000.00   198000.0   198000.0   198000.0  \n",
       "1400   338089.0   338089.00   338089.0   338089.0   338089.0  \n",
       "1401   175000.0   175000.00   175000.0   175000.0   175000.0  \n",
       "1402   158705.0   158705.00   158705.0   158705.0   158705.0  \n",
       "1403   114648.0   114648.00   114648.0   114648.0   114648.0  \n",
       "1404   392584.0   392584.00   392584.0   392584.0   478939.0  \n",
       "1405  1815679.0  1815679.00  1815679.0  1815679.0  1815679.0  \n",
       "1406   431258.0   431258.00   431258.0   431258.0   431258.0  \n",
       "1407   239050.0   239050.00   239050.0   239050.0   239050.0  \n",
       "1408   202141.0   202141.00   202141.0   886951.0   886951.0  \n",
       "1409   696218.0   696218.00   696218.0   696218.0   696218.0  \n",
       "\n",
       "[1410 rows x 11 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_subset_GFY19_exec1 = df_study_subset_GFY19.groupby(['recipient_parent_name'])['highly_compensated_officer_1_amount'].describe().sort_values(by='mean', ascending=False).reset_index()\n",
    "#Scramble_Company_Exec_Names(df_study_subset_GFY19_exec1)\n",
    "\n",
    "df_study_subset_GFY19_compensation_obs = df_study_subset_GFY19_recipient_obs.merge(df_study_subset_GFY19_exec1, on='recipient_parent_name').reset_index()\n",
    "Scramble_Company_Exec_Names(df_study_subset_GFY19_compensation_obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_study_subset_GFY19_compensation_obs.plot.scatter(x='federal_action_obligation', y='mean', grid=True)\n",
    "plt.ylabel(\"Annual Compensation\")\n",
    "plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selector = (df_study_subset_GFY19_compensation_obs['federal_action_obligation'] > 25e6) & (df_study_subset_GFY19_compensation_obs['federal_action_obligation'] < 2e9)\n",
    "df_study_subset_GFY19_compensation_obs[selector].plot.scatter(x='federal_action_obligation', y='mean', grid=True, rot=45)\n",
    "plt.ylabel(\"Annual Compensation\")\n",
    "plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selector = (df_study_subset_GFY19_compensation_obs['federal_action_obligation'] > 25e6) & (df_study_subset_GFY19_compensation_obs['federal_action_obligation'] < 150e6)\n",
    "df_study_subset_GFY19_compensation_obs[selector].plot.scatter(x='federal_action_obligation', y='mean', grid=True, rot=45)\n",
    "plt.ylabel(\"Annual Compensation\")\n",
    "plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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narWaRUREsM2bNxueU1JSwmbOnMk8PT2ZXC5nvr6+7L333jPMT01NZREREUylUrGAgAC2ZcuWJh3mO3fuZEFBQUyhUDQ7VLehbdu2MQBNOpPrbdiwgQ0aNIgplUrm7OzMwsPD2aefftrq+9XccHE0M3iisdraWrZy5Uo2ZMgQplKpmIODAxs8eDCLjY01GgK9adMmFhQUxORyOfPy8mILFixoMlS38WcaFRXVZCj2fffdx6ZMmWJ4XD9Ud9q0aczBwYG5uLiw119/nWm12lZzdyccY+LfkZebm4v3338fH3zwASoqKjB37lysX7++xeXfeOMNPPfccwgMDDRjSkKIWPj6+mL69OlYtGiRpaNYLdH3eTSmUqng7u5u2F/KGENmZqZhflZWFsrLy5vt+COEECKM6Ps8Vq5ciZSUFJSWluLFF1/E448/jtmzZ+O///0vtm7dCq1Wi5EjR8LX1xcA8Mcff2DEiBFd+sR3hBBial1itxUhhBDzMkvLo60D+RhjiIuLQ3JyMpRKJWbMmNEpQ+0IIYSYhln6PEaPHm0Y5tec5ORk5OTkYPXq1Xj++eexYcMGc8QihBDSQWZpefTv37/Jof8NHTt2DBEREeA4Dv369UN5eTmKiooEneK64UFSYqPRaJCfn2/pGB0i5uyAuPOLOTsg7vxizg7cyt8Z13KxitFWhYWFRgcvqdXqJhdBIoQQYj2sYrRVc332LY2Gio+PR3x8PAD9kc8tHTErBjKZTLT5xZwdEHd+MWcHxJ1fzNmBzs1vFcVDrVYbNQULCgpa3GU1duxYjB071vC4KzQhxUjM2QFx5xdzdkDc+cWcHeiCu63CwsJw4MABMMaQlpYGlUolmkt6EkJId2SWlkdzB/JptVoAQHR0NEJCQnDixAnMnj0bCoUCM2bMMEcsQgghHWSW4tHWxeo5jsP06dPNEYUQQkgnsIrdVoQQQsSFigchhJB2E33xYHV9J4QQQsxH9MUDhS0fuU4IIcQ0xF88qiotnYAQQrod8RcPOqM8IYSYHRUPQggh7UbFgxBCSLuJv3jwvKUTEEJItyP+4kEtD0IIMTsqHoQQQtqtCxQP2m1FCCHm1gWKh6UDEEJI99MFige1PAghxNy6QPGgpgchhJhbFyge1PIghBBzE3/x4KnlQQgh5ib+4kG7rQghxOzEXzxouBUhhJid+IsH7bYihBCzE3/xoA5zQggxuy5QPKjlQQgh5tYFige1PAghxNy6QPGglgchhJgbFQ9CCCHtJvriwah4EEKI2Ym+eNCVBAkhxPzEXzyo5UEIIWZHxYMQQki7dYHiQbutCCHE3LpA8bB0AEII6X66QPGglgchhJhbFyge1PQghBBzk5lrQydPnkRcXBx4nkdUVBQefvhho/kVFRVYvXo1CgoKoNPpMGHCBIwZM6btFVPLgxBCzM4sxYPneWzcuBGLFi2CWq3G/PnzERYWhl69ehmW2bVrF3r16oU333wTJSUlmDNnDu6++27IZG1EpFOyE0KI2Zllt1V6ejo8PT3h4eEBmUyGESNGICkpyWgZjuNQVVUFxhiqqqpgb28PiURAPNptRQghZmeWlkdhYSHUarXhsVqtxoULF4yWGTduHFasWIEXXngBlZWVeOWVV5otHvHx8YiPjwcALF++HPZ2dlBpNKZ9ASYik8mgoewWIeb8Ys4OiDu/mLMDnZtfUPEoKyvDL7/8gsuXL6Oqqspo3tKlS9t8fnPnn+I4zujxqVOn4OPjg7fffhs3btzAu+++i6CgIKhUKqPlxo4di7Fjx97KVlqKivx8IS/D6mg0GuRTdosQc34xZwfEnV/M2YFb+b28vG57XYKKx6pVq6DVajF8+HAoFIp2b0StVqOgoMDwuKCgAC4uLkbL7Nu3Dw8//DA4joOnpyfc3d2RlZWFgICA1ldOHeaEEGJ2gopHWloaNmzYALlc3qGN+Pv7Izs7G7m5uXB1dcXhw4cxe/Zso2U0Gg1Onz6N4OBg3Lx5E1lZWXB3d2975dTnQQghZieoeHh7e6OgoACenp4d2ohUKkVMTAxiY2PB8zzGjBmD3r17Y8+ePQCA6OhoTJw4EZ9++ilee+01AMCUKVPg6OjY9sqp5UEIIWYnqHjccccdWLZsGUaPHg1nZ2ejeffcc4+gDYWGhiI0NNRoWnR0tOG+q6srFi1aJGhdRqjlQQghZieoeJw/fx5qtRqnT59uMk9o8TAZKh6EEGJ2gorH4sWLTZ2j46h4EEKI2Qk+zqOsrAzHjx9HYWEhXF1dMWTIENjb25symzB0JUFCCDE7QUeYp6WlYdasWfj9999x+fJlxMfHY9asWUhLSzN1vrZRy4MQQsxOUMtj06ZNmD59OkaOHGmYdvjwYcTFxeG9994zWThBqHgQQojZCWp5ZGdnY/jw4UbT7rrrLuTk5JgkVLvQUF1CCDE7QcXD09MThw8fNpp25MgReHh4mCRUu1DDgxBCzE7Qbqtp06Zh+fLl2LlzJzQaDfLy8pCdnY0333zT1PnaRi0PQggxO0HFIzAwEGvWrMGJEydQVFSEIUOGIDQ01DpGW1GfByGEmJ3gobr29vaIiIgwZZaOoZYHIYSYXYvFIzY2FgsXLgQAvP32201OoV5PyCnZTYquJEgIIWbXYvGIjIw03Lf4KUhaQ7utCCHE7FosHqNGjTLc79mzJ/r27dtkmfT0dNOkahcqHoQQYm6Chur+61//anZ6bGxsp4bpENptRQghZtdqhzlfd94oxpjhVu/GjRuQSqWmTScEdZgTQojZtVo8nnzyScP9J554wmieRCLBI488YppU7UF9HoQQYnatFo+PP/4YjDEsWbLEaFQVx3FwdHTs0PXMOx21PAghxOxaLR5ubm4AgE8//dQsYTqEWh6EEGJ2gg8SPHbsGFJSUlBSUmI0febMmZ0eql2oeBBCiNkJGm31448/Yv369eB5HomJibC3t8epU6egUqlMna9tVDwIIcTsBLU89u3bh0WLFsHb2xv79+/HtGnTMGrUKGzZssXU+dpGxYMQQsxOUMujvLwc3t7eAACZTAatVouAgACkpKSYNJwgdBlaQggxO0EtD09PT1y9ehW9e/dG7969sWfPHtjb29NZdQkhpJsSVDwmT56M0tJSAMCUKVOwatUqVFVVYfr06SYNJwgN1SWEELMTVDxCQ0MN9wMCArBmzRqTBWo3angQQojZCSoe165dg729PZydnVFVVYVffvkFEokEEyZMgFKpNHXG1lHLgxBCzE5Qh/mqVatQUVEBAPjyyy9x7tw5pKWlYf369SYNJwj1eRBCiNkJannk5eXBy8sLjDEkJSXhgw8+gEKhsPwBggAVD0IIsQBBxUMul6OyshLXrl2DWq2Go6MjdDodamtrTZ2vTYzXWToCIYR0O4KKx8iRI/HOO++gsrIS48aNAwBcunQJ7u7uJg0niBUUMEII6W4EFY9p06bh1KlTkEqluOOOOwDoz6z7zDPPmDScILU1lk5ACCHdjuATIw4aNMjosb+/f6eH6RBqeRBCiNkJKh65ubn47rvvkJmZiaqqKqN5a9euFbShkydPIi4uDjzPIyoqCg8//HCTZc6ePYtNmzZBp9PBwcHB6BoiLaKWByGEmJ2g4rFq1Sp4eHjg6aef7tBxHTzPY+PGjVi0aBHUajXmz5+PsLAw9OrVy7BMeXk5NmzYgIULF0Kj0aC4uFjYyql4EEKI2Qk+SPDdd9+FRCLosJAm0tPT4enpCQ8PDwDAiBEjkJSUZFQ8/vjjDwwbNgwajQYA4OTkJGzlVDwIIcTsBBWP4OBgZGZmws/Pr0MbKSwshFqtNjxWq9W4cOGC0TLZ2dnQarVYsmQJKisrMX78eERGRjZZV3x8POLj4wEAy5cvh0SnNRQcsZHJZJTdQsScX8zZAXHnF3N2oHPzCyoebm5uiI2NRXh4OJydnY3mTZ48uc3ns2YO5OM4zuixTqfDpUuX8NZbb6GmpgaLFi1C37594eXlZbTc2LFjMXbsWMNjvqYG+fn5Ql6G1dFoNJTdQsScX8zZAXHnF3N24Fb+xt+rHSGoeFRXV2PIkCHQ6XQoKCho90bUarXR8woKCuDi4tJkGQcHB9jY2MDGxgbBwcG4fPly2y9SS6OtCCHE3AQVjxkzZtzWRvz9/ZGdnY3c3Fy4urri8OHDmD17ttEyYWFh+Pzzz6HT6aDVapGeno4HHnig7ZVrtbeVjRBCSPsJPs7j2rVrSExMRHFxMZ577jlkZWWhtrYWPj4+bT5XKpUiJiYGsbGx4HkeY8aMMVxUCgCio6PRq1cvDB48GK+//jokEgnuuecew9ULW6XTgjHWZDcYIYQQ0xFUPI4cOYINGzZg2LBhOHToEJ577jlUVlbi22+/xVtvvSVoQ6GhoUbXBQH0RaOhhx56CA899JDA6HUYA3Q6QCa4DhJCCLlNgr5xN2/ejLfeegu+vr44cuQIAMDHxweZmZmmzCacTkvFgxBCzEjQgRvFxcVNdk9xHGc9u4qo34MQQsxKUPHw8/PDgQMHjKYdOnQIAQEBJgnVbjTiihBCzErQvp5nn30W//rXv7B3715UV1cjNjYWWVlZWLRokanzCUMtD0IIMStBxaNnz55YuXIljh8/jiFDhkCtVmPIkCGwsbExdT5hdNTyIIQQcxLcy6xUKhEUFITCwkK4urpaT+EAgFpqeRBCiDkJKh75+flYvXo1Lly4ADs7O5SXlyMgIACzZ8+Gm5ubqTO2jVoehBBiVoI6zD/55BP4+fkhLi4OGzZsQFxcHPz9/fHJJ5+YOp8w1OdBCCFmJah4ZGRkYOrUqYZdVTY2Npg6dSoyMjJMGk4wGm1FCCFmJah49O3bF+np6UbTLl68iH79+pkkVLtRy4MQQsxKUJ+Hh4cH3nvvPYSGhhrOkJucnIxRo0bhhx9+MCwn5PTsJkEtD0IIMStBxaO2thbDhg0DAJSUlEAulyM8PBw1NTUdOkV7p6OWByGEmJVZTsluakynhZWcKIUQQroFwcd5VFdXIycnB1VVVUbTAwMDOz1Uu9XSbitCCDEnQcUjISEBn3/+OWQyGRQKhdG8tWvXmiRYu+hotxUhhJiToOLx9ddf47XXXsPAgQNNnadjqM+DEELMStBQXZlMhv79+5s6S8fRaCtCCDErQcVj8uTJ+PLLL1FSUmLqPB1DLQ9CCDErQbutvLy8sHnzZuzevbvJvIbHeVgMnduKEELMSlDxWLNmDSIiIjBixIgmHeYWx0norLqEEGJmgopHWVkZJk+ebD2XnW1ILqOWByGEmJmgPo/Ro0c3uQyt1ZDKqc+DEELMTFDLIz09Hbt27cLWrVvh7OxsNG/p0qUmCSaYTEajrQghxMwEFY+oqChERUWZOkvHyOVATY2lUxBCSLciqHiMHj3axDFug8oerKLM0ikIIaRbEXxuq3379uHAgQOGa5hHRERgzJgxpswmjJ0DUE7FgxBCzElQ8di6dSsSEhIwYcIEaDQa5Ofn45dffkFRUREeffRRU2dsnZ0DkH3VshkIIaSbEVQ8/ve//2HJkiVwc3MzTBs0aBAWL15s8eLB2TuAlZdaNAMhhHQ3gobqVldXw9HR0Wiag4MDaqyho9rOHigvBWPM0kkIIaTbEFQ8Bg8ejNWrVyMrKws1NTW4fv06Pv74YwwaNMjU+dpm5wjodEBhnqWTEEJItyFot1VMTAw+//xzzJs3D1qtFjKZDHfddRdiYmJMna9NnH8gGAC27zdwk6ZZOg4hhHQLgoqHSqXCzJkzMWPGDJSWlsLBwQESiaBGi8lxAf0BpS1A/R6EEGI2rVaAq1evYvv27bcWlkjg5OQEiUSC7du349q1ayYPKIizK1Bd1fZyhBBCOkWrxeOnn36CWq1udp6bmxt++uknwRs6efIk5syZg1mzZmHbtm0tLpeeno7JkycjMTFR8LqhVIJR8SCEELNptXikpaUhPDy82XlDhw5FamqqoI3wPI+NGzdiwYIF+Oijj3Do0KFmWy08z+Obb77B4MGDBa3XQGFDLQ9CCDGjVotHWVlZi30bHMehrEzYkd3p6enw9PSEh4cHZDIZRowYgaSkpCbL7dy5E8OGDWsyLLhNNlQ8CCHEnFrtMHd3d0daWlqz1y9PS0uDu/OOr4cAACAASURBVLu7oI0UFhYa7f5Sq9W4cOFCk2X+/PNPLF68GGvXrm1xXfHx8YiPjwcALF++HBqNBjft7KErLYZaoxGUx1rIZDJoRJa5npizA+LOL+bsgLjzizk70Ln5Wy0eUVFR+OyzzzB37lz4+fkZpmdkZGDdunUYN26coI00dwBf4wtLbdq0CVOmTGlzFNfYsWMxduxYw+P8/HzwPAOrqkJ+fr6gPNai/lQvYiTm7IC484s5OyDu/GLODtzK7+XlddvrarV4jB8/Hjk5OViwYAHUajVcXFxQVFSEwsJCREdH4/777xe0EbVajYKCAsPjgoICuLi4GC1z8eJFrFq1CgBQUlKC5ORkSCSSFvtcjF8FXdODEELMqc3jPGJiYjB+/HicPn3acIzHnXfeCU9PT8Eb8ff3R3Z2NnJzc+Hq6orDhw9j9uzZRst88sknRveHDBkirHAAgExOxYMQQsxI0EGCnp6e7SoWjUmlUsTExCA2NhY8z2PMmDHo3bs39uzZAwCIjo7u8LoB6FsetVQ8CCHEXARfz+N2hYaGIjQ01GhaS0Xj5Zdfbt/KqeVBCCFmZR3nGLldVDwIIcSsuk7x4HkwXmfpJIQQ0i10jeKhUOj/ram2bA5CCOkmWuzzeOmllwStoLUD+syF03iAAUBuNuDtb+k4hBDS5bVYPGbNmmXOHLfHyxsAwLKugKPiQQghJtdi8WjulCRWy90LkMqAa5ctnYQQQroFwUN1MzMzce7cOZSWGl8vfPLkySYJ1h6cTAb08gW7nG7pKIQQ0i0IKh7x8fH44osvMHDgQJw8eRKDBw/GX3/9hbCwMFPnE4zr0w8scR8YY03Om0UIIaRzCRpttX37dixYsADz5s2DQqHAvHnz8Oqrr0IqlZo6n3BqN6Cqkk7NTgghZiCoeJSUlCA4OBiA/my4PM8jJCQEx48fN2m4drGvuwYIXcucEEJMTlDxcHV1RW5uLgCgR48eOHbsGM6dOweZzGxnN2kTZ+8AAGB7Wr7ELSGEkM4h6Nv/b3/7G65fvw53d3dMmjQJH374IbRaLZ599llT5xMuQD86jKWdtXAQQgjp+gQVj9GjRxvuh4SEIC4uDlqtFjY2NqbK1W6cvSO4+x4B+9+vYDodOGvqjyGEkC5G0G4rnueNbhKJBAqFAjzPmzpf+3h5A1otkJdt6SSEENKlCWp5PPnkky3O++GHHzotzO3iXDT605SU3AQ8e1k6DiGEdFmCisfHH39s9LioqAjbtm2zquM8ANwacVVWYtkchBDSxQnabeXm5mZ069evH2bOnInt27ebOl/71BUPVlJs4SCEENK1dfiU7BUVFSgpsbJf+E7O+tOz37hu6SSEENKlCdpttWbNGqNTflRXV+PcuXO4++67TRasIziJFPDtC3b+L0tHIYSQLk1Q8fD09DR6rFQqce+992LgwIEmCXU7uJDhYD9sALuUBq5PP0vHIYSQLklQ8XjsscdMnaPTcGEjwbZ8AX77N5DOXWrpOIQQ0iUJPr/IqVOnkJmZiaoq4xMPWsMp2RvinNXgRo0FO0Jn2CWEEFMRVDw2btyII0eOYMCAAVAqlabOdPv8goD9O8H27wQ3Zryl0xBCSJcjqHgcOnQIK1asgEajMXWeTsENiwT7fj1AF4cihBCTEDRU18HBAXZ2dqbO0mk4iQRwdQcrKrB0FEII6ZIEtTwefPBBrF69Go888gicnJyM5nl4eJgk2G1zcgbOJoNVVYKzsbV0GkII6VIEFY8NGzYAAE6cONFknjWd26oh7q7RYGeTwXb+BO6Rv1s6DiGEdCmCioe1FojWcOERYL/+ALZnG9j4x8GJoaOfEEJEosOnJ7F2nEQKyZQXAG0t2MlES8chhJAuRVDLIzc3F999912zx3msXbvWJME6RdAgwN4BOHcKGBZp6TSEENJlCCoeq1atgoeHB55++mlxHOdRh5NIgJ6+YFcuWjoKIYR0KYKKx7Vr1/Duu+9CIhHfXi7OoyfYgV3gv/kM3FMv0BHnhBDSCQQVj+DgYGRmZsLPz6/DGzp58iTi4uLA8zyioqLw8MMPG80/ePCg4fogNjY2mD59Onx9fTu8vXrcA4+B5eeA7f8N6NEL3D0P3vY6CSGkuxNUPNzc3BAbG4vw8HA4OzsbzRNybiue57Fx40YsWrQIarUa8+fPR1hYGHr1unWpWHd3dyxZsgT29vZITk7G+vXrsWzZsna+nKY4VzdIZr0N/p05YD9+DjZwKDiNlR6bQgghIiFoP1R1dTWGDBkCnU6HgoICo5sQ6enp8PT0hIeHB2QyGUaMGIGkpCSjZQIDA2Fvbw8A6Nu3r+B1C8HJZJC89Cag1YKlnum09RJCSHclqOUxY8aMZqfzPC9oI4WFhVCr1YbHarUaFy5caHH5vXv3IiQkpNl58fHxiI+PBwAsX75c8Pm2mLMzcqVSKFL/gvPfrONMwDKZTDTnC2tMzNkBcecXc3ZA3PnFnB3o3PyCT8ne0JUrV5CQkIA//vgD69ata3N5xliTaS11XJ85cwb79u3DO++80+z8sWPHYuzYsYbH+fn5AlMDuDMM1Uf2IfeHTZBEWb7vQ6PRtC+/FRFzdkDc+cWcHRB3fjFnB27l9/Lyuu11CS4eJSUl+OOPP5CQkIDMzEwEBQVh2rRpgp6rVquNdkMVFBTAxcWlyXKXL1/GunXrMH/+fDg4OAiNJpjk+Xngl8wG27wBzD8QnG/fTt8GIYR0B632eWi1WiQmJmL58uV44YUX8Pvvv2Po0KGws7PDq6++iuHDhwvaiL+/P7Kzs5GbmwutVovDhw8jLCzMaJn8/Hz85z//wcyZMzulKjaHkysg+ed7gFwBtmebSbZBCCHdQastj3/84x+QSCSIjIzE448/bhiqu2fPnnZtRCqVIiYmBrGxseB5HmPGjEHv3r0N64mOjsZPP/2EsrIyw0kYpVIpli9f3pHX1CrO0QXc3dFg+34DqygDp7Lv9G0QQkhX12rx8PHxwfnz55Geno4ePXrA3d3dMCKqvUJDQxEaGmo0LTo62nD/xRdfxIsvvtihdbcXFzYKLP4XsFNJ4IaPMcs2CSGkK2m1eCxZsgR5eXlISEjAr7/+iri4OAwcOBDV1dXQ6XTmytj5+vQDnFyAlGSAigchhLRbmx3mbm5umDRpEiZNmoTz588jISEBHMdh3rx5GDNmDKZOnWqOnJ2Kk0gAz15g1zItHYUQQkSpXUN1g4KCEBQUhGeffRZ//vknDhw4YKpcJsf59gXbvRXs9HFwdw6xdBxCCBGVDp3