sap-forensic-accounting.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Data-Mining-the-&quot;Big-Two&quot;-SAP-FI-Tables\" data-toc-modified-id=\"Data-Mining-the-&quot;Big-Two&quot;-SAP-FI-Tables-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Data Mining the \"Big Two\" SAP FI Tables</a></span><ul class=\"toc-item\"><li><span><a href=\"#Executive-Summary\" data-toc-modified-id=\"Executive-Summary-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Executive Summary</a></span></li><li><span><a href=\"#Scope\" data-toc-modified-id=\"Scope-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Scope</a></span><ul class=\"toc-item\"><li><span><a href=\"#Data\" data-toc-modified-id=\"Data-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Loading-the-data\" data-toc-modified-id=\"Loading-the-data-1.2.1.1\"><span class=\"toc-item-num\">1.2.1.1&nbsp;&nbsp;</span>Loading the data</a></span></li><li><span><a href=\"#Transforming-the-data\" data-toc-modified-id=\"Transforming-the-data-1.2.1.2\"><span class=\"toc-item-num\">1.2.1.2&nbsp;&nbsp;</span>Transforming the data</a></span></li></ul></li><li><span><a href=\"#Methodology\" data-toc-modified-id=\"Methodology-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>Methodology</a></span></li><li><span><a href=\"#Analysis\" data-toc-modified-id=\"Analysis-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Vendor-Analysis\" data-toc-modified-id=\"Vendor-Analysis-1.2.3.1\"><span class=\"toc-item-num\">1.2.3.1&nbsp;&nbsp;</span>Vendor Analysis</a></span></li><li><span><a href=\"#User-Analysis\" data-toc-modified-id=\"User-Analysis-1.2.3.2\"><span class=\"toc-item-num\">1.2.3.2&nbsp;&nbsp;</span>User Analysis</a></span></li></ul></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Conclusion</a></span></li><li><span><a href=\"#Disclaimer\" data-toc-modified-id=\"Disclaimer-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Disclaimer</a></span></li><li><span><a href=\"#Appendix\" data-toc-modified-id=\"Appendix-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Appendix</a></span></li></ul></li></ul></div>"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Mining the \"Big Two\" SAP FI Tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Executive Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a forensic accounting technique on SAP data, there is evidence that almost 50 vendor payments worth nearly **$4 million** are irregular and should be investigated further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Scope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am tasked with the preliminary data analysis on two SAP Financial Accounting transaction tables.  Specifically I will apply a fraud detection technique to screen the dataset and determine if any further investigation will be required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of 2 CSV files with sample SAP outputs for 2017 from the BSEG and BKPF tables.  Data was sourced from Michael Schermann.  The BSEG table records the transaction line items and the BKPF table records the corresponding headers.  Here are the high level ETL steps required to prepare our data:\n",
    "\n",
    "1. Importing the necessary libraries\n",
    "2. Loading into the dataframe\n",
    "3. Wrangling and cleansing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "bseg = pd.read_csv('BSEG.csv', low_memory = False)\n",
    "bkpf = pd.read_csv('BKPF.csv', low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>FMFGUS_KEY</th>\n",
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       "      <th>FMXDOCLN</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/24/17</td>\n",
       "      <td>1/24/17</td>\n",
       "      <td>1.500000e+09</td>\n",
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       "      <th>4</th>\n",
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       "   Unnamed: 0  MANDT BUKRS         BELNR  GJAHR  BUZEI BUZID    AUGDT  \\\n",
       "0           0    375  US00  1.500000e+09   2017      1   NaN      NaN   \n",
       "1           1    375  US00  1.500000e+09   2017      2   NaN  1/24/17   \n",
       "2           2    375  US00  1.500000e+09   2017      1   NaN      NaN   \n",
       "3           3    375  US00  1.500000e+09   2017      2   NaN  1/24/17   \n",
       "4           4    375  US00  1.500000e+09   2017      1   NaN      NaN   \n",
       "\n",
       "     AUGCP         AUGBL  ...  PEROP_END FASTPAY  IGNR_IVREF  FMFGUS_KEY  \\\n",
       "0      NaN           NaN  ...        NaN     NaN         NaN         NaN   \n",
       "1  1/24/17  1.500000e+09  ...        NaN     NaN         NaN         NaN   \n",
       "2      NaN           NaN  ...        NaN     NaN         NaN         NaN   \n",
       "3  1/24/17  1.500000e+09  ...        NaN     NaN         NaN         NaN   \n",
       "4      NaN           NaN  ...        NaN     NaN         NaN         NaN   \n",
       "\n",
       "   FMXDOCNR FMXYEAR FMXDOCLN FMXZEKKN PRODPER  RECRF  \n",
       "0       NaN     NaN        0        0     NaN    NaN  \n",
       "1       NaN     NaN        0        0     NaN    NaN  \n",
       "2       NaN     NaN        0        0     NaN    NaN  \n",
       "3       NaN     NaN        0        0     NaN    NaN  \n",
       "4       NaN     NaN        0        0     NaN    NaN  \n",
       "\n",
       "[5 rows x 337 columns]"
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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "bseg.head()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there is an unnamed column is read into the data frame, I will reimport the CSV using the 1st column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>GJAHR</th>\n",
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       "      <th>AUGCP</th>\n",
       "      <th>AUGBL</th>\n",
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       "      <th>...</th>\n",
       "      <th>PEROP_END</th>\n",
       "      <th>FASTPAY</th>\n",
       "      <th>IGNR_IVREF</th>\n",
       "      <th>FMFGUS_KEY</th>\n",
       "      <th>FMXDOCNR</th>\n",
       "      <th>FMXYEAR</th>\n",
       "      <th>FMXDOCLN</th>\n",
       "      <th>FMXZEKKN</th>\n",
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       "      <td>1/24/17</td>\n",
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       "      <td>375</td>\n",
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       "      <td>2017</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1/24/17</td>\n",
       "      <td>1/24/17</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 336 columns</p>\n",
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      ],
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       "   MANDT BUKRS         BELNR  GJAHR  BUZEI BUZID    AUGDT    AUGCP  \\\n",
       "0    375  US00  1.500000e+09   2017      1   NaN      NaN      NaN   \n",
       "1    375  US00  1.500000e+09   2017      2   NaN  1/24/17  1/24/17   \n",
       "2    375  US00  1.500000e+09   2017      1   NaN      NaN      NaN   \n",
       "3    375  US00  1.500000e+09   2017      2   NaN  1/24/17  1/24/17   \n",
       "4    375  US00  1.500000e+09   2017      1   NaN      NaN      NaN   \n",
       "\n",
       "          AUGBL  BSCHL  ... PEROP_END  FASTPAY  IGNR_IVREF  FMFGUS_KEY  \\\n",
       "0           NaN     50  ...       NaN      NaN         NaN         NaN   \n",
       "1  1.500000e+09     25  ...       NaN      NaN         NaN         NaN   \n",
       "2           NaN     50  ...       NaN      NaN         NaN         NaN   \n",
       "3  1.500000e+09     25  ...       NaN      NaN         NaN         NaN   \n",
       "4           NaN     50  ...       NaN      NaN         NaN         NaN   \n",
       "\n",
       "  FMXDOCNR FMXYEAR FMXDOCLN FMXZEKKN  PRODPER RECRF  \n",
       "0      NaN     NaN        0        0      NaN   NaN  \n",
       "1      NaN     NaN        0        0      NaN   NaN  \n",
       "2      NaN     NaN        0        0      NaN   NaN  \n",
       "3      NaN     NaN        0        0      NaN   NaN  \n",
       "4      NaN     NaN        0        0      NaN   NaN  \n",
       "\n",
       "[5 rows x 336 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg = pd.read_csv('BSEG.csv', low_memory = False, index_col = 0)\n",
    "\n",
    "bseg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2811 entries, 0 to 2810\n",
      "Columns: 336 entries, MANDT to RECRF\n",
      "dtypes: float64(180), int64(104), object(52)\n",
      "memory usage: 7.2+ MB\n"
     ]
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   "source": [
    "bseg.info()"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BUZEI', 'BUZID', 'AUGDT',\n",
       "       'AUGCP', 'AUGBL', 'BSCHL', 'KOART', 'UMSKZ', 'UMSKS', 'ZUMSK',\n",
       "       'SHKZG', 'GSBER', 'PARGB', 'MWSKZ', 'QSSKZ', 'DMBTR', 'WRBTR',\n",
       "       'KZBTR', 'PSWBT', 'PSWSL', 'TXBHW', 'TXBFW', 'MWSTS', 'WMWST',\n",
       "       'HWBAS', 'FWBAS', 'HWZUZ', 'FWZUZ', 'SHZUZ', 'STEKZ', 'MWART',\n",
       "       'TXGRP', 'KTOSL', 'QSSHB', 'KURSR', 'GBETR', 'BDIFF', 'BDIF2',\n",
       "       'VALUT', 'ZUONR', 'SGTXT', 'ZINKZ', 'VBUND', 'BEWAR', 'ALTKT',\n",
       "       'VORGN', 'FDLEV', 'FDGRP', 'FDWBT', 'FDTAG', 'FKONT', 'KOKRS',\n",
       "       'KOSTL', 'PROJN', 'AUFNR', 'VBELN', 'VBEL2', 'POSN2', 'ETEN2',\n",
       "       'ANLN1', 'ANLN2', 'ANBWA', 'BZDAT', 'PERNR', 'XUMSW', 'XHRES',\n",
       "       'XKRES', 'XOPVW', 'XCPDD', 'XSKST', 'XSAUF', 'XSPRO', 'XSERG',\n",
       "       'XFAKT', 'XUMAN', 'XANET', 'XSKRL', 'XINVE', 'XPANZ', 'XAUTO',\n",
       "       'XNCOP', 'XZAHL', 'SAKNR', 'HKONT', 'KUNNR', 'LIFNR', 'FILKD',\n",
       "       'XBILK', 'GVTYP', 'HZUON', 'ZFBDT', 'ZTERM', 'ZBD1T', 'ZBD2T',\n",
       "       'ZBD3T', 'ZBD1P', 'ZBD2P', 'SKFBT', 'SKNTO', 'WSKTO', 'ZLSCH',\n",
       "       'ZLSPR', 'ZBFIX', 'HBKID', 'BVTYP', 'NEBTR', 'MWSK1', 'DMBT1',\n",
       "       'WRBT1', 'MWSK2', 'DMBT2', 'WRBT2', 'MWSK3', 'DMBT3', 'WRBT3',\n",
       "       'REBZG', 'REBZJ', 'REBZZ', 'REBZT', 'ZOLLT', 'ZOLLD', 'LZBKZ',\n",
       "       'LANDL', 'DIEKZ', 'SAMNR', 'ABPER', 'VRSKZ', 'VRSDT', 'DISBN',\n",
       "       'DISBJ', 'DISBZ', 'WVERW', 'ANFBN', 'ANFBJ', 'ANFBU', 'ANFAE',\n",
       "       'BLNBT', 'BLNKZ', 'BLNPZ', 'MSCHL', 'MANSP', 'MADAT', 'MANST',\n",
       "       'MABER', 'ESRNR', 'ESRRE', 'ESRPZ', 'KLIBT', 'QSZNR', 'QBSHB',\n",
       "       'QSFBT', 'NAVHW', 'NAVFW', 'MATNR', 'WERKS', 'MENGE', 'MEINS',\n",
       "       'ERFMG', 'ERFME', 'BPMNG', 'BPRME', 'EBELN', 'EBELP', 'ZEKKN',\n",
       "       'ELIKZ', 'VPRSV', 'PEINH', 'BWKEY', 'BWTAR', 'BUSTW', 'REWRT',\n",
       "       'REWWR', 'BONFB', 'BUALT', 'PSALT', 'NPREI', 'TBTKZ', 'SPGRP',\n",
