Introduction.ipynb

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      "source": [
        "# If there's something strange in you accounting department, who you gonna call?"
      ]
    },
    {
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        "id": "65snHCbHUcb6",
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      "cell_type": "markdown",
      "source": [
        "Some initialization to make life easier."
      ]
    },
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      "source": [
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5-x4cevIUcb-",
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      "cell_type": "markdown",
      "source": [
        "[Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics."
      ]
    },
    {
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        "id": "x7culpJSUcb_",
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      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      "metadata": {
        "id": "RvWqILowUccB",
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      "cell_type": "markdown",
      "source": [
        "We have downloaded two tables from the SAP system:\n",
        "1. The line items are stored in the table `BSEG`\n",
        "2. The header information is stored in the table `BKPF`"
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "bseg = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/BSEG.csv',low_memory=False)\n",
        "bkpf = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/BKPF.csv', low_memory=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "Both tables from with huge numbers of columns."
      ]
    },
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      "cell_type": "code",
      "source": [
        "bseg.columns"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BUZEI', 'BUZID',\n",
              "       'AUGDT', 'AUGCP', 'AUGBL',\n",
              "       ...\n",
              "       'PEROP_END', 'FASTPAY', 'IGNR_IVREF', 'FMFGUS_KEY', 'FMXDOCNR',\n",
              "       'FMXYEAR', 'FMXDOCLN', 'FMXZEKKN', 'PRODPER', 'RECRF'],\n",
              "      dtype='object', length=337)"
            ]
          },
          "metadata": {
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          "execution_count": 55
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      "cell_type": "code",
      "source": [
        "bkpf.columns"
      ],
      "execution_count": 57,
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          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'MANDT', 'BUKRS', 'BELNR', 'GJAHR', 'BLART', 'BLDAT',\n",
              "       'BUDAT', 'MONAT', 'CPUDT',\n",
              "       ...\n",
              "       'SSBLK', 'BATCH', 'SNAME', 'SAMPLED', 'EXCLUDE_FLAG', 'BLIND',\n",
              "       'OFFSET_STATUS', 'OFFSET_REFER_DAT', 'PENRC', 'KNUMV'],\n",
              "      dtype='object', length=114)"
            ]
          },
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            "tags": []
          },
          "execution_count": 57
        }
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      "cell_type": "markdown",
      "source": [
        "You can find the definition of all the columns using the transaction code `SE16`."
      ]
    },
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      "metadata": {
        "id": "UOpe_IojXsEd",
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      "cell_type": "markdown",
      "source": [
        "## BKPF"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "For our purposes, we use the following columns from `BKPF`:\n",
        "* `BELNR` - That is the financial document number\n",
        "* `BLART` - That is the document type\n",
        "* `USNAM` - That is the user who posted the financial document"
      ]
    },
    {
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      "cell_type": "code",
      "source": [
        "bkpf = bkpf.loc[:, ['BELNR', 'BLART','USNAM']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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          "timestamp": 1524249594124,
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            "displayName": "Michael Schermann",
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      },
      "cell_type": "code",
      "source": [
        "bkpf.sample(10, random_state=456)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BELNR</th>\n",
              "      <th>BLART</th>\n",
              "      <th>USNAM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>4900000573</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1183</th>\n",
              "      <td>4900000869</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2547</th>\n",
              "      <td>5000000184</td>\n",
              "      <td>WE</td>\n",
              "      <td>GBIFAC-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>690</th>\n",
              "      <td>4900000376</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1280</th>\n",
              "      <td>4900000966</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1500000013</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2778</th>\n",
              "      <td>5000000415</td>\n",
              "      <td>WE</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2266</th>\n",
              "      <td>4900001952</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1013</th>\n",
              "      <td>4900000699</td>\n",
              "      <td>WA</td>\n",
              "      <td>BOETTCHER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3262</th>\n",
              "      <td>1500000304</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBIFAC-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           BELNR BLART      USNAM\n",
              "887   4900000573    WA  BOETTCHER\n",
              "1183  4900000869    WA  BOETTCHER\n",
              "2547  5000000184    WE  GBIFAC-01\n",
              "690   4900000376    WA  BOETTCHER\n",
              "1280  4900000966    WA  BOETTCHER\n",
              "19    1500000013    KZ    GBI-005\n",
              "2778  5000000415    WE    GBI-005\n",
              "2266  4900001952    WA  BOETTCHER\n",
              "1013  4900000699    WA  BOETTCHER\n",
              "3262  1500000304    KZ  GBIFAC-01"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 59
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          "timestamp": 1524249596870,
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            "displayName": "Michael Schermann",
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      },
      "cell_type": "code",
      "source": [
        "bkpf['BLART'].unique()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['RV', 'DZ', 'KZ', 'KG', 'WA', 'WL', 'WE', 'RE'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
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      "metadata": {
        "id": "SpEOhp6QUccX",
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      "cell_type": "markdown",
      "source": [
        "Interesting for us is the document type `KZ`, which represents vendor payments."