pUKFQYNSoUViwYEFn5zEbbsKTgEQCfvdWS0chhBDREd9pcjsJp1SCG/MAkHoafNIfYDXVlo5ECCGi0W2LBwBw4yYCDk5g61eAfyMG7FKapSMRQogodO/i4ewKyZI1kLzwBlBVSQcOEkKIQN26eAAA5+gMLmwUuOiHwY79Af7nr8AEnvCREEK6qw6dGLEr4v42Bci/Afbbj0BZCbi/v2zpSIQQYrWoeNThpFJg+qsAJwE7sBu8qxu48Y/RZWsJIaQZ3X63VUOcRApuyouAlzfYtq/Bf7oM7EpGs6eUJ4SQ7oyKRyOcyg6SN1eAGxYJnD4G/t25YF+sBiu5aelohBBiNah4NIOzVUEy/TVI/r0JXOQ4sEP/A//6M+D3/2bpaIQQYhWoeLSCc3ACN+UlSOYtAzQeYN98Bt0ny8AfTQAT84khCSHkNlGHeRs4jgP63QHJmyv058I69D+wk4lgcSuBgP7gAu8EFzQQXN/+lo5KCCFmQ8VDIM7RGdxjMWAP/x0s6SCQngJ2/i+wX74F++VbcOGR4O4aDXh4gXPvYem4hBBiUlQ82omTy8GNuAcYcQ8AgJUUgf9qLdifCWB/JugXcvcCNzgc3ENPgVPaWDAtIYSYBhWP28Q5ukAyYz5QchPIywa7fBHszAmwPdvADv8PXMT94O55AJyTi6WjEkJIp6Hi0Qk4jtNfmdDJBVxAfyBqAtjZZPA7fgD7bTPY/t/AjboXXOhwwC+QDjwkhIgeFQ8T4QaEQNJ/MHA9E/yWL8H+9yvYnp8BtTvQyxdcTx9UBd0B5qgGPHvqj3AnhBCRoOJhQhzHAb36QDpnMVhFGdjRBLDU00DWVbDTx1D824/6Be0dAW9/cPaOgJ29vtPdyxvw7AU4OIGT0cdECLEu9K1kJpzKXn/xqTEPAABYbS2cq8pQ9Ndx4GwyWF4OWF42UFqsPz18wyc7OgPuPcD5B4Pz6wf49gXn6maR10EIIQAVD4vh5HLIe/SFxMEFGDnWMJ0xBuRlAwV5YDnXgNISoCgfLOca2O/bbp0uXuOh71/x6AG4aMANCAEcXcBJ6LhPQojpUfGwMhzHAe5e+uG+wYOM5rGaauDqJbCMVLD0c2BnTwCJxfp5ACCVAg7O4PoPBrz9AUcn/a4wB0fAzpF2gRFCOg19k4gIp1AC/kHg/IOAe/8GAGDaWn0fyoWzQHGR/pokxw8Bh/+nn994JXYOgLOrvrXi4AjY2gEqO8BWBdjagVPZ6ftgbFWAXAHI5Pp/5QpAoX9Mo8UIIVQ8RI6TyQFvP3DefoZpjNcB5WVASTFQehMoLwUrKwWKC4GSm2DFRUBhPlj2VaCyHKisAOpOO9/myeclEsBGhTw7O/ByJaC00fe/2NgCShtAoQTsHfS70OwdABsVoFTqpyttDcvR7jVCxI2KRxfESaSAg5P+Bm/9tFaWZzwPVFfpC0lF+a1O+9oaoLYWqK0BtDVAdbV+uapKKMBQdbMQqKwAu5apn15TN1+n1a+35YD6lo2dPaCy17d8FEpw9S0cGxt9kbFR1f1rC67+vm3dv8q6+1SICLEIKh5E/+Vrq9LfGoziaq3gOGk0qM3Pb3Yeq6oAbhYBFWVAVQVQUw1WVWUoPPoiVQaUl4NVlOkfl5Xoi1VNtb5IVVUADc5c3GqLSFlXbBT6lpDhccOiI1fo5yv0/1a6qMFXV4NTKgG5Uj9fqWywi06p32UnkwNymb4gE0IMqHiQTsfZqABPlfG0dq6DMQZoa/W71Koq6276+6yyAqium1ZZ9291JVBdDVZdqS9AFeX6XXP1z62tBrRaw/pL6rcjNJBMpu8fqusbqi+2t4qTHJDK6gqOrO5Wd1+uL0ScvG6aVKZf3rBso/tSmX73oESib6VJJNS6IlaHigexShzH3WoFODobz+vgOhmvA2pqgNoauNrboTAnR19UaqoN05nhfrW+eNVq9f/WVBv6h1hlhf5+bjZYZbm+OGm1+t11rVzn5bYvZiyVApwEuTIZWH1Rqh/IUN9aqnvMNXxcX5iksluFTWpc5LiG8xsWQWmjQtjwuVIZIJXSAIpuiooH6TY4idSwO0uq0YDjmv753+7XION5fRHR1t1qa+r6jOqKkLa2wX39v8xoWq3++TxvfGO8vjDxPGyVClSWlNQVvpq63X11Ba+0WF8EG04zdWGrLyjyRoVFJmumAMlxU6UCr+ObzmtcqBoWM7kckCvByRuM/pMr9NMVCkDWYESgXE67Gc2AigchnYiTSABJ3ReZ0Oe0cxsOGg2qW+hvao2+sOkAXV2LSld7q8gZCl6t8X2tFqy5eY2W0U8zXh+rX6627nl1uxN1pTf1fWCN11H//PoDYZt7DUJfbP1uP4kE4KTGj+t3B0ql+qHrSqVxoZLK9EPWlTYNpksBqQzljk7gq6uNloVUqh/12LDQNd4NWb9bswsNeafiQUg3wdV/ccrlQDsuM9PZX3FqjQb5rRQ/xuv0RU6rNW6R1bfi6kcB1uhHAd5qZdXqRwXW1OiXb9xy43XG03RasPKyWy3D6qpbBbOivNlWW1lLmTvyRkhlgFRS18cl1Reo+n8b31fUDXdX2OiP92rYypPJ9YVOpT9mi7OxbdoSVCgBpRK8QqbvT+wEZiseJ0+eRFxcHHieR1RUFB5++GGj+YwxxMXFITk5GUqlEjNmzICfn18LayOEdFWcpO6LU2DrzRy/3xljgE4LtZMTCnJz61pJukYtsUa7Hw27JRu0wOqLW21NXVGqK2r193Xausd1xY3X3dr9WVWpP06rfjh8fYtOW1dI67O28jryAH2x+iXxtt8TsxQPnuexceNGLFq0CGq1GvPnz0dYWBh69eplWCY5ORk5OTlYvXo1Lly4gA0bNmDZsmXmiEcIIa3iOA6QySGxVYGzs2/fc02UqSGm0+lHI1Y0HMDRoJjVVINVV8MePMqyr3XKNs1SPNLT0+Hp6QkPDw8AwIgRI5CUlGRUPI4dO4aIiAhwHId+/fqhvLwcRUVFcHGhK/ARQkhruPr+GzuHlpcBoNJoUNGB/rLmmKV4FBYWQq1WGx6r1WpcuHChyTIajcZomcLCwibFIz4+HvHx8QCA5cuXGz1HbGQymWjzizk7IO78Ys4OiDu/mLMDnZvfLMWjuQ6axiMNhCwDAGPHjsXYsbdOYd5ax5u107TRcWjNxJwdEHd+MWcHxJ1fzNmBW/m9vLxue11mOWxVrVajoKDA8LigoKBJi0KtVht9KM0tQwghxDqYpXj4+/sjOzsbubm50Gq1OHz4MMLCwoyWCQsLw4EDB8AYQ1paGlQqFRUPQgixUmbZbSWVShETE4PY2FjwPI8xY8agd+/e2LNnDwAgOjoaISEhOHHiBGbPng2FQoEZM2aYIxohhJAOMNtxHqGhoQgNDTWaFh0dbbjPcRymT59urjiEEEJuA52qkxBCSLtR8SCEENJuHOusE50QQgjpNkTd8njzzTctHeG2iDm/mLMD4s4v5uyAuPOLOTvQuflFXTwIIYRYBhUPQggh7SZdsmTJEkuHuB1iP227mPOLOTsg7vxizg6IO7+YswOdl586zAkhhLQb7bYihBDSblQ8CCGEtBsVD0IIIe0muuKRlZWFtLQ0aLVa8Dxv6TiEkE4i9u5XMefvSHaznRixMxw9ehTfffcdXF1d4erqCj8/P4wePRoqlcrS0Trkr7/+QlZWFhhjuP/++8EYa/YCWNZIzNkBcecXc/aGMjIyUFBQAF9fXzg6OkKpVILneUgk4vhNK+b8nZFdNEN1tVottmzZgieffBITJ04Ez/O4du0a0tPT4efnB7lcbumI7XL+/Hl8+OGHCAkJwcGDB5GZmWm4hom1//GJOTsg7vxizt7QsWPHsH79ehQXFyM1NRXnzp2Dr68vVCoVeJ63+mIo5vydlV00xYPneSQkJMDFxQW+vr7w8vKCQqHA9evXUVBQAD8/P6v+wBpLTEyEr68vHnzwQYwaNQrp6enIzMyEvb290fXerZGYswPizi/m7A3t2LED0dHRmDRpEjQaDfLy8nDw4EEEBwfD1tbW0vHaJOb8nZVdND9VZDIZHnzwQRw9ehTnzp2DRCJBUFAQfH19cf78eUvHazdvb2+kpaUhKysLCoUCEydOhEKhwMGDBy0drU1izg6IO7+Ys9fjeR48z+P69esA9AetRUVFoWfPnvjll19QU1Nj4YStE3P+zswumpYHALi4uKCyshJ//fUXVCoV3N3d4e3tjV27dsHX1xfOzs6Wjtiq/Px8yOVy1NbWwsPDA5cuXYJWq4WzszPs7OwQGBiIrVu3QiaTwdfX19JxjYg5OyDu/GLO3pBWq4VOp4NMJoNGo8HWrVthZ2eH3r17w9bWFgqFAmfPnkX//v1hY2Nj6bhNiDm/KbKLqnhIpVJ4enqipKQE+/btQ3V1Na5cuYK//voL9913n9V9YA2dOHECn376Ka5cuYLExET069cPnp6eSExMRG1tLZRKJVxcXHDz5k3Y2dlZ1ZeAmLMD4s4v5uwNJSYm4scff8T+/fuhUqkQEBAAd3d37N27FxKJBN7e3lCr1di3bx/c3d3h4eFh6chGxJzfVNlFVTwAQKFQwNfXF2q1GomJiSgoKMDUqVPRo0cPS0drFmMMBQUF+PTTTxETE4Phw4ejuLgY//3vf3H//fejd+/eOHv2LOLj45GZmYn9+/fjkUcegaOjo6Wjizp7vcLCQnzyySeiy98V3vt6WVlZWL9+PaZMmQI3NzckJCSgoqICPj4+6NmzJzZv3ozS0lJcunQJp0+fxgMPPGBVIyjFnN+U2UVXPABAIpHA3d0d4eHhGDp0KFxdXS0dqUUcx8HGxgaXL1/GsGHD4OzsjKCgIADAunXrMH78eAwfPhyenp6QSCSYPHkyvLy8LJxaj+M42Nra4tKlS7jrrrtElR0AampqoFQqce3aNYSHh4sqf/3fzfXr1xEWFgYXFxfRZG8sJycHaWlpmDhxIry9veHm5oZTp06htrYWI0eORHBwMC5evIiKigqrfB1izm/K7KIsHvU4jrPqEVY5OTnIycmBQqHA0aNHUVpaavgC6NevH7RaLRITEzFo0CB4enrCz88P9vb2Fk6td/78eZw9exa+vr5ITExEXl4eBgwYAMD6swNAUlIS9uzZAx8fHxw/fhw3b95E//79AVh//rS0NJw5cwZlZWU4ffo0ysrKEBwcDMD6szdHrVbjzJkz0Gq18PLygpubGxwcHLB3717Y29sjKCgIAwcOxKBBg6yy31LM+Z2dnXH+/HnU1tZ2enZRFw9rdvz4caxfvx7nzp0z/PLdsmULampqDF8Erq6uSE1NRXh4uNUUQZ7nUV1djRUrVuDcuXNwcnJCdHQ0NmzYgKqqKsMXsDVmr5eSkoJNmzZh/Pjx8Pf3R9++fbFx40YwxtCvXz8A1pv/2LFj+OKLL1BdXY3s7GxERETgxx9/hFarNfzwsNbsDV24cAE3btxAcXExXF1dUVZWhoyMDCiVSri6usLd3R06nQ5HjhxBeHi41R2jkpGRgRs3bqCoqAhqtRolJSXIzMyEQqGw+vzJycm4cOECfHx8AOh33V69erXTs4vqCHOxSE1NxVdffYU5c+agT58+WLduHdLT0/Huu+9i4cKF4HkeI0eOxPnz53Hp0iWUl5dbzS9HiUQCGxsbREZGQiKR4M8//wRjDGvWrMHLL78MW1tbhISEIDU11eqy18vIyMA999yDwYMHIz8/H1VVVXjiiSewYcMGyOVy3HnnnUhLS7O6/KWlpdi9ezdmz54Nb29vfPzxx7C1tcWsWbOwYsUKyOVyDB482CqzN5ScnIy4uDgMGDAAxcXF0Gg0iImJwbZt25CUlISCggJERESA4zgoFAqrK4DHjx/HDz/8AG9vb1RXVyM8PByjR4/Gli1bcPz4cavOX1NTgz179uCvv/6CjY2NIfu2bdtw/Phx5OfnIzIyslOyU8vDBAoKCtCrVy+EhIQA0O9q2LNnD6KjozF06FAkJSXh4sWL+PPPPzFjxgy4ublZOHFT6enpKCgowMiRI3HgwAEUFBSA4ziUlpbi/PnzOHHihFVn53kegYGBWLZsGS5evAidTofa2lrcvHkT2dnZVvne63Q6HDhwAJ6ennB1dcW3336La9euoaysDE5OTsjLy8PVq1eRlJRkddnr8TyPzZs3IyoqChMnTkRISAi2b9+O1NRU/P3vf0dJSQlOnjyJnTt34vTp05g6dapV9VleunQJn3/+OV5++WVER0eD4zikpqZiyJAh8Pf3x82bN5GSkoLffvvNKvNLpVJUVVVBLpdj7969sLW1Rd++feHn54eSkhKcPXu207LTxaBMgOd5VFVVGQ73Lyoqwvvvv4/58+fDxcUFeXl5cHV1RXV1tdWMymgsJycHiYmJePjhh/Hrr7/i22+/xd/+9jc88cQTAICysjKr/NULAFeuXMEHH3yAPn36YNCgQRgzZgyysrKwf/9+9O3bF0OHDrXa/ImJifj5558hk8kQEhKCSZMm4dSpUzh58iRGjBiBvn37Wm32etu2bYOrqysiIiIM0xYtWoSAgABMmzYNgP4zcnJygpOTk4VSNi81NRWXL19GdHQ0AP3/g1WrVuGVV16Bu7u74TximZmZcHFxsar8Wq0WMpkMSUlJ0Gq18PDwwMqVKxEWFgaZTIbHH38cUqkUV69e7ZT33np21HUhEonEUBQYY7Czs4O9vT1cXFxw4MAB/Pzzz9DpdFZbOAD9kOisrCzEx8fj999/x6OPPorMzEzs2bMHAGBnZ2fhhC3z9vbG3//+d1y4cAG5ubkAAC8vLxQXF6OiogKA9ea/66678NZbbyEoKAh9+vQBAAwaNAi5ubkoKioCYJ3Zs7KyDPddXV2xfft25OfnG6a98cYbyM/Px5UrVwDoPyNr+uKtzx8YGIi77roLgP5HYH2BqC/W2dnZAABfX1+ryV+fXSbT90L4+Pjg6NGj8PPzw9ixY7Fz505UVFRAJpOB47hOe++peJiYVCqFjY0N1Go1vv32W+zYsQPjxo2DQqGwdLRWubq6Qq1WY8uWLXj66afx2GOP4cEHH0RoaCgAWNV+3uaEhITg8ccfx8GDB7F3717s3bsXly9fRmBgIADrzm9vb4877rgDR48exalTp3Ds2DHk5+cbDgC0tuzHjx/HG2+8gZUrVwIAIiIiMHToULz11luGAuLo6AiZTIbq6mpLRm1W4/yOjo6GM8zK5XLD6coPHDiAr776CmVlZZaMa6Q++6pVqwzTVCoVHB0dcfjwYezbtw+TJk3C4cOHcfjw4U7dNu22MjHGGHQ6HV555RVotVq8/fbbVntAY2P5+fkoKSmBn58fAIjmdNMNZWRkIDExEVqtFqNHj4a3t7elIwlSXl6OhIQEHD16FAqFAlOmTLHKo8erqqrwwQcfYNiwYUhNTUVtbS3mzp0LAPj+++9x/PhxREdHo7S0FAcPHsT8+fPh7u5u4dS3NM6v0+kwe/ZsAPq/d8YYVq1aBZVKhczMTMycORO9evWycGq91rJ/88032LFjB2bPno277roLKSkpcHV1haenZ6dtn4qHmezfvx/+/v7o3bu3paO0m1ivF9EVVFZWgjFm1bs4CwsLoVKpUFNTg//+97+QSqWGAvLnn3/i5s2byMjIwPjx462yeDfOL5fLDV/CALBirFlkHgAAAS5JREFUxQpkZ2dj3rx5VnUAINA0u0wmw5w5c8DzPHJycuDl5WWy/79UPMyEvoBJd1BaWop169ZBJpNh7ty5uHr1KmxsbKxyZFhz6vMrFArMnj0b2dnZ2LdvHyIiIqymxdGSxtkzMzMhk8lMlpuKByGkU5WUlODrr79GamoqeJ7HkiVLRHWtkYb5AWDp0qVWd+R4S+qzp6Wlged5LF682GTvvbh2YBNCrJ6joyO8vb1RUVGBefPmiapwAMb5X3vtNdEUDuBW9vLycrz++usmfe+peBBCOlVZWRmSk5OxaNEiq+zjaIuY85szO+22IoR0upqaGqsfjt4aMec3V3YqHoQQQtqNdlsRQghpNyoehBBC2o2KByGEkHaj4kEIIaTdqHgQQghpNyoehBBC2u3/ARAyEQmYCV7gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_study_subset_GFY19_exec1['mean'].plot(title=\"Distribution of Executive 1 Comp\", grid=True, rot=45)\n",
    "plt.ylabel(\"Annual Compensation\")\n",
    "plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n",
    "#axes = plt.axes()\n",
    "#axes#.get_xaxis().set_visible(False) #https://stackoverflow.com/questions/40705614/hide-axis-label-only-not-entire-axis-in-pandas-plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Executive_1 Compensation as a Fraction of Total Federal Obligations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>recipient_parent_name</th>\n",
       "      <th>federal_action_obligation</th>\n",
       "      <th>exec_1_comp_fraction_obligations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>Os Dnogpieilrars.rch</td>\n",
       "      <td>34389540.24</td>\n",
       "      <td>0.142885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>Rm. Ncninicnaln Tieao Oliptci</td>\n",
       "      <td>33848885.36</td>\n",
       "      <td>0.177005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>N S E Ddorpnsg.uaciievfe</td>\n",
       "      <td>25828211.82</td>\n",
       "      <td>0.107825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Nrc Ermocn Rcipenseeae Itreuofrprse</td>\n",
       "      <td>24633249.68</td>\n",
       "      <td>0.179766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>M.rs T Arehr Nmcntievoeeianen Sgcn</td>\n",
       "      <td>23788559.16</td>\n",
       "      <td>0.100902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>Prly Uoccbel Esscotlians</td>\n",
       "      <td>20416302.08</td>\n",
       "      <td>0.163350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>Laltlsnu Lntioal C Ois</td>\n",
       "      <td>12702238.00</td>\n",
       "      <td>0.250472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>Tilrecoerilstlrnspus Seh S Ocemcvae</td>\n",
       "      <td>11839973.32</td>\n",
       "      <td>0.111500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>Isluo Tolne Ss.nrtalci</td>\n",
       "      <td>10634615.93</td>\n",
       "      <td>0.256789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>Meit Nrrm E Ieros .coaal-lyrihnoncacr</td>\n",
       "      <td>10376478.84</td>\n",
       "      <td>0.118362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>Rmcgrtn Inici E En .aeincatad</td>\n",
       "      <td>10236832.01</td>\n",
       "      <td>0.182522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Icc Cenrveennt Snerero Vmg Irse.</td>\n",
       "      <td>9257209.80</td>\n",
       "      <td>1.040594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Tg Cc Tltrgeauonaeucoop Lhtalhlyhpl</td>\n",
       "      <td>8839249.80</td>\n",
       "      <td>0.389624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>Aosc Merctiodnsnruaieidt Io Olpcrn</td>\n",
       "      <td>7796269.79</td>\n",
       "      <td>0.227581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>Eenati Scrcd .i</td>\n",
       "      <td>7620940.94</td>\n",
       "      <td>0.117161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>Tiaa Di Rciy.oogcnvuipn Anm</td>\n",
       "      <td>6995512.89</td>\n",
       "      <td>0.175604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>L Iia Cl</td>\n",
       "      <td>6204593.58</td>\n",
       "      <td>0.124609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>950</th>\n",
       "      <td>C Alrscl</td>\n",
       "      <td>5690847.79</td>\n",
       "      <td>0.559066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>Cniiasse Cimngce</td>\n",
       "      <td>5389272.01</td>\n",
       "      <td>0.108929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>Mrtnucne.u Ic</td>\n",
       "      <td>4923382.79</td>\n",