       "       'SPGRM', 'SPGRT', 'SPGRG', 'SPGRV', 'SPGRQ', 'STCEG', 'EGBLD',\n",
       "       'EGLLD', 'RSTGR', 'RYACQ', 'RPACQ', 'RDIFF', 'RDIF2', 'PRCTR',\n",
       "       'XHKOM', 'VNAME', 'RECID', 'EGRUP', 'VPTNR', 'VERTT', 'VERTN',\n",
       "       'VBEWA', 'DEPOT', 'TXJCD', 'IMKEY', 'DABRZ', 'POPTS', 'FIPOS',\n",
       "       'KSTRG', 'NPLNR', 'AUFPL', 'APLZL', 'PROJK', 'PAOBJNR', 'PASUBNR',\n",
       "       'SPGRS', 'SPGRC', 'BTYPE', 'ETYPE', 'XEGDR', 'LNRAN', 'HRKFT',\n",
       "       'DMBE2', 'DMBE3', 'DMB21', 'DMB22', 'DMB23', 'DMB31', 'DMB32',\n",
       "       'DMB33', 'MWST2', 'MWST3', 'NAVH2', 'NAVH3', 'SKNT2', 'SKNT3',\n",
       "       'BDIF3', 'RDIF3', 'HWMET', 'GLUPM', 'XRAGL', 'UZAWE', 'LOKKT',\n",
       "       'FISTL', 'GEBER', 'STBUK', 'TXBH2', 'TXBH3', 'PPRCT', 'XREF1',\n",
       "       'XREF2', 'KBLNR', 'KBLPOS', 'STTAX', 'FKBER', 'OBZEI', 'XNEGP',\n",
       "       'RFZEI', 'CCBTC', 'KKBER', 'EMPFB', 'XREF3', 'DTWS1', 'DTWS2',\n",
       "       'DTWS3', 'DTWS4', 'GRICD', 'GRIRG', 'GITYP', 'XPYPR', 'KIDNO',\n",
       "       'ABSBT', 'IDXSP', 'LINFV', 'KONTT', 'KONTL', 'TXDAT', 'AGZEI',\n",
       "       'PYCUR', 'PYAMT', 'BUPLA', 'SECCO', 'LSTAR', 'CESSION_KZ', 'PRZNR',\n",
       "       'PPDIFF', 'PPDIF2', 'PPDIF3', 'PENLC1', 'PENLC2', 'PENLC3',\n",
       "       'PENFC', 'PENDAYS', 'PENRC', 'GRANT_NBR', 'SCTAX', 'FKBER_LONG',\n",
       "       'GMVKZ', 'SRTYPE', 'INTRENO', 'MEASURE', 'AUGGJ', 'PPA_EX_IND',\n",
       "       'DOCLN', 'SEGMENT', 'PSEGMENT', 'PFKBER', 'HKTID', 'KSTAR',\n",
       "       'XLGCLR', 'TAXPS', 'PAYS_PROV', 'PAYS_TRAN', 'MNDID', 'XFRGE_BSEG',\n",
       "       'SQUAN', 'RE_BUKRS', 'RE_ACCOUNT', 'PGEBER', 'PGRANT_NBR',\n",
       "       'BUDGET_PD', 'PBUDGET_PD', 'J_1TPBUPL', 'PEROP_BEG', 'PEROP_END',\n",
       "       'FASTPAY', 'IGNR_IVREF', 'FMFGUS_KEY', 'FMXDOCNR', 'FMXYEAR',\n",
       "       'FMXDOCLN', 'FMXZEKKN', 'PRODPER', 'RECRF'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "336 columns.  Which are actually useful?  With some additional research and sleuthing, I was able to narrow down the columns in this table to just 5 in particular.  However, before dropping the remainder, I wanted to skim the summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>MANDT</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BUZEI</th>\n",
       "      <th>AUGBL</th>\n",
       "      <th>BSCHL</th>\n",
       "      <th>UMSKZ</th>\n",
       "      <th>UMSKS</th>\n",
       "      <th>ZUMSK</th>\n",
       "      <th>QSSKZ</th>\n",
       "      <th>...</th>\n",
       "      <th>PEROP_END</th>\n",
       "      <th>FASTPAY</th>\n",
       "      <th>IGNR_IVREF</th>\n",
       "      <th>FMFGUS_KEY</th>\n",
       "      <th>FMXDOCNR</th>\n",
       "      <th>FMXYEAR</th>\n",
       "      <th>FMXDOCLN</th>\n",
       "      <th>FMXZEKKN</th>\n",
       "      <th>PRODPER</th>\n",
       "      <th>RECRF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2811.0</td>\n",
       "      <td>2.811000e+03</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>2811.000000</td>\n",
       "      <td>7.030000e+02</td>\n",
       "      <td>2811.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>2811.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>375.0</td>\n",
       "      <td>4.043594e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.675916</td>\n",
       "      <td>1.515363e+09</td>\n",
       "      <td>67.438634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.596921e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.726656</td>\n",
       "      <td>5.329699e+07</td>\n",
       "      <td>28.122635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>375.0</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>5.000000e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>5.105600e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375.0</td>\n",
       "      <td>5.105601e+09</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.700000e+09</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 284 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MANDT         BELNR   GJAHR        BUZEI         AUGBL        BSCHL  \\\n",
       "count  2811.0  2.811000e+03  2811.0  2811.000000  7.030000e+02  2811.000000   \n",
       "mean    375.0  4.043594e+09  2017.0     1.675916  1.515363e+09    67.438634   \n",
       "std       0.0  1.596921e+09     0.0     0.726656  5.329699e+07    28.122635   \n",
       "min     375.0  1.500000e+09  2017.0     1.000000  1.500000e+09    21.000000   \n",
       "25%     375.0  1.500000e+09  2017.0     1.000000  1.500000e+09    31.000000   \n",
       "50%     375.0  5.000000e+09  2017.0     2.000000  1.500000e+09    86.000000   \n",
       "75%     375.0  5.105600e+09  2017.0     2.000000  1.500000e+09    89.000000   \n",
       "max     375.0  5.105601e+09  2017.0     9.000000  1.700000e+09    99.000000   \n",
       "\n",
       "       UMSKZ  UMSKS  ZUMSK  QSSKZ  ...  PEROP_END  FASTPAY  IGNR_IVREF  \\\n",
       "count    0.0    0.0    0.0    0.0  ...        0.0      0.0         0.0   \n",
       "mean     NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "std      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "min      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "25%      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "50%      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "75%      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "max      NaN    NaN    NaN    NaN  ...        NaN      NaN         NaN   \n",
       "\n",
       "       FMFGUS_KEY  FMXDOCNR  FMXYEAR  FMXDOCLN  FMXZEKKN  PRODPER  RECRF  \n",
       "count         0.0       0.0      0.0    2811.0    2811.0      0.0    0.0  \n",
       "mean          NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "std           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "min           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "25%           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "50%           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "75%           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "max           NaN       NaN      NaN       0.0       0.0      NaN    NaN  \n",
       "\n",
       "[8 rows x 284 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MANDT         int64\n",
       "BUKRS        object\n",
       "BELNR       float64\n",
       "GJAHR         int64\n",
       "BUZEI         int64\n",
       "             ...   \n",
       "FMXYEAR     float64\n",
       "FMXDOCLN      int64\n",
       "FMXZEKKN      int64\n",
       "PRODPER     float64\n",
       "RECRF       float64\n",
       "Length: 336, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MANDT, BELNR, GJAHR, BUZEI, and BSCHL are accounted for.  I will need to validate that BUKRS, WRBTR, and LIFNR also exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2811\n",
       "unique       1\n",
       "top       US00\n",
       "freq      2811\n",
       "Name: BUKRS, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg['BUKRS'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         2811\n",
       "unique         494\n",
       "top       1,000.00\n",
       "freq           202\n",
       "Name: WRBTR, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg['WRBTR'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the amount field is currently a string but contains commas and periods.  These will have to be removed in order to extract the significant digits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "572         1000\n",
       "579         1000\n",
       "1001         371\n",
       "1219         240\n",
       "1469    15625000\n",
       "1998       12200\n",
       "1769       15000\n",
       "501       144000\n",
       "1869          40\n",
       "218         2900\n",
       "Name: WRBTR, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg['WRBTR'] = bseg['WRBTR'].replace({',': ''}, regex = True)\n",
    "bseg['WRBTR'] = pd.to_numeric(bseg['WRBTR'], errors = 'coerce').astype(int)\n",
    "bseg['WRBTR'] = bseg['WRBTR'].apply(str)\n",
    "bseg['WRBTR'].sample(10, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1807.000000\n",
       "mean     125088.422247\n",
       "std          42.827934\n",
       "min      125000.000000\n",
       "25%      125053.000000\n",
       "50%      125102.000000\n",
       "75%      125126.000000\n",
       "max      125136.000000\n",
       "Name: LIFNR, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg['LIFNR'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BUZEI</th>\n",
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       "  <tbody>\n",
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       "      <th>572</th>\n",
       "      <td>375</td>\n",
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       "      <th>1469</th>\n",
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       "      <td>1</td>\n",
       "      <td>99</td>\n",
       "      <td>15625000</td>\n",
       "      <td>125088.0</td>\n",
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       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
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       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>12200</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1769</th>\n",
       "      <td>375</td>\n",
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       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>96</td>\n",
       "      <td>15000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>144000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5.105600e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>86</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>2900</td>\n",
       "      <td>125135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MANDT BUKRS         BELNR  GJAHR  BUZEI  BSCHL     WRBTR     LIFNR\n",
       "572     375  US00  1.500000e+09   2017      1     50      1000       NaN\n",
       "579     375  US00  1.500000e+09   2017      2     25      1000  125135.0\n",
       "1001    375  US00  5.000000e+09   2017      3     86       371  125070.0\n",
       "1219    375  US00  5.000000e+09   2017      1     89       240  125070.0\n",
       "1469    375  US00  5.000000e+09   2017      1     99  15625000  125088.0\n",
       "1998    375  US00  5.105600e+09   2017      2     86     12200       NaN\n",
       "1769    375  US00  5.105600e+09   2017      1     96     15000       NaN\n",
       "501     375  US00  1.500000e+09   2017      1     50    144000       NaN\n",
       "1869    375  US00  5.105600e+09   2017      2     86        40       NaN\n",
       "218     375  US00  1.500000e+09   2017      2     25      2900  125135.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg = bseg[['MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BUZEI', 'BSCHL', 'WRBTR', 'LIFNR']]\n",
    "bseg.sample(10, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows:  (0, 8)\n"
     ]
    }
   ],
   "source": [
    "bseg_dupe = bseg[bseg.duplicated()]\n",
    "print(\"Number of duplicate rows: \", bseg_dupe.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicate rows, otherwise the .drop_duplicates() function can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MANDT       0\n",
      "BUKRS       0\n",
      "BELNR       0\n",
      "GJAHR       0\n",
      "BUZEI       0\n",
      "BSCHL       0\n",
      "WRBTR       0\n",
      "LIFNR    1004\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bseg.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LIFNR has a significant amount of null values.  I will keep the field but clearly further work should be done to understand the reasoning behind this.  Should null vendors even be allowed into transactions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BUZEI</th>\n",
       "      <th>BSCHL</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>LIFNR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>213710</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1068550</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>933153</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>30300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT BUKRS         BELNR  GJAHR  BUZEI  BSCHL    WRBTR  LIFNR\n",
       "0    375  US00  1.500000e+09   2017      1     50   213710    NaN\n",
       "2    375  US00  1.500000e+09   2017      1     50  1068550    NaN\n",
       "4    375  US00  1.500000e+09   2017      1     50   933153    NaN\n",
       "6    375  US00  1.500000e+09   2017      1     50     1050    NaN\n",
       "8    375  US00  1.500000e+09   2017      1     50    30300    NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bsegNull = bseg[bseg['LIFNR'].isnull()]\n",
    "bsegNull.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DW814QU\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1000       89\n",
       "10000      50\n",
       "100000     35\n",
       "1000000    24\n",
       "200000     21\n",
       "           ..\n",
       "82677       1\n",
       "933153      1\n",
       "93959       1\n",
       "24154       1\n",
       "1299        1\n",
       "Name: WRBTR, Length: 342, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bsegNull['WRBTR'] = pd.to_numeric(bsegNull['WRBTR'], errors = 'coerce')\n",
    "bsegNull['WRBTR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125104.0    140\n",
       "125131.0    139\n",
       "125135.0    133\n",
       "125094.0    129\n",
       "125000.0     68\n",
       "           ... \n",
       "125002.0      3\n",
       "125082.0      3\n",
       "125048.0      3\n",
       "125028.0      2\n",
       "125013.0      1\n",
       "Name: LIFNR, Length: 105, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg['LIFNR'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I replace the null values with an arbitrary 999999 value to include this data into the later calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MANDT    0\n",
      "BUKRS    0\n",
      "BELNR    0\n",
      "GJAHR    0\n",
      "BUZEI    0\n",
      "BSCHL    0\n",
      "WRBTR    0\n",
      "LIFNR    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "bseg['LIFNR'].fillna('999999', inplace = True)\n",
    "print(bseg.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transforming the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BSCHL is the posting key which is our field of interest as it indicates whether the entry is debit or credit and to what type of account it is executed on (e.g. GL, customer, vendor, assets, material).  More importantly, I will extract digits from our WRBTR field which represents the amount and will enable the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DW814QU\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\DW814QU\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"\n",
      "C:\\Users\\DW814QU\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BUZEI</th>\n",
       "      <th>BSCHL</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>Digit 1</th>\n",
       "      <th>Digit 2</th>\n",
       "      <th>Digit 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2330</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5.105600e+09</td>\n",
       "      <td>2017</td>\n",
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       "      <td>4750</td>\n",
       "      <td>999999</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5.105600e+09</td>\n",
       "      <td>2017</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>100000</td>\n",
       "      <td>999999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1055</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5.000000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>96</td>\n",
       "      <td>12400</td>\n",
       "      <td>125094</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2737</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1927</td>\n",
       "      <td>999999</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>43136</td>\n",
       "      <td>999999</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1000</td>\n",
       "      <td>999999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MANDT BUKRS         BELNR  GJAHR  BUZEI  BSCHL     WRBTR   LIFNR  \\\n",
       "2330    375  US00  5.105600e+09   2017      5     86      4750  999999   \n",
       "2223    375  US00  5.105600e+09   2017      2     86     10100  999999   \n",
       "1972    375  US00  5.105600e+09   2017      2     93   1000000  999999   \n",
       "1472    375  US00  5.000000e+09   2017      1     91  15000000  125088   \n",
       "348     375  US00  1.500000e+09   2017      1     50     85074  999999   \n",
       "61      375  US00  1.500000e+09   2017      1     50    100000  999999   \n",
       "1055    375  US00  5.000000e+09   2017      2     96     12400  125094   \n",
       "2737    375  US00  1.500000e+09   2017      1     50      1927  999999   \n",
       "381     375  US00  1.500000e+09   2017      1     50     43136  999999   \n",
       "596     375  US00  1.500000e+09   2017      1     50      1000  999999   \n",
       "\n",
       "      Digit 1  Digit 2  Digit 3  \n",
       "2330        4        7        5  \n",
       "2223        1        0        1  \n",
       "1972        1        0        0  \n",
       "1472        1        5        0  \n",
       "348         8        5        0  \n",
       "61          1        0        0  \n",
       "1055        1        2        4  \n",
       "2737        1        9        2  \n",
       "381         4        3        1  \n",
       "596         1        0        0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg_stg = bseg[(bseg['WRBTR'].str.len() > 3) & (bseg['WRBTR'].str.contains('-') == False)]\n",
    "#bseg_stg = bseg[(bseg['WRBTR'] > 1000)]\n",
    "\n",
    "bseg_stg['Digit 1'] = bseg_stg['WRBTR'].astype(str).str[0:1].astype(int)\n",
    "bseg_stg['Digit 2'] = bseg_stg['WRBTR'].astype(str).str[1:2].astype(int)\n",
    "bseg_stg['Digit 3'] = bseg_stg['WRBTR'].astype(str).str[2:3].astype(int)\n",
    "\n",
    "bseg_stg.sample(10, random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For data processing, I have filtered for only amounts that are at least $1,000 while retaining the amount field as a string to extract the digits.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BLART</th>\n",
       "      <th>BLDAT</th>\n",
       "      <th>BUDAT</th>\n",
       "      <th>MONAT</th>\n",
       "      <th>CPUDT</th>\n",
       "      <th>...</th>\n",
       "      <th>SSBLK</th>\n",
       "      <th>BATCH</th>\n",
       "      <th>SNAME</th>\n",
       "      <th>SAMPLED</th>\n",
       "      <th>EXCLUDE_FLAG</th>\n",
       "      <th>BLIND</th>\n",
       "      <th>OFFSET_STATUS</th>\n",
       "      <th>OFFSET_REFER_DAT</th>\n",
       "      <th>PENRC</th>\n",
       "      <th>KNUMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>RV</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000001</td>\n",
       "      <td>2016</td>\n",
       "      <td>RV</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000002</td>\n",
       "      <td>2016</td>\n",
       "      <td>RV</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1400000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>DZ</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1400000001</td>\n",
       "      <td>2016</td>\n",
       "      <td>DZ</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 114 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  MANDT BUKRS       BELNR  GJAHR BLART    BLDAT    BUDAT  MONAT  \\\n",
       "0           0    375  US00    90000000   2016    RV  5/27/16  5/27/16      5   \n",
       "1           1    375  US00    90000001   2016    RV  5/27/16  5/27/16      5   \n",
       "2           2    375  US00    90000002   2016    RV  5/27/16  5/27/16      5   \n",
       "3           3    375  US00  1400000000   2016    DZ  5/27/16  5/27/16      5   \n",
       "4           4    375  US00  1400000001   2016    DZ  5/27/16  5/27/16      5   \n",
       "\n",
       "     CPUDT  ... SSBLK BATCH  SNAME SAMPLED EXCLUDE_FLAG BLIND OFFSET_STATUS  \\\n",
       "0  5/27/16  ...   NaN   NaN    NaN     NaN          NaN   NaN           NaN   \n",
       "1  5/27/16  ...   NaN   NaN    NaN     NaN          NaN   NaN           NaN   \n",
       "2  5/27/16  ...   NaN   NaN    NaN     NaN          NaN   NaN           NaN   \n",
       "3  5/27/16  ...   NaN   NaN    NaN     NaN          NaN   NaN           NaN   \n",
       "4  5/27/16  ...   NaN   NaN    NaN     NaN          NaN   NaN           NaN   \n",
       "\n",
       "  OFFSET_REFER_DAT  PENRC  KNUMV  \n",
       "0              NaN    NaN    NaN  \n",
       "1              NaN    NaN    NaN  \n",
       "2              NaN    NaN    NaN  \n",
       "3              NaN    NaN    NaN  \n",
       "4              NaN    NaN    NaN  \n",
       "\n",
       "[5 rows x 114 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BLART</th>\n",
       "      <th>BLDAT</th>\n",
       "      <th>BUDAT</th>\n",
       "      <th>MONAT</th>\n",
       "      <th>CPUDT</th>\n",
       "      <th>CPUTM</th>\n",
       "      <th>...</th>\n",
       "      <th>SSBLK</th>\n",