      ]
    },
    {
      "metadata": {
        "id": "oD6pg6DIUccY",
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      "cell_type": "code",
      "source": [
        "bkpf = bkpf[bkpf['BLART']=='KZ']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bXMu-LGAXwZ7",
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      "cell_type": "markdown",
      "source": [
        "## BSEG"
      ]
    },
    {
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      "cell_type": "markdown",
      "source": [
        "We use the following columns from `BSEG`:\n",
        "* `BELNR` - That is the financial document number\n",
        "* `BSCHL` - That is the posting key\n",
        "* `WRBTR` - That is the amount posted\n",
        "* `LIFNR` - That is vendor account"
      ]
    },
    {
      "metadata": {
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        "colab": {
          "autoexec": {
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        }
      },
      "cell_type": "code",
      "source": [
        "bseg = bseg.loc[:, ['BELNR', 'BSCHL', 'WRBTR', 'LIFNR']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
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        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249600014,
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            "displayName": "Michael Schermann",
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      },
      "cell_type": "code",
      "source": [
        "bseg.sample(10, random_state=456)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BELNR</th>\n",
              "      <th>BSCHL</th>\n",
              "      <th>WRBTR</th>\n",
              "      <th>LIFNR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1,000.00</td>\n",
              "      <td>125131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>50</td>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>1.700000e+09</td>\n",
              "      <td>35</td>\n",
              "      <td>1,960,000.00</td>\n",
              "      <td>125088.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>50</td>\n",
              "      <td>1,010,000.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2737</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>50</td>\n",
              "      <td>1927.48</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>50</td>\n",
              "      <td>1,000,000.00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2527</th>\n",
              "      <td>5.105600e+09</td>\n",
              "      <td>31</td>\n",
              "      <td>96,000.00</td>\n",
              "      <td>125108.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2808</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1,000.00</td>\n",
              "      <td>125107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>943</th>\n",
              "      <td>5.000000e+09</td>\n",
              "      <td>96</td>\n",
              "      <td>63,830.00</td>\n",
              "      <td>125018.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1,000.00</td>\n",
              "      <td>125131.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             BELNR  BSCHL         WRBTR     LIFNR\n",
              "559   1.500000e+09     25      1,000.00  125131.0\n",
              "235   1.500000e+09     50             5       NaN\n",
              "619   1.700000e+09     35  1,960,000.00  125088.0\n",
              "32    1.500000e+09     50  1,010,000.00       NaN\n",
              "2737  1.500000e+09     50       1927.48       NaN\n",
              "425   1.500000e+09     50  1,000,000.00       NaN\n",
              "2527  5.105600e+09     31     96,000.00  125108.0\n",
              "2808  1.500000e+09     25      1,000.00  125107.0\n",
              "943   5.000000e+09     96     63,830.00  125018.0\n",
              "557   1.500000e+09     25      1,000.00  125131.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
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        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249602410,
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            "displayName": "Michael Schermann",
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      },
      "cell_type": "code",
      "source": [
        "bseg['BSCHL'].unique()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([50, 25, 40, 35, 89, 96, 86, 99, 81, 91, 31, 83, 93, 21])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "metadata": {
        "id": "qN8p8LhlUccs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Interesting for us is the posting key `25`, which represents the debit posting on the vendor account."