       "      <td>0.147256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>Praxcllrsu Idostc P</td>\n",
       "      <td>4510000.00</td>\n",
       "      <td>0.170306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>Llfrelelitdeacn</td>\n",
       "      <td>4448684.00</td>\n",
       "      <td>0.764106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>Ccge.nsiv O</td>\n",
       "      <td>4335928.14</td>\n",
       "      <td>0.108949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>Y.inc Tfi Inip</td>\n",
       "      <td>4105115.10</td>\n",
       "      <td>0.317145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>Oioegoynntholel Te Lcnol Iuintsnnisvicantwrgoa</td>\n",
       "      <td>4050916.37</td>\n",
       "      <td>0.118492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>Cv Cineorasotun.sis El Eneoscnvrdi</td>\n",
       "      <td>3877827.24</td>\n",
       "      <td>0.377987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Rrricpecnooirn Saehacrt Ono</td>\n",
       "      <td>3666908.00</td>\n",
       "      <td>0.210116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>Noi. Sctvesccv Ineiisiaar</td>\n",
       "      <td>3487667.35</td>\n",
       "      <td>0.132041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>Lic. R Rmjaibanehc.ke</td>\n",
       "      <td>3369611.45</td>\n",
       "      <td>0.226038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1008</th>\n",
       "      <td>Potyecusrtrenirfdes Ni.ssom Le S</td>\n",
       "      <td>3300050.16</td>\n",
       "      <td>0.177426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>Nss Naccieemvadttiiisv Rire</td>\n",
       "      <td>25699.53</td>\n",
       "      <td>6.809463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>C Ndl Ronie T.l.aoamyafin.icr</td>\n",
       "      <td>25351.61</td>\n",
       "      <td>5.916784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1259</th>\n",
       "      <td>Npeioulosrdoel Nrtmtiop Cnccnetr Aa Ds Ic</td>\n",
       "      <td>22171.23</td>\n",
       "      <td>19.168986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1260</th>\n",
       "      <td>Iltc Aoiyontanlasvl1us</td>\n",
       "      <td>21772.20</td>\n",
       "      <td>10.078081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>Tndg Liincceetlo Rcofselech</td>\n",
       "      <td>21600.00</td>\n",
       "      <td>4.857315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>Elirle Roico Fnnslct Rosutefieuuf Ng</td>\n",
       "      <td>17719.60</td>\n",
       "      <td>7.054335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>.airc Cae Tnccne</td>\n",
       "      <td>17682.85</td>\n",
       "      <td>51.044218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>Lv U.ucdatehrl T In</td>\n",
       "      <td>17300.00</td>\n",
       "      <td>14.450867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>Tl Otvoip,lapclni</td>\n",
       "      <td>16901.58</td>\n",
       "      <td>8.874910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1266</th>\n",
       "      <td>Ln'olao Elsr Teimrvrcnn</td>\n",
       "      <td>15637.31</td>\n",
       "      <td>7.034458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1267</th>\n",
       "      <td>Coeecasntl Ievr .kesmsilc</td>\n",
       "      <td>14479.73</td>\n",
       "      <td>60.168940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1268</th>\n",
       "      <td>Rcae Onotd S Ptr Ri</td>\n",
       "      <td>14476.02</td>\n",
       "      <td>7.252270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>Svih Nopica Adrct.</td>\n",
       "      <td>13500.00</td>\n",
       "      <td>12.088889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>Tyrkuierob</td>\n",
       "      <td>9974.00</td>\n",
       "      <td>14.171947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1271</th>\n",
       "      <td>Esc Hincr Vnteitee.r</td>\n",
       "      <td>6420.54</td>\n",
       "      <td>111.642167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>Ttsoirkislekm Iecl.c</td>\n",
       "      <td>6042.00</td>\n",
       "      <td>49.652433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>Qr C.cin</td>\n",
       "      <td>5287.00</td>\n",
       "      <td>49.593342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>O Ts Sosvheneceseant Griit Mlocloysiy&amp; Nu.bs Ces</td>\n",
       "      <td>5000.00</td>\n",
       "      <td>716.155400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>Pssmehcad</td>\n",
       "      <td>4377.97</td>\n",
       "      <td>37.688700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1276</th>\n",
       "      <td>.onvia. Meecn Irrei G Gnnnci</td>\n",
       "      <td>4258.86</td>\n",
       "      <td>112.706217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1277</th>\n",
       "      <td>Gral E.onc Vei Nonchesit</td>\n",
       "      <td>4100.00</td>\n",
       "      <td>80.531350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>Nosim.lhn'ac</td>\n",
       "      <td>3716.82</td>\n",
       "      <td>245.698204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>Lfsts Liuobli Iaste Iaibuyntriisnliddct</td>\n",
       "      <td>2705.51</td>\n",
       "      <td>45.093162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>Pgr Nedljenl Eiliacttgh Ec Crieanonnc</td>\n",
       "      <td>2600.64</td>\n",
       "      <td>124.846192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>Ntvgocrrapiaeeeond</td>\n",
       "      <td>500.00</td>\n",
       "      <td>603.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>Mo Bl Gtrtupd</td>\n",
       "      <td>500.00</td>\n",
       "      <td>546.066000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>Rcl Se Nrsniiugoonpaaeadonictitootces Tcr</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1007.482000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>N Xtessmeci Dtyaasne.gn</td>\n",
       "      <td>500.00</td>\n",