       "      <th>BATCH</th>\n",
       "      <th>SNAME</th>\n",
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       "      <th>OFFSET_REFER_DAT</th>\n",
       "      <th>PENRC</th>\n",
       "      <th>KNUMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
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       "      <td>RV</td>\n",
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       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
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       "      <td>1:50:33 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000001</td>\n",
       "      <td>2016</td>\n",
       "      <td>RV</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>1:50:42 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>90000002</td>\n",
       "      <td>2016</td>\n",
       "      <td>RV</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>1:50:44 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1400000000</td>\n",
       "      <td>2016</td>\n",
       "      <td>DZ</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>1:53:58 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1400000001</td>\n",
       "      <td>2016</td>\n",
       "      <td>DZ</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>5</td>\n",
       "      <td>5/27/16</td>\n",
       "      <td>1:53:58 PM</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 113 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MANDT BUKRS       BELNR  GJAHR BLART    BLDAT    BUDAT  MONAT    CPUDT  \\\n",
       "0    375  US00    90000000   2016    RV  5/27/16  5/27/16      5  5/27/16   \n",
       "1    375  US00    90000001   2016    RV  5/27/16  5/27/16      5  5/27/16   \n",
       "2    375  US00    90000002   2016    RV  5/27/16  5/27/16      5  5/27/16   \n",
       "3    375  US00  1400000000   2016    DZ  5/27/16  5/27/16      5  5/27/16   \n",
       "4    375  US00  1400000001   2016    DZ  5/27/16  5/27/16      5  5/27/16   \n",
       "\n",
       "        CPUTM  ... SSBLK  BATCH SNAME SAMPLED EXCLUDE_FLAG BLIND  \\\n",
       "0  1:50:33 PM  ...   NaN    NaN   NaN     NaN          NaN   NaN   \n",
       "1  1:50:42 PM  ...   NaN    NaN   NaN     NaN          NaN   NaN   \n",
       "2  1:50:44 PM  ...   NaN    NaN   NaN     NaN          NaN   NaN   \n",
       "3  1:53:58 PM  ...   NaN    NaN   NaN     NaN          NaN   NaN   \n",
       "4  1:53:58 PM  ...   NaN    NaN   NaN     NaN          NaN   NaN   \n",
       "\n",
       "  OFFSET_STATUS  OFFSET_REFER_DAT  PENRC  KNUMV  \n",
       "0           NaN               NaN    NaN    NaN  \n",
       "1           NaN               NaN    NaN    NaN  \n",
       "2           NaN               NaN    NaN    NaN  \n",
       "3           NaN               NaN    NaN    NaN  \n",
       "4           NaN               NaN    NaN    NaN  \n",
       "\n",
       "[5 rows x 113 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf = pd.read_csv('BKPF.csv', low_memory = False, index_col = 0)\n",
    "\n",
    "bkpf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BLART', 'BLDAT', 'BUDAT',\n",
       "       'MONAT', 'CPUDT', 'CPUTM', 'AEDAT', 'UPDDT', 'WWERT', 'USNAM',\n",
       "       'TCODE', 'BVORG', 'XBLNR', 'DBBLG', 'STBLG', 'STJAH', 'BKTXT',\n",
       "       'WAERS', 'KURSF', 'KZWRS', 'KZKRS', 'BSTAT', 'XNETB', 'FRATH',\n",
       "       'XRUEB', 'GLVOR', 'GRPID', 'DOKID', 'ARCID', 'IBLAR', 'AWTYP',\n",
       "       'AWKEY', 'FIKRS', 'HWAER', 'HWAE2', 'HWAE3', 'KURS2', 'KURS3',\n",
       "       'BASW2', 'BASW3', 'UMRD2', 'UMRD3', 'XSTOV', 'STODT', 'XMWST',\n",
       "       'CURT2', 'CURT3', 'KUTY2', 'KUTY3', 'XSNET', 'AUSBK', 'XUSVR',\n",
       "       'DUEFL', 'AWSYS', 'TXKRS', 'CTXKRS', 'LOTKZ', 'XWVOF', 'STGRD',\n",
       "       'PPNAM', 'BRNCH', 'NUMPG', 'ADISC', 'XREF1_HD', 'XREF2_HD',\n",
       "       'XREVERSAL', 'REINDAT', 'RLDNR', 'LDGRP', 'PROPMANO', 'XBLNR_ALT',\n",
       "       'VATDATE', 'DOCCAT', 'XSPLIT', 'CASH_ALLOC', 'FOLLOW_ON', 'XREORG',\n",
       "       'SUBSET', 'KURST', 'KURSX', 'KUR2X', 'KUR3X', 'XMCA',\n",
       "       'RESUBMISSION', '/SAPF15/STATUS', 'PSOTY', 'PSOAK', 'PSOKS',\n",
       "       'PSOSG', 'PSOFN', 'INTFORM', 'INTDATE', 'PSOBT', 'PSOZL', 'PSODT',\n",
       "       'PSOTM', 'FM_UMART', 'CCINS', 'CCNUM', 'SSBLK', 'BATCH', 'SNAME',\n",
       "       'SAMPLED', 'EXCLUDE_FLAG', 'BLIND', 'OFFSET_STATUS',\n",
       "       'OFFSET_REFER_DAT', 'PENRC', 'KNUMV'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "113 columns.  Which are actually useful?  With some additional research and sleuthing, I was able to narrow down the columns in this table to just 5 in particular.  However, before dropping the remainder, I wanted to skim the summary statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>MANDT</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>MONAT</th>\n",
       "      <th>UPDDT</th>\n",
       "      <th>DBBLG</th>\n",
       "      <th>STBLG</th>\n",
       "      <th>STJAH</th>\n",
       "      <th>BKTXT</th>\n",
       "      <th>KZWRS</th>\n",
       "      <th>...</th>\n",
       "      <th>SSBLK</th>\n",
       "      <th>BATCH</th>\n",
       "      <th>SNAME</th>\n",
       "      <th>SAMPLED</th>\n",
       "      <th>EXCLUDE_FLAG</th>\n",
       "      <th>BLIND</th>\n",
       "      <th>OFFSET_STATUS</th>\n",
       "      <th>OFFSET_REFER_DAT</th>\n",
       "      <th>PENRC</th>\n",
       "      <th>KNUMV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3313.0</td>\n",
       "      <td>3.313000e+03</td>\n",
       "      <td>3313.000000</td>\n",
       "      <td>3313.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3313.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>375.0</td>\n",
       "      <td>4.542381e+09</td>\n",
       "      <td>2016.378811</td>\n",
       "      <td>3.835497</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.110655e+09</td>\n",
       "      <td>0.485164</td>\n",
       "      <td>1.500135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>375.0</td>\n",
       "      <td>9.000000e+07</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>4.900000e+09</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>4.900001e+09</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.0</td>\n",
       "      <td>5.000000e+09</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>375.0</td>\n",
       "      <td>5.105601e+09</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MANDT         BELNR        GJAHR        MONAT  UPDDT  DBBLG  STBLG  \\\n",
       "count  3313.0  3.313000e+03  3313.000000  3313.000000    0.0    0.0    0.0   \n",
       "mean    375.0  4.542381e+09  2016.378811     3.835497    NaN    NaN    NaN   \n",
       "std       0.0  1.110655e+09     0.485164     1.500135    NaN    NaN    NaN   \n",
       "min     375.0  9.000000e+07  2016.000000     1.000000    NaN    NaN    NaN   \n",
       "25%     375.0  4.900000e+09  2016.000000     2.000000    NaN    NaN    NaN   \n",
       "50%     375.0  4.900001e+09  2016.000000     5.000000    NaN    NaN    NaN   \n",
       "75%     375.0  5.000000e+09  2017.000000     5.000000    NaN    NaN    NaN   \n",
       "max     375.0  5.105601e+09  2017.000000     5.000000    NaN    NaN    NaN   \n",
       "\n",
       "        STJAH  BKTXT  KZWRS  ...  SSBLK  BATCH  SNAME  SAMPLED  EXCLUDE_FLAG  \\\n",
       "count  3313.0    0.0    0.0  ...    0.0    0.0    0.0      0.0           0.0   \n",
       "mean      0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "std       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "min       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "25%       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "50%       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "75%       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "max       0.0    NaN    NaN  ...    NaN    NaN    NaN      NaN           NaN   \n",
       "\n",
       "       BLIND  OFFSET_STATUS  OFFSET_REFER_DAT  PENRC  KNUMV  \n",
       "count    0.0            0.0               0.0    0.0    0.0  \n",
       "mean     NaN            NaN               NaN    NaN    NaN  \n",
       "std      NaN            NaN               NaN    NaN    NaN  \n",
       "min      NaN            NaN               NaN    NaN    NaN  \n",
       "25%      NaN            NaN               NaN    NaN    NaN  \n",
       "50%      NaN            NaN               NaN    NaN    NaN  \n",
       "75%      NaN            NaN               NaN    NaN    NaN  \n",
       "max      NaN            NaN               NaN    NaN    NaN  \n",
       "\n",
       "[8 rows x 90 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MANDT                 int64\n",
       "BUKRS                object\n",
       "BELNR                 int64\n",
       "GJAHR                 int64\n",
       "BLART                object\n",
       "                     ...   \n",
       "BLIND               float64\n",
       "OFFSET_STATUS       float64\n",
       "OFFSET_REFER_DAT    float64\n",
       "PENRC               float64\n",
       "KNUMV               float64\n",
       "Length: 113, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MANDT, BUKRS, BELNR, and GJAHR are accounted for.  I will need to validate that BLART and USNAM also exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          3313\n",
       "unique           32\n",
       "top       BOETTCHER\n",
       "freq           2048\n",
       "Name: USNAM, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf['USNAM'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>MANDT</th>\n",
       "      <th>BUKRS</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR</th>\n",
       "      <th>BLART</th>\n",
       "      <th>USNAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2673</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5000000310</td>\n",
       "      <td>2017</td>\n",
       "      <td>WE</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>4900001562</td>\n",
       "      <td>2016</td>\n",
       "      <td>WA</td>\n",
       "      <td>BOETTCHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1500000050</td>\n",
       "      <td>2017</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>4900001789</td>\n",
       "      <td>2016</td>\n",
       "      <td>WA</td>\n",
       "      <td>BOETTCHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2990</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5105600302</td>\n",
       "      <td>2017</td>\n",
       "      <td>RE</td>\n",
       "      <td>GBI-022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>4900001167</td>\n",
       "      <td>2016</td>\n",