      ]
    },
    {
      "metadata": {
        "id": "krdWy19BUccu",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg = bseg[bseg['BSCHL']==25]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "M4H1oJJLUccy",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "87f5a3fa-3c5e-454d-ab98-f2e4304527f6",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249605604,
          "user_tz": 420,
          "elapsed": 592,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg.dtypes"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BELNR    float64\n",
              "BSCHL      int64\n",
              "WRBTR     object\n",
              "LIFNR    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "metadata": {
        "id": "sfslWgKpYAxL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We need to fix the posting value."
      ]
    },
    {
      "metadata": {
        "id": "a3jVSvr7Ucc1",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg['WRBTR'] = bseg['WRBTR'].str.replace(',','')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tucKeOO1Ucc4",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cd6ed536-7b49-4c76-ceaa-56ed22650b97",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249607614,
          "user_tz": 420,
          "elapsed": 550,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg.head()"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>BELNR</th>\n",
              "      <th>BSCHL</th>\n",
              "      <th>WRBTR</th>\n",
              "      <th>LIFNR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>213710.00</td>\n",
              "      <td>125131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1068550.00</td>\n",
              "      <td>125131.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>933153.41</td>\n",
              "      <td>125135.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1050.00</td>\n",
              "      <td>125135.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>30300.00</td>\n",
              "      <td>125131.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          BELNR  BSCHL       WRBTR     LIFNR\n",
              "1  1.500000e+09     25   213710.00  125131.0\n",
              "3  1.500000e+09     25  1068550.00  125131.0\n",
              "5  1.500000e+09     25   933153.41  125135.0\n",
              "7  1.500000e+09     25     1050.00  125135.0\n",
              "9  1.500000e+09     25    30300.00  125131.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "metadata": {
        "id": "U2DcMmpQX1O4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## First digit of the posting value"
      ]
    },
    {
      "metadata": {
        "id": "fg3UyGDYUcc8",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg['first'] = bseg['WRBTR'].str[0:1].astype(int)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "YYBO3yzGYGvC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## How many documents do we have per vendor?"
      ]
    },
    {
      "metadata": {
        "id": "yWn1AE7uUcc-",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "0c7b2386-82e0-40a4-fd07-218dd3fbf912",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249609741,
          "user_tz": 420,
          "elapsed": 272,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vendors = bseg.groupby('LIFNR').size().sort_values(ascending=False)\n",
        "vendors"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LIFNR\n",
              "125131.0    122\n",
              "125135.0    118\n",
              "125134.0     47\n",
              "125104.0     31\n",
              "125107.0     22\n",
              "125105.0      4\n",
              "125121.0      3\n",
              "125127.0      1\n",
              "125101.0      1\n",
              "125098.0      1\n",
              "125070.0      1\n",
              "125014.0      1\n",
              "125000.0      1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "metadata": {
        "id": "9KuWsOHXUcdE",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "4e84c517-e461-4f27-c5e1-e7f4f6e8379d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249611335,
          "user_tz": 420,
          "elapsed": 309,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vendors = vendors[vendors > 20]\n",
        "vendors"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LIFNR\n",
              "125131.0    122\n",
              "125135.0    118\n",
              "125134.0     47\n",
              "125104.0     31\n",
              "125107.0     22\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "metadata": {
        "id": "XvIEnA-RUcdI",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2c7597ba-7221-4cec-eb49-653c445fb98e",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249612308,
          "user_tz": 420,
          "elapsed": 487,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vendors.index"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Float64Index([125131.0, 125135.0, 125134.0, 125104.0, 125107.0], dtype='float64', name='LIFNR')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "metadata": {
        "id": "JOo-OdG9UcdO",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg = bseg[bseg['LIFNR'].isin(vendors.index)]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3VNxNDQtUcdP",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "80583663-3386-46ec-896d-6c8b48bdc078",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249977509,
          "user_tz": 420,
          "elapsed": 489,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "bseg.head()\n",
        "bkpf.head()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>BELNR</th>\n",
              "      <th>BSCHL</th>\n",
              "      <th>WRBTR</th>\n",
              "      <th>LIFNR</th>\n",
              "      <th>first</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>213710.00</td>\n",
              "      <td>125131.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1068550.00</td>\n",
              "      <td>125131.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>933153.41</td>\n",
              "      <td>125135.0</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>1050.00</td>\n",
              "      <td>125135.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.500000e+09</td>\n",
              "      <td>25</td>\n",
              "      <td>30300.00</td>\n",
              "      <td>125131.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          BELNR  BSCHL       WRBTR     LIFNR  first\n",