       "      <td>1597.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>Nnpncsbroaotelaitroau Urrnilpioit</td>\n",
       "      <td>500.00</td>\n",
       "      <td>595.836833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>Teosisndr Inopc Taeyesttdmrr</td>\n",
       "      <td>347.00</td>\n",
       "      <td>1288.184438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 recipient_parent_name  \\\n",
       "518                               Os Dnogpieilrars.rch   \n",
       "523                      Rm. Ncninicnaln Tieao Oliptci   \n",
       "610                           N S E Ddorpnsg.uaciievfe   \n",
       "632                Nrc Ermocn Rcipenseeae Itreuofrprse   \n",
       "646                 M.rs T Arehr Nmcntievoeeianen Sgcn   \n",
       "698                           Prly Uoccbel Esscotlians   \n",
       "801                             Laltlsnu Lntioal C Ois   \n",
       "815                Tilrecoerilstlrnspus Seh S Ocemcvae   \n",
       "832                             Isluo Tolne Ss.nrtalci   \n",
       "836              Meit Nrrm E Ieros .coaal-lyrihnoncacr   \n",
       "840                      Rmcgrtn Inici E En .aeincatad   \n",
       "867                   Icc Cenrveennt Snerero Vmg Irse.   \n",
       "878                Tg Cc Tltrgeauonaeucoop Lhtalhlyhpl   \n",
       "899                 Aosc Merctiodnsnruaieidt Io Olpcrn   \n",
       "905                                    Eenati Scrcd .i   \n",
       "920                        Tiaa Di Rciy.oogcnvuipn Anm   \n",
       "936                                           L Iia Cl   \n",
       "950                                           C Alrscl   \n",
       "957                                   Cniiasse Cimngce   \n",
       "962                                      Mrtnucne.u Ic   \n",
       "970                                Praxcllrsu Idostc P   \n",
       "973                                    Llfrelelitdeacn   \n",
       "981                                        Ccge.nsiv O   \n",
       "988                                     Y.inc Tfi Inip   \n",
       "990     Oioegoynntholel Te Lcnol Iuintsnnisvicantwrgoa   \n",
       "994                 Cv Cineorasotun.sis El Eneoscnvrdi   \n",
       "997                        Rrricpecnooirn Saehacrt Ono   \n",
       "1004                         Noi. Sctvesccv Ineiisiaar   \n",
       "1007                             Lic. R Rmjaibanehc.ke   \n",
       "1008                  Potyecusrtrenirfdes Ni.ssom Le S   \n",
       "...                                                ...   \n",
       "1257                       Nss Naccieemvadttiiisv Rire   \n",
       "1258                     C Ndl Ronie T.l.aoamyafin.icr   \n",
       "1259         Npeioulosrdoel Nrtmtiop Cnccnetr Aa Ds Ic   \n",
       "1260                            Iltc Aoiyontanlasvl1us   \n",
       "1261                       Tndg Liincceetlo Rcofselech   \n",
       "1262              Elirle Roico Fnnslct Rosutefieuuf Ng   \n",
       "1263                                  .airc Cae Tnccne   \n",
       "1264                               Lv U.ucdatehrl T In   \n",
       "1265                                 Tl Otvoip,lapclni   \n",
       "1266                           Ln'olao Elsr Teimrvrcnn   \n",
       "1267                         Coeecasntl Ievr .kesmsilc   \n",
       "1268                               Rcae Onotd S Ptr Ri   \n",
       "1269                                Svih Nopica Adrct.   \n",
       "1270                                        Tyrkuierob   \n",
       "1271                              Esc Hincr Vnteitee.r   \n",
       "1272                              Ttsoirkislekm Iecl.c   \n",
       "1273                                          Qr C.cin   \n",
       "1274  O Ts Sosvheneceseant Griit Mlocloysiy& Nu.bs Ces   \n",
       "1275                                         Pssmehcad   \n",
       "1276                      .onvia. Meecn Irrei G Gnnnci   \n",
       "1277                          Gral E.onc Vei Nonchesit   \n",
       "1278                                      Nosim.lhn'ac   \n",
       "1279           Lfsts Liuobli Iaste Iaibuyntriisnliddct   \n",
       "1280             Pgr Nedljenl Eiliacttgh Ec Crieanonnc   \n",
       "1281                                Ntvgocrrapiaeeeond   \n",
       "1282                                     Mo Bl Gtrtupd   \n",
       "1283         Rcl Se Nrsniiugoonpaaeadonictitootces Tcr   \n",
       "1284                           N Xtessmeci Dtyaasne.gn   \n",
       "1285                 Nnpncsbroaotelaitroau Urrnilpioit   \n",
       "1286                      Teosisndr Inopc Taeyesttdmrr   \n",
       "\n",
       "      federal_action_obligation  exec_1_comp_fraction_obligations  \n",
       "518                 34389540.24                          0.142885  \n",
       "523                 33848885.36                          0.177005  \n",
       "610                 25828211.82                          0.107825  \n",
       "632                 24633249.68                          0.179766  \n",
       "646                 23788559.16                          0.100902  \n",
       "698                 20416302.08                          0.163350  \n",
       "801                 12702238.00                          0.250472  \n",
       "815                 11839973.32                          0.111500  \n",
       "832                 10634615.93                          0.256789  \n",
       "836                 10376478.84                          0.118362  \n",
       "840                 10236832.01                          0.182522  \n",
       "867                  9257209.80                          1.040594  \n",
       "878                  8839249.80                          0.389624  \n",
       "899                  7796269.79                          0.227581  \n",
       "905                  7620940.94                          0.117161  \n",