       "      <td>WA</td>\n",
       "      <td>BOETTCHER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3180</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5105600494</td>\n",
       "      <td>2017</td>\n",
       "      <td>RE</td>\n",
       "      <td>GBI-008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2962</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5105600274</td>\n",
       "      <td>2017</td>\n",
       "      <td>RE</td>\n",
       "      <td>GBI-064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2515</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>5000000152</td>\n",
       "      <td>2017</td>\n",
       "      <td>WE</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>4900000125</td>\n",
       "      <td>2016</td>\n",
       "      <td>WA</td>\n",
       "      <td>BOETTCHER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MANDT BUKRS       BELNR  GJAHR BLART      USNAM\n",
       "2673    375  US00  5000000310   2017    WE    GBI-005\n",
       "1876    375  US00  4900001562   2016    WA  BOETTCHER\n",
       "56      375  US00  1500000050   2017    KZ  GBIFAC-01\n",
       "2103    375  US00  4900001789   2016    WA  BOETTCHER\n",
       "2990    375  US00  5105600302   2017    RE    GBI-022\n",
       "1481    375  US00  4900001167   2016    WA  BOETTCHER\n",
       "3180    375  US00  5105600494   2017    RE    GBI-008\n",
       "2962    375  US00  5105600274   2017    RE    GBI-064\n",
       "2515    375  US00  5000000152   2017    WE    GBI-005\n",
       "439     375  US00  4900000125   2016    WA  BOETTCHER"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bkpf = bkpf[['MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BLART', 'USNAM']]\n",
    "bkpf.sample(10, random_state = 101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BLART is the document type which is our key field of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows:  (0, 6)\n"
     ]
    }
   ],
   "source": [
    "bkpf_dupe = bkpf[bkpf.duplicated()]\n",
    "print(\"Number of duplicate rows: \", bkpf_dupe.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, there are no duplicate rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MANDT    0\n",
      "BUKRS    0\n",
      "BELNR    0\n",
      "GJAHR    0\n",
      "BLART    0\n",
      "USNAM    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(bkpf.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((342, 11), (355, 6))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bseg25 = bseg_stg[bseg_stg['BSCHL'] == 25]\n",
    "bkpfKZ = bkpf[bkpf['BLART'] == 'KZ']\n",
    "\n",
    "bseg25.shape, bkpfKZ.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The measure of interest will be vendor transactions that are debit entries (i.e. outgoing vendor payments), so I left join using the Accounting Document Number in order to retain all 342 transaction line items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>MANDT_x</th>\n",
       "      <th>BUKRS_x</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR_x</th>\n",
       "      <th>BUZEI</th>\n",
       "      <th>BSCHL</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>Digit 1</th>\n",
       "      <th>Digit 2</th>\n",
       "      <th>Digit 3</th>\n",
       "      <th>MANDT_y</th>\n",
       "      <th>BUKRS_y</th>\n",
       "      <th>GJAHR_y</th>\n",
       "      <th>BLART</th>\n",
       "      <th>USNAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>213710</td>\n",
       "      <td>125131</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1068550</td>\n",
       "      <td>125131</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>933153</td>\n",
       "      <td>125135</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1050</td>\n",
       "      <td>125135</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>30300</td>\n",
       "      <td>125131</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBI-005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1000</td>\n",
       "      <td>125107</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1000</td>\n",
       "      <td>125107</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1000</td>\n",
       "      <td>125107</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1000</td>\n",
       "      <td>125107</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1000</td>\n",
       "      <td>125131</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MANDT_x BUKRS_x         BELNR  GJAHR_x  BUZEI  BSCHL    WRBTR   LIFNR  \\\n",
       "0        375    US00  1.500000e+09     2017      2     25   213710  125131   \n",
       "1        375    US00  1.500000e+09     2017      2     25  1068550  125131   \n",
       "2        375    US00  1.500000e+09     2017      2     25   933153  125135   \n",
       "3        375    US00  1.500000e+09     2017      2     25     1050  125135   \n",
       "4        375    US00  1.500000e+09     2017      2     25    30300  125131   \n",
       "..       ...     ...           ...      ...    ...    ...      ...     ...   \n",
       "337      375    US00  1.500000e+09     2017      2     25     1000  125107   \n",
       "338      375    US00  1.500000e+09     2017      2     25     1000  125107   \n",
       "339      375    US00  1.500000e+09     2017      2     25     1000  125107   \n",
       "340      375    US00  1.500000e+09     2017      2     25     1000  125107   \n",
       "341      375    US00  1.500000e+09     2017      2     25     1000  125131   \n",
       "\n",
       "     Digit 1  Digit 2  Digit 3  MANDT_y BUKRS_y  GJAHR_y BLART      USNAM  \n",
       "0          2        1        3    375.0    US00   2017.0    KZ    GBI-005  \n",
       "1          1        0        6    375.0    US00   2017.0    KZ    GBI-005  \n",
       "2          9        3        3    375.0    US00   2017.0    KZ    GBI-005  \n",
       "3          1        0        5    375.0    US00   2017.0    KZ    GBI-005  \n",
       "4          3        0        3    375.0    US00   2017.0    KZ    GBI-005  \n",
       "..       ...      ...      ...      ...     ...      ...   ...        ...  \n",
       "337        1        0        0    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "338        1        0        0    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "339        1        0        0    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "340        1        0        0    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "341        1        0        0      NaN     NaN      NaN   NaN        NaN  \n",
       "\n",
       "[342 rows x 16 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.merge(bseg25, bkpfKZ, on = 'BELNR', how = 'left')\n",
    "merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Methodology"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data has been wrangled and cleansed, I will apply Benford's law to examine the transaction data.  Simply stated, Benford's law is used to analyze the validity and authenticity of financial records.  In naturally occurring distributions, the leading significant sigit will follow a right-skewed distribution.  In other words, the digits have non-equal chances of being the leading digit.  As a limitation, Benford's law should not be used for datasets with constraints such as minimums, maximums, and numbering conventions or structures.  This includes phone numbers, hourly wage rates, and heights for instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.30102999566398114,\n",
       " 0.17609125905568124,\n",
       " 0.1249387366082999,\n",
       " 0.0969100130080564,\n",
       " 0.0791812460476248,\n",
       " 0.06694678963061322,\n",
       " 0.057991946977686726,\n",
       " 0.051152522447381284,\n",
       " 0.04575749056067514]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "benford = [math.log((1+1/d)) / math.log(10) for d in range(1, 10)]\n",
    "d = {\n",
    "    'n': range(1, 10),\n",
    "    'b': benford\n",
    "}\n",
    "benford_predict = pd.DataFrame(d)\n",
    "benford"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output dictates the proportion of leading digits that is expected from a Benford distrbution.  That is, the digit 1 should appear 30% of the time as the first digit in the dataset as opposed to 5% for the digit 9."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to apply the Benford analysis, I will convert the vendor values into a relative count and display the vendor count as a percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digit 1</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.520468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.114035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.038012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.040936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.040936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.032164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.064327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.064327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.084795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Percentage\n",
       "Digit 1            \n",
       "1          0.520468\n",
       "2          0.114035\n",
       "3          0.038012\n",
       "4          0.040936\n",
       "5          0.040936\n",
       "6          0.032164\n",
       "7          0.064327\n",
       "8          0.064327\n",
       "9          0.084795"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor = merged.groupby(['Digit 1'])['Digit 1'].count().reset_index(name='Vendor Count')\n",
    "vendor_Total = vendor.groupby(['Digit 1'])['Vendor Count'].sum().rename('Percentage')\n",
    "vendor_Total\n",
    "\n",
    "vendor_Percentage = vendor_Total / vendor_Total.sum()\n",
    "vendor_Percentage\n",
    "pd.DataFrame(vendor_Percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our exploratory data analysis will be to plot the distributions against each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17560e5ac88>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.set_palette('Paired')\n",
    "\n",
    "fig, (ax1) = plt.subplots(nrows = 1, figsize = (12, 6))\n",
    "sns.barplot(x = 'n', y = 'b', data = benford_predict, color = 'black', facecolor=(1, 1, 1, 0), edgecolor = '0.2', ax = ax1)\n",
    "sns.pointplot(x = 'Digit 1', y = 'Percentage', data = vendor_Percentage.reset_index(), ax = ax1, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After plotting this against the Benford distribution, it appears visually that the digit 1 is a severe deviation and 9 may also be a deviation.  \n",
    "\n",