              "1  1.500000e+09     25   213710.00  125131.0      2\n",
              "3  1.500000e+09     25  1068550.00  125131.0      1\n",
              "5  1.500000e+09     25   933153.41  125135.0      9\n",
              "7  1.500000e+09     25     1050.00  125135.0      1\n",
              "9  1.500000e+09     25    30300.00  125131.0      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "execute_result",
          "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>BELNR</th>\n",
              "      <th>BLART</th>\n",
              "      <th>USNAM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1500000000</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1500000001</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1500000002</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1500000003</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1500000004</td>\n",
              "      <td>KZ</td>\n",
              "      <td>GBI-005</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         BELNR BLART    USNAM\n",
              "6   1500000000    KZ  GBI-005\n",
              "7   1500000001    KZ  GBI-005\n",
              "8   1500000002    KZ  GBI-005\n",
              "9   1500000003    KZ  GBI-005\n",
              "10  1500000004    KZ  GBI-005"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "metadata": {
        "id": "66jWcphRYRnT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Integrated dataset"
      ]
    },
    {
      "metadata": {
        "id": "-PEP2gFEUcdS",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vp = pd.merge(bseg, bkpf, how='left', on='BELNR')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2E6nhLcfUcdU",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6e45afac-686b-4aa6-afb8-c589866a6108",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249617326,
          "user_tz": 420,
          "elapsed": 432,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "ben_vp_vendor = vp.groupby(['first', 'LIFNR'])['first'].count().reset_index(name='count')\n",
        "ben_vp_vendor.head()"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>first</th>\n",
              "      <th>LIFNR</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>125104.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>125107.0</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>125131.0</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>125134.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>125135.0</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   first     LIFNR  count\n",
              "0      1  125104.0      2\n",
              "1      1  125107.0     20\n",
              "2      1  125131.0     72\n",
              "3      1  125134.0      3\n",
              "4      1  125135.0     74"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "metadata": {
        "id": "_hNnjA1PUcdX",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "50c903d6-507a-4d68-e79b-0a0b822a84bd",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249618383,
          "user_tz": 420,
          "elapsed": 293,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "ben_vp_accountant = vp.groupby(['first', 'USNAM'])['first'].count().reset_index(name='count')\n",
        "ben_vp_accountant.head()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>first</th>\n",
              "      <th>USNAM</th>\n",
              "      <th>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>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>GBI-026</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>GBIFAC-01</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>GBI-005</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>GBI-026</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   first      USNAM  count\n",
              "0      1    GBI-005     28\n",
              "1      1    GBI-026     83\n",
              "2      1  GBIFAC-01     59\n",
              "3      2    GBI-005     17\n",
              "4      2    GBI-026      7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "metadata": {
        "id": "loHESQOWYXVC",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Benford Analysis"
      ]
    },
    {
      "metadata": {
        "id": "LLmqxE51Ucda",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import math\n",
        "benford = [math.log10(1 + 1 / d)*100 for d in range(1,10)]\n",
        "d = {'n': range(1,10), 'b': benford}\n",
        "ben_predict = pd.DataFrame(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dICDOv9kUcdc",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "lYru8t31Ycf4",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Posting value by vendor"
      ]
    },
    {
      "metadata": {
        "id": "FZzhAMPpUcdd",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "48a9aa88-d9bb-418f-bb5f-d21e111b081b",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249676890,
          "user_tz": 420,
          "elapsed": 666,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "sns.pointplot(x='first', y='count', hue='LIFNR',data=ben_vp_vendor)\n",
        "sns.pointplot(x='n', y='b' ,data=ben_predict, color='black');"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Hjpp3bgHelWX5AIHgfqBpfQ5wBpAN/CbMvo8DNwFXNC3bWjXnlAND2t3rEGFJ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\nh0jSF0KIHCJJXwghcsj/A4w/t8UnN8v9AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9bf49787f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "PZE_firLYhN5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Posting value by accountant"
      ]
    },
    {
      "metadata": {
        "id": "i2uY7X8aUcdg",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "cbcd993b-6a9a-48b8-d2a8-6c751dfd9302",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249680197,
          "user_tz": 420,
          "elapsed": 617,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.pointplot(x='first', y='count', hue='USNAM',data=ben_vp_accountant)\n",
        "sns.pointplot(x='n', y='b' ,data=ben_predict, color='black');"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J0gZZlh3AVZIk9a2vvCzL18qyPFqW5XHAQtyjd9binlidqp/nRB5+d+bNsZ7l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\nvAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f9bf4273278>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "0azj-0PzYkzd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Who is this GBIFAC-01 and what happened?"