       "920                  6995512.89                          0.175604  \n",
       "936                  6204593.58                          0.124609  \n",
       "950                  5690847.79                          0.559066  \n",
       "957                  5389272.01                          0.108929  \n",
       "962                  4923382.79                          0.147256  \n",
       "970                  4510000.00                          0.170306  \n",
       "973                  4448684.00                          0.764106  \n",
       "981                  4335928.14                          0.108949  \n",
       "988                  4105115.10                          0.317145  \n",
       "990                  4050916.37                          0.118492  \n",
       "994                  3877827.24                          0.377987  \n",
       "997                  3666908.00                          0.210116  \n",
       "1004                 3487667.35                          0.132041  \n",
       "1007                 3369611.45                          0.226038  \n",
       "1008                 3300050.16                          0.177426  \n",
       "...                         ...                               ...  \n",
       "1257                   25699.53                          6.809463  \n",
       "1258                   25351.61                          5.916784  \n",
       "1259                   22171.23                         19.168986  \n",
       "1260                   21772.20                         10.078081  \n",
       "1261                   21600.00                          4.857315  \n",
       "1262                   17719.60                          7.054335  \n",
       "1263                   17682.85                         51.044218  \n",
       "1264                   17300.00                         14.450867  \n",
       "1265                   16901.58                          8.874910  \n",
       "1266                   15637.31                          7.034458  \n",
       "1267                   14479.73                         60.168940  \n",
       "1268                   14476.02                          7.252270  \n",
       "1269                   13500.00                         12.088889  \n",
       "1270                    9974.00                         14.171947  \n",
       "1271                    6420.54                        111.642167  \n",
       "1272                    6042.00                         49.652433  \n",
       "1273                    5287.00                         49.593342  \n",
       "1274                    5000.00                        716.155400  \n",
       "1275                    4377.97                         37.688700  \n",
       "1276                    4258.86                        112.706217  \n",
       "1277                    4100.00                         80.531350  \n",
       "1278                    3716.82                        245.698204  \n",
       "1279                    2705.51                         45.093162  \n",
       "1280                    2600.64                        124.846192  \n",
       "1281                     500.00                        603.375000  \n",
       "1282                     500.00                        546.066000  \n",
       "1283                     500.00                       1007.482000  \n",
       "1284                     500.00                       1597.480000  \n",
       "1285                     500.00                        595.836833  \n",
       "1286                     347.00                       1288.184438  \n",
       "\n",
       "[270 rows x 3 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_subset_GFY19_compensation_obs['exec_1_comp_fraction_obligations'] = df_study_subset_GFY19_compensation_obs['mean']/df_study_subset_GFY19_compensation_obs['federal_action_obligation']\n",
    "selector = (df_study_subset_GFY19_compensation_obs['exec_1_comp_fraction_obligations'] > 0.0) & (~df_study_subset_GFY19_compensation_obs['exec_1_comp_fraction_obligations'].isin([np.inf]))\n",
    "df_study_subset_GFY19_compensation_obs[selector]['exec_1_comp_fraction_obligations'] #.hist()\n",
    "\n",
    "# Which have exec_1 compensation 10% or higher of total prime obligations?\n",
    "# some of these may be coding errors and others may be companies with a small fraction of revenue from USApsneding.gov-reported Federal obligations\n",
    "selector = (df_study_subset_GFY19_compensation_obs['exec_1_comp_fraction_obligations'] > 0.1) & (~df_study_subset_GFY19_compensation_obs['exec_1_comp_fraction_obligations'].isin([np.inf]))\n",
    "Scramble_Company_Exec_Names(df_study_subset_GFY19_compensation_obs[selector])[['recipient_parent_name', 'federal_action_obligation', 'exec_1_comp_fraction_obligations']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_study_subset_GFY19[~df_study_subset_GFY19['highly_compensated_officer_1_multiple_vs_officer_n_2'].isin([np.nan, np.inf, -np.inf])]['highly_compensated_officer_1_multiple_vs_officer_n_2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recipient_parent_name</th>\n",
       "      <th>highly_compensated_officer_1_name</th>\n",
       "      <th>highly_compensated_officer_1_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Rcee Nnnmtesev Ogrirrnv Ceesci.</td>\n",
       "      <td>Rerf Tn Seabh</td>\n",
       "      <td>9.633001e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ovtihel Oc Lnrleoe Sgkac. Go</td>\n",
       "      <td>Rosasrk Gm</td>\n",
       "      <td>9.548116e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Slsf Ncadsotrahclaeealik</td>\n",
       "      <td>Falhekaa Dwrn</td>\n",
       "      <td>7.489787e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Wtatlrecoecc Rhtia E Pshar.ilaenl</td>\n",
       "      <td>Advine Jrtyc Dim</td>\n",