    "Let's take a look at the vendors in particular."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vendor Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Digit 1</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>Vendor Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>125000.0</td>\n",
       "      <td>11000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>125014.0</td>\n",
       "      <td>148148</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>125098.0</td>\n",
       "      <td>15000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>125101.0</td>\n",
       "      <td>1578116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>125104.0</td>\n",
       "      <td>100058</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>1</td>\n",
       "      <td>125135.0</td>\n",
       "      <td>150500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "      <td>125135.0</td>\n",
       "      <td>16588027</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>1</td>\n",
       "      <td>125135.0</td>\n",
       "      <td>175000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1</td>\n",
       "      <td>125135.0</td>\n",
       "      <td>186760</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>1</td>\n",
       "      <td>125135.0</td>\n",
       "      <td>191250</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Digit 1     LIFNR     WRBTR  Vendor Count\n",
       "0         1  125000.0     11000             1\n",
       "1         1  125014.0    148148             1\n",
       "2         1  125098.0     15000             1\n",
       "3         1  125101.0   1578116             1\n",
       "4         1  125104.0    100058             2\n",
       "..      ...       ...       ...           ...\n",
       "61        1  125135.0    150500             1\n",
       "62        1  125135.0  16588027             1\n",
       "63        1  125135.0    175000             1\n",
       "64        1  125135.0    186760             1\n",
       "65        1  125135.0    191250             1\n",
       "\n",
       "[66 rows x 4 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor1 = merged.groupby(['Digit 1', 'LIFNR', 'WRBTR'])['LIFNR'].count().reset_index(name='Vendor Count')\n",
    "vendor1 = vendor1[vendor1['Digit 1'] == 1]\n",
    "vendor1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to apply the Benford analysis, I will convert the WRBTR amount into a numeric value to sum and display the vendors with the highest amounts.  Recall that this sample space only includes transactions where the leading digit is 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Digit 1           int64\n",
       "LIFNR           float64\n",
       "WRBTR            object\n",
       "Vendor Count      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "vendor1['WRBTR'] = pd.to_numeric(vendor1['WRBTR'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Digit 1  LIFNR   \n",
       "1        125000.0       11000\n",
       "         125014.0      148148\n",
       "         125098.0       15000\n",
       "         125101.0     1578116\n",
       "         125104.0      100058\n",
       "         125105.0        1000\n",
       "         125107.0        1000\n",
       "         125121.0       10000\n",
       "         125131.0    16395978\n",
       "         125134.0        2927\n",
       "         125135.0    31603633\n",
       "Name: Amount, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor_Total = vendor1.groupby(['Digit 1', 'LIFNR'])['WRBTR'].sum().rename('Amount')\n",
    "vendor_Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digit 1</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1</th>\n",
       "      <th>125000.0</th>\n",
       "      <td>0.000221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125014.0</th>\n",
       "      <td>0.002971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125098.0</th>\n",
       "      <td>0.000301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125101.0</th>\n",
       "      <td>0.031647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.002007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125105.0</th>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125107.0</th>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125121.0</th>\n",
       "      <td>0.000201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.328795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.633760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Amount\n",
       "Digit 1 LIFNR             \n",
       "1       125000.0  0.000221\n",
       "        125014.0  0.002971\n",
       "        125098.0  0.000301\n",
       "        125101.0  0.031647\n",
       "        125104.0  0.002007\n",
       "        125105.0  0.000020\n",
       "        125107.0  0.000020\n",
       "        125121.0  0.000201\n",
       "        125131.0  0.328795\n",
       "        125134.0  0.000059\n",
       "        125135.0  0.633760"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendor1_Percentage = vendor_Total / vendor_Total.groupby(level = 0).sum()\n",
    "pd.DataFrame(vendor1_Percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vendor 125135 is responsible for over 60% of the transaction amounts (leading with the digit 1) worth $31 million, so any further investigations may be best suited to begin there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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></th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digit 1</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"11\" valign=\"top\">1</th>\n",
       "      <th>125000.0</th>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125014.0</th>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125098.0</th>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125101.0</th>\n",
       "      <td>0.005618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.011236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125105.0</th>\n",
       "      <td>0.022472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125107.0</th>\n",
       "      <td>0.112360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125121.0</th>\n",
       "      <td>0.016854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.398876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.016854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.398876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">2</th>\n",
       "      <th>125127.0</th>\n",
       "      <td>0.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.051282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.461538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>125107.0</th>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.538462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">7</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125107.0</th>\n",
       "      <td>0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.227273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">9</th>\n",
       "      <th>125104.0</th>\n",
       "      <td>0.137931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125131.0</th>\n",
       "      <td>0.103448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125134.0</th>\n",
       "      <td>0.620690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125135.0</th>\n",
       "      <td>0.137931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Percentage\n",
       "Digit 1 LIFNR               \n",
       "1       125000.0    0.005618\n",
       "        125014.0    0.005618\n",
       "        125098.0    0.005618\n",
       "        125101.0    0.005618\n",
       "        125104.0    0.011236\n",
       "        125105.0    0.022472\n",
       "        125107.0    0.112360\n",
       "        125121.0    0.016854\n",
       "        125131.0    0.398876\n",
       "        125134.0    0.016854\n",
       "        125135.0    0.398876\n",
       "2       125127.0    0.025641\n",
       "        125131.0    0.461538\n",
       "        125134.0    0.051282\n",
       "        125135.0    0.461538\n",
       "3       125107.0    0.076923\n",
       "        125131.0    0.384615\n",
       "        125135.0    0.538462\n",
       "4       125104.0    0.142857\n",
       "        125131.0    0.500000\n",
       "        125135.0    0.357143\n",
       "5       125104.0    0.571429\n",
       "        125131.0    0.285714\n",
       "        125135.0    0.142857\n",
       "6       125104.0    0.363636\n",
       "        125131.0    0.363636\n",
       "        125135.0    0.272727\n",
       "7       125104.0    0.363636\n",
       "        125107.0    0.045455\n",
       "        125131.0    0.227273\n",
       "        125134.0    0.272727\n",
       "        125135.0    0.090909\n",
       "8       125104.0    0.136364\n",
       "        125131.0    0.045455\n",
       "        125134.0    0.818182\n",
       "9       125104.0    0.137931\n",
       "        125131.0    0.103448\n",
       "        125134.0    0.620690\n",
       "        125135.0    0.137931"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendorLIFNR = merged.groupby(['Digit 1', 'LIFNR'])['Digit 1'].count().reset_index(name='Vendor Count')\n",
    "vendorLIFNR_Total = vendorLIFNR.groupby(['Digit 1', 'LIFNR'])['Vendor Count'].sum().rename('Percentage')\n",
    "vendorLIFNR_Total\n",
    "\n",
    "vendorLIFNR_Percentage = vendorLIFNR_Total / vendorLIFNR_Total.groupby(level = 0).sum()\n",
    "pd.DataFrame(vendorLIFNR_Percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x17562135dd8>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1) = plt.subplots(nrows = 1, figsize = (12, 6))\n",
    "\n",
    "sns.barplot(x = 'Digit 1', y = 'Percentage', data = vendorLIFNR_Percentage.reset_index(), ax = ax1, hue = 'LIFNR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After examining the distribution by vendor account, 125134 stands out as it comprises the majority (at over 60%) of the transactions with leading digits of 8 and 9.\n",
    "\n",
    "Next, I will run through the user data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### User Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Digit 1</th>\n",
       "      <th>USNAM</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>User Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>GBI-005</td>\n",
       "      <td>10000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>GBI-005</td>\n",
       "      <td>1010</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>GBI-005</td>\n",
       "      <td>10100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>GBI-005</td>\n",
       "      <td>1010000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>GBI-005</td>\n",