      ]
    },
    {
      "metadata": {
        "id": "ktMKB4niUcdj",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e37b5316-3b04-41f3-eeae-7631d637fa2d",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249690104,
          "user_tz": 420,
          "elapsed": 631,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "len(vp[(vp['USNAM']=='GBIFAC-01') & (vp['first'].isin([7,8,9])) & (vp['LIFNR'] != 125134)])"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "metadata": {
        "id": "CWz4GK4PXJMz",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2187a4be-08c6-4f16-8325-0c391452cb12",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249709799,
          "user_tz": 420,
          "elapsed": 322,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "len(vp[(vp['USNAM']=='GBIFAC-01') & (vp['first'].isin([7,8,9])) & (vp['LIFNR'] == 125134)])"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "metadata": {
        "id": "35js37GJXKoV",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d34f323f-12b2-4497-8465-b21018946106",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249710634,
          "user_tz": 420,
          "elapsed": 334,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "len(vp[(vp['USNAM']=='GBIFAC-01') & ~(vp['first'].isin([7,8,9])) & (vp['LIFNR'] == 125134)])"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "metadata": {
        "id": "XJG3DJvoXL9r",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "36f454c7-c663-4c46-9ec4-e48f322d60f3",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249711857,
          "user_tz": 420,
          "elapsed": 423,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "len(vp[(vp['USNAM']=='GBIFAC-01')])"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "metadata": {
        "id": "2X6GX2MoUcdm",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "56d3f29a-b295-45fd-8dad-99bf759f3749",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249732411,
          "user_tz": 420,
          "elapsed": 289,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vp[(vp['USNAM']=='GBIFAC-01') & (vp['first'].isin([7,8,9])) & (vp['LIFNR'] == 125134)]['WRBTR'].astype(float).sum()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3697715.58"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "metadata": {
        "id": "t6ypLYXMUcdp",
        "colab_type": "code",
        "colab": {
          "autoexec": {
            "startup": false,
            "wait_interval": 0
          },
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "069ec5e0-a057-4730-895f-c26ac06027da",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1524249745966,
          "user_tz": 420,
          "elapsed": 286,
          "user": {
            "displayName": "Michael Schermann",
            "photoUrl": "//lh3.googleusercontent.com/-sXyTj9V4mk8/AAAAAAAAAAI/AAAAAAAAAD4/JcGCCFE3xt8/s50-c-k-no/photo.jpg",
            "userId": "115913240975069260630"
          }
        }
      },
      "cell_type": "code",
      "source": [
        "vp[(vp['USNAM']=='GBIFAC-01') & ~(vp['first'].isin([7,8,9])) & (vp['LIFNR'] == 125134)]['WRBTR'].astype(float).sum()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59659.58"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "metadata": {
        "id": "EH7-zWmnXgYs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**We need to talk to GBIFAC-01!**"
      ]
    }
  ]
}