       "      <td>6.340032e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Clcitican.np Aliitomo Innnr E</td>\n",
       "      <td>H Ineacruhd Scdislh</td>\n",
       "      <td>5.991407e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C Rsmsseinimyna Niotcto.inf</td>\n",
       "      <td>An Esllucarj</td>\n",
       "      <td>5.736979e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ntecqit Cihnua</td>\n",
       "      <td>Wbarnsiohl E</td>\n",
       "      <td>5.466806e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yacn Rolr Ctgpsarnnieeiadomo</td>\n",
       "      <td>Dajo In Nenhlos</td>\n",
       "      <td>5.124830e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ioc Dape Raatnnonlesrrcgmiyo</td>\n",
       "      <td>Olnode Snh Ansji</td>\n",
       "      <td>4.984412e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Eeoactteror Pniconn</td>\n",
       "      <td>E S Ntnjrueseh</td>\n",
       "      <td>4.962844e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               recipient_parent_name highly_compensated_officer_1_name  \\\n",
       "0    Rcee Nnnmtesev Ogrirrnv Ceesci.                     Rerf Tn Seabh   \n",
       "1       Ovtihel Oc Lnrleoe Sgkac. Go                        Rosasrk Gm   \n",
       "2         A Slsf Ncadsotrahclaeealik                     Falhekaa Dwrn   \n",
       "3  Wtatlrecoecc Rhtia E Pshar.ilaenl                  Advine Jrtyc Dim   \n",
       "4      Clcitican.np Aliitomo Innnr E               H Ineacruhd Scdislh   \n",
       "5        C Rsmsseinimyna Niotcto.inf                      An Esllucarj   \n",
       "6                     Ntecqit Cihnua                      Wbarnsiohl E   \n",
       "7       Yacn Rolr Ctgpsarnnieeiadomo                   Dajo In Nenhlos   \n",
       "8       Ioc Dape Raatnnonlesrrcgmiyo                  Olnode Snh Ansji   \n",
       "9                Eeoactteror Pniconn                    E S Ntnjrueseh   \n",
       "\n",
       "   highly_compensated_officer_1_amount  \n",
       "0                         9.633001e+06  \n",
       "1                         9.548116e+06  \n",
       "2                         7.489787e+06  \n",
       "3                         6.340032e+06  \n",
       "4                         5.991407e+06  \n",
       "5                         5.736979e+06  \n",
       "6                         5.466806e+06  \n",
       "7                         5.124830e+06  \n",
       "8                         4.984412e+06  \n",
       "9                         4.962844e+06  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp = df_study_subset_GFY19.groupby(['recipient_parent_name','highly_compensated_officer_1_name'])['highly_compensated_officer_1_amount'].mean().sort_values(ascending=False).reset_index()\n",
    "Scramble_Company_Exec_Names(df_temp).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the relative compensation of the top 5 executives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8ef187090>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note addition of , showfliers=False to remove outliers - https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.boxplot.html\n",
    "boxplot_columns = ['highly_compensated_officer_' + str(n) + '_amount' for n in range(1,6)]\n",
    "df_study_subset_GFY19.boxplot(column = boxplot_columns,  showfliers=False, rot=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_study_relative_comp = df_study_subset_GFY19[['recipient_parent_name'] + boxplot_columns].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "exec_multiple_fields = []\n",
    "for n in range(2,6):\n",
    "    field_name = 'highly_compensated_officer_' + str(n) + '_amount'\n",
    "    new_field = 'exec1_multiple_exec_' + str(n)\n",
    "    exec_multiple_fields.append(new_field)\n",
    "    df_study_relative_comp[new_field] = df_study_relative_comp['highly_compensated_officer_1_amount']/df_study_relative_comp[field_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd87242bc90>"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_study_relative_comp.boxplot(column = exec_multiple_fields, showfliers=False, rot=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd8e91f4850>"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = list(range(100000, 3000000, 10000))\n",
    "df_study_relative_comp['highly_compensated_officer_1_amount'].hist(bins=bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# $1m/year or greater\n",
    "selector = (df_study_relative_comp['highly_compensated_officer_1_amount'] >= 1e6)\n",
    "df_study_relative_comp[selector]['recipient_parent_name'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "323"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Between $500k/year-$1M/year\n",
    "selector = (df_study_relative_comp['highly_compensated_officer_1_amount'] >= 0.5e6) & ((df_study_relative_comp['highly_compensated_officer_1_amount'] < 1e6))\n",
    "df_study_relative_comp[selector]['recipient_parent_name'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1123"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Between less than $500k/year\n",
    "selector = (df_study_relative_comp['highly_compensated_officer_1_amount'] < 0.5e6)\n",
    "df_study_relative_comp[selector]['recipient_parent_name'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1410"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_study_relative_comp['recipient_parent_name'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assuming you can clean up the data sufficiently and the business is mostly Federal, can an analyst use the Top 5 compensation data to estimate profit margins of your competitors?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://opensource.org/licenses/MIT\n",
    "\n",
    "# MIT Open Source License\n",
    "# Copyright 2020 Leif C Ulstrup\n",
    "\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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