       "      <td>10100000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Digit 1    USNAM     WRBTR  User Count\n",
       "0        1  GBI-005  10000000           1\n",
       "1        1  GBI-005      1010           1\n",
       "2        1  GBI-005     10100           1\n",
       "3        1  GBI-005   1010000           1\n",
       "4        1  GBI-005  10100000           1"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user1 = merged.groupby(['Digit 1', 'USNAM', 'WRBTR'])['USNAM'].count().reset_index(name='User Count')\n",
    "user1 = user1[user1['Digit 1'] == 1]\n",
    "user1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Digit 1        int64\n",
       "USNAM         object\n",
       "WRBTR         object\n",
       "User Count     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user1.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "user1['WRBTR'] = pd.to_numeric(user1['WRBTR'], errors = 'coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Digit 1  USNAM    \n",
       "1        GBI-005      25171866\n",
       "         GBI-026      19661969\n",
       "         GBIFAC-01     3653581\n",
       "Name: Amount, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user1_Total = user1.groupby(['Digit 1', 'USNAM'])['WRBTR'].sum().rename('Amount')\n",
    "user1_Total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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></th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digit 1</th>\n",
       "      <th>USNAM</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.519142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.405507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.075351</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Amount\n",
       "Digit 1 USNAM              \n",
       "1       GBI-005    0.519142\n",
       "        GBI-026    0.405507\n",
       "        GBIFAC-01  0.075351"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_Percentage = user1_Total / user1_Total.sum()\n",
    "user_Percentage\n",
    "pd.DataFrame(user_Percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GBI-005 and GBI-026 are responsible for over 90% of the transaction amounts (leading with the digit 1) worth over $40 million, so any further investigations may be best suited to begin there."
   ]
  },
  {
   "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></th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Digit 1</th>\n",
       "      <th>USNAM</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.152542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.485876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.361582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.435897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.205128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.358974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.230769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.214286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.681818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.318182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">9</th>\n",
       "      <th>GBI-005</th>\n",
       "      <td>0.068966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBI-026</th>\n",
       "      <td>0.206897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GBIFAC-01</th>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Percentage\n",
       "Digit 1 USNAM                \n",
       "1       GBI-005      0.152542\n",
       "        GBI-026      0.485876\n",
       "        GBIFAC-01    0.361582\n",
       "2       GBI-005      0.435897\n",
       "        GBI-026      0.205128\n",
       "        GBIFAC-01    0.358974\n",
       "3       GBI-005      0.153846\n",
       "        GBI-026      0.615385\n",
       "        GBIFAC-01    0.230769\n",
       "4       GBI-026      0.857143\n",
       "        GBIFAC-01    0.142857\n",
       "5       GBI-005      0.071429\n",
       "        GBI-026      0.714286\n",
       "        GBIFAC-01    0.214286\n",
       "6       GBI-005      0.181818\n",
       "        GBI-026      0.727273\n",
       "        GBIFAC-01    0.090909\n",
       "7       GBI-026      0.681818\n",
       "        GBIFAC-01    0.318182\n",
       "8       GBI-026      0.181818\n",
       "        GBIFAC-01    0.818182\n",
       "9       GBI-005      0.068966\n",
       "        GBI-026      0.206897\n",
       "        GBIFAC-01    0.724138"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userUSNAM = merged.groupby(['Digit 1', 'USNAM'])['Digit 1'].count().reset_index(name='User Count')\n",
    "userUSNAM_Total = userUSNAM.groupby(['Digit 1', 'USNAM'])['User Count'].sum().rename('Percentage')\n",
    "userUSNAM_Total\n",
    "\n",
    "userUSNAM_Percentage = userUSNAM_Total / userUSNAM_Total.groupby(level = 0).sum()\n",
    "pd.DataFrame(userUSNAM_Percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x175625e7748>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(nrows = 1, ncols = 2, figsize = (12, 6))\n",
    "\n",
    "sns.barplot(x = 'Digit 1', y = 'Percentage', data = userUSNAM_Percentage.reset_index(), ax = ax1, hue = 'USNAM')\n",
    "#sns.barplot(x = 'Digit 1', y = 'Percentage', data = vendorLIFNR_Percentage.reset_index(), ax = ax2, hue = 'LIFNR')\n",
    "\n",
    "#sns.pointplot(x = 'Digit 1', y = 'Percentage', data = userUSNAM_Percentage.reset_index(), ax = ax1, hue = 'USNAM')\n",
    "sns.pointplot(x = 'Digit 1', y = 'Percentage', data = vendorLIFNR_Percentage.reset_index(), ax = ax2, hue = 'LIFNR', join = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After examining the distribution by user name, GBIFAC-01 stands out as it comprises the majority (at over 70%) of the transactions with leading digits of 8 and 9.  GBI-026 visually stands out as leading the transactions with leading digits of 3 through 7, but recall that our preliminary Benford distribution did not highlight any significant deviations.\n",
    "\n",
    "Let's examine the GBIFAC-01 transactions using the 125134 account."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merged[(merged['LIFNR'] == 125134) & (merged['USNAM'] == 'GBIFAC-01')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3757350.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[(merged['LIFNR'] == 125134) & (merged['USNAM'] == 'GBIFAC-01')]['WRBTR'].astype('float').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 46 transactions worth nearly $4 million to examine.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>MANDT_x</th>\n",
       "      <th>BUKRS_x</th>\n",
       "      <th>BELNR</th>\n",
       "      <th>GJAHR_x</th>\n",
       "      <th>BUZEI</th>\n",
       "      <th>BSCHL</th>\n",
       "      <th>WRBTR</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>Digit 1</th>\n",
       "      <th>Digit 2</th>\n",
       "      <th>Digit 3</th>\n",
       "      <th>MANDT_y</th>\n",
       "      <th>BUKRS_y</th>\n",
       "      <th>GJAHR_y</th>\n",
       "      <th>BLART</th>\n",
       "      <th>USNAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1927</td>\n",
       "      <td>125134</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>27902</td>\n",
       "      <td>125134</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>86241</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>72117</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>81321</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>97473</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>93249</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>89658</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>87776</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>92105</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>79949</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>87602</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>96879</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>91806</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>84991</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>90831</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>93766</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>88338</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>94639</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>83709</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>96412</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>88432</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>71552</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1927</td>\n",
       "      <td>125134</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>27902</td>\n",
       "      <td>125134</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>86241</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>72117</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>81321</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>97473</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>93249</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>89658</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>87776</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>92105</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>79949</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>87602</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>96879</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>91806</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>84991</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>90831</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>93766</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>88338</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>94639</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>83709</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>96412</td>\n",
       "      <td>125134</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>88432</td>\n",
       "      <td>125134</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>375</td>\n",
       "      <td>US00</td>\n",
       "      <td>1.500000e+09</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>71552</td>\n",
       "      <td>125134</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>375.0</td>\n",
       "      <td>US00</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>KZ</td>\n",
       "      <td>GBIFAC-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     MANDT_x BUKRS_x         BELNR  GJAHR_x  BUZEI  BSCHL  WRBTR   LIFNR  \\\n",
       "268      375    US00  1.500000e+09     2017      2     25   1927  125134   \n",
       "269      375    US00  1.500000e+09     2017      2     25  27902  125134   \n",
       "270      375    US00  1.500000e+09     2017      2     25  86241  125134   \n",
       "271      375    US00  1.500000e+09     2017      2     25  72117  125134   \n",
       "272      375    US00  1.500000e+09     2017      2     25  81321  125134   \n",
       "273      375    US00  1.500000e+09     2017      2     25  97473  125134   \n",
       "274      375    US00  1.500000e+09     2017      2     25  93249  125134   \n",
       "275      375    US00  1.500000e+09     2017      2     25  89658  125134   \n",
       "276      375    US00  1.500000e+09     2017      2     25  87776  125134   \n",
       "277      375    US00  1.500000e+09     2017      2     25  92105  125134   \n",
       "278      375    US00  1.500000e+09     2017      2     25  79949  125134   \n",
       "279      375    US00  1.500000e+09     2017      2     25  87602  125134   \n",
       "280      375    US00  1.500000e+09     2017      2     25  96879  125134   \n",
       "281      375    US00  1.500000e+09     2017      2     25  91806  125134   \n",
       "282      375    US00  1.500000e+09     2017      2     25  84991  125134   \n",
       "283      375    US00  1.500000e+09     2017      2     25  90831  125134   \n",
       "284      375    US00  1.500000e+09     2017      2     25  93766  125134   \n",
       "285      375    US00  1.500000e+09     2017      2     25  88338  125134   \n",
       "286      375    US00  1.500000e+09     2017      2     25  94639  125134   \n",
       "287      375    US00  1.500000e+09     2017      2     25  83709  125134   \n",
       "288      375    US00  1.500000e+09     2017      2     25  96412  125134   \n",
       "289      375    US00  1.500000e+09     2017      2     25  88432  125134   \n",
       "290      375    US00  1.500000e+09     2017      2     25  71552  125134   \n",
       "305      375    US00  1.500000e+09     2017      2     25   1927  125134   \n",
       "306      375    US00  1.500000e+09     2017      2     25  27902  125134   \n",
       "307      375    US00  1.500000e+09     2017      2     25  86241  125134   \n",
       "308      375    US00  1.500000e+09     2017      2     25  72117  125134   \n",
       "309      375    US00  1.500000e+09     2017      2     25  81321  125134   \n",
       "310      375    US00  1.500000e+09     2017      2     25  97473  125134   \n",
       "311      375    US00  1.500000e+09     2017      2     25  93249  125134   \n",
       "312      375    US00  1.500000e+09     2017      2     25  89658  125134   \n",
       "313      375    US00  1.500000e+09     2017      2     25  87776  125134   \n",
       "314      375    US00  1.500000e+09     2017      2     25  92105  125134   \n",
       "315      375    US00  1.500000e+09     2017      2     25  79949  125134   \n",
       "316      375    US00  1.500000e+09     2017      2     25  87602  125134   \n",
       "317      375    US00  1.500000e+09     2017      2     25  96879  125134   \n",
       "318      375    US00  1.500000e+09     2017      2     25  91806  125134   \n",
       "319      375    US00  1.500000e+09     2017      2     25  84991  125134   \n",
       "320      375    US00  1.500000e+09     2017      2     25  90831  125134   \n",
       "321      375    US00  1.500000e+09     2017      2     25  93766  125134   \n",
       "322      375    US00  1.500000e+09     2017      2     25  88338  125134   \n",
       "323      375    US00  1.500000e+09     2017      2     25  94639  125134   \n",
       "324      375    US00  1.500000e+09     2017      2     25  83709  125134   \n",
       "325      375    US00  1.500000e+09     2017      2     25  96412  125134   \n",
       "326      375    US00  1.500000e+09     2017      2     25  88432  125134   \n",
       "327      375    US00  1.500000e+09     2017      2     25  71552  125134   \n",
       "\n",
       "     Digit 1  Digit 2  Digit 3  MANDT_y BUKRS_y  GJAHR_y BLART      USNAM  \n",
       "268        1        9        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "269        2        7        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "270        8        6        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "271        7        2        1    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "272        8        1        3    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "273        9        7        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "274        9        3        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "275        8        9        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "276        8        7        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "277        9        2        1    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "278        7        9        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "279        8        7        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "280        9        6        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "281        9        1        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "282        8        4        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "283        9        0        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "284        9        3        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "285        8        8        3    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "286        9        4        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "287        8        3        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "288        9        6        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "289        8        8        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "290        7        1        5    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "305        1        9        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "306        2        7        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "307        8        6        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "308        7        2        1    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "309        8        1        3    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "310        9        7        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "311        9        3        2    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "312        8        9        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "313        8        7        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "314        9        2        1    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "315        7        9        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "316        8        7        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "317        9        6        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "318        9        1        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "319        8        4        9    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "320        9        0        8    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "321        9        3        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "322        8        8        3    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "323        9        4        6    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "324        8        3        7    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "325        9        6        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "326        8        8        4    375.0    US00   2017.0    KZ  GBIFAC-01  \n",
       "327        7        1        5    375.0    US00   2017.0    KZ  GBIFAC-01  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[(merged['LIFNR'] == 125134) & (merged['USNAM'] == 'GBIFAC-01')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The conclusion is that there is evidence of an anomaly within the 2017 SAP BSEG and BKPF tables.  Specifically, the GBIFAC-01 and 125134 transactions should be given additional scrutiny.  For additional analysis, the following tests can be conducted:\n",
    "1. The second digit test\n",
    "2. The leading two digits test\n",
    "3. The leading three digits test\n",
    "4. The trailing two digits test\n",
    "\n",
    "Lastly, with deeper knowledge of the SAP tables, supplementary data analysis could be performed for instance on incoming vendor payments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "## Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. No liability for any errors or omissions\n",
    "\n",
    "The information contained in this report has been provided for information purposes only. This information does not constitute legal, professional or commercial advice. While every care has been taken to ensure that the content is useful and accurate, there are no guarantees, undertakings or warranties in this regard, and any legal liability or responsibility for the content or the accuracy of the information so provided, or, for any loss or damage caused arising directly or indirectly in connection with reliance on the use of such information is forfeited. Any errors or omissions brought to the attention will be corrected as soon as possible.\n",
    "\n",
    "The information in this website may contain technical inaccuracies and typographical errors. The information in this website may be updated from time to time and may at times be out of date.  There is no responsibility for keeping the information in this website up to date or any liability whatsoever for any failure to do so.\n",
    "\n",
    "2. Material on this website does not constitute legal and/or professional advice\n",
    "\n",
    "Any views, opinions and guidance set out in this report are provided for information purposes only, and do not purport to be legal and/or professional advice or a definitive interpretation of any law. Anyone contemplating action in respect of matters set out in this report should obtain advice from a suitably qualified professional adviser based on their unique requirements."
   ]
  },
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   "source": [
    "***\n",
    "\n",
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.leanx.eu/en/sap/table/bseg.html\n",
    "\n",
    "https://www.leanx.eu/en/sap/table/bkpf.html\n",
    "\n",
    "http://sap-ficoexpert.blogspot.com/2011/12/posting-keys.html\n",
    "\n",
    "https://it.toolbox.com/question/fi-document-types-101201"
   ]
  }
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