JET.ipynb

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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Journal Entry Test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926cb63c",
   "metadata": {},
   "source": [
    "This is a template jupyter notebook to perform journal entry tests upon request of the financial audit team. \n",
    "\n",
    "## Instructions\n",
    "\n",
    "Make sure that you are not saving this repository on a network drive, as this may cause issues with the virtual environment because the resolved network path may differ from the path used to create the virtual environment. \n",
    "\n",
    "First of all, you need to download the journal entry test template from the [JET repository](https://github.com/tlex-web/journal-entry-tests.git) and save it to your local machine. To do so, run the following command:\n",
    "\n",
    "```bash\n",
    "git clone https://github.com/tlex-web/journal-entry-tests.git\n",
    "```\n",
    "\n",
    "You need to create a virtual environment for python using the `venv` module. To do so, open a command prompt and run the following command, assuming you are in the root folder of the JET repository and have at least Python 3.10 installed on your machine:\n",
    "\n",
    "```bash\n",
    "python.exe -m venv venv\n",
    "```\n",
    "\n",
    "Once the virtual environment is created, you need to activate it. To do so, run the following command:\n",
    "\n",
    "```bash\n",
    "\\venv\\scripts\\activate.bat\n",
    "```\n",
    "\n",
    "Once the virtual environment is activated, you need to install the dependencies. To do so, run the following command:\n",
    "\n",
    "```bash\n",
    "pip install -r requirements.txt\n",
    "```\n",
    "\n",
    "You can now run the notebook. To do so, run the following command:\n",
    "\n",
    "```bash\n",
    "jupyter-lab\n",
    "```\n",
    "\n",
    "Once the notebook is running, you can open the notebook by clicking on the `JET.ipynb` file in the file browser.\n",
    "\n",
    "**_or_** \n",
    "\n",
    "You can also open and run the notebook directly in Visual Studio Code. To do so, open the `JET.ipynb` file in Visual Studio Code and click on the `Run Cell` button in the top right corner of the notebook.\n",
    "\n",
    "\n",
    "## Input\n",
    "\n",
    "The following information is required to run the journal entry test:\n",
    "\n",
    "* **Journal Entry Test Date**: The date the journal entry test was performed.\n",
    "* **Journal Entry Test Performed By**: The name of the person who performed the journal entry test.\n",
    "* **Journal Entry Test Reviewed By**: The name of the person who reviewed the journal entry test.\n",
    "* **Journal Entry Test Reviewed Date**: The date the journal entry test was reviewed.\n",
    "* **General Ledger**: The general ledger of the company.\n",
    "* **Trial Balance**: The trial balance of the company.\n",
    "\n",
    "The `General Ledger` and `Trial Balance` files must be in CSV format. The `General Ledger` file must at least contain the following columns:\n",
    "\n",
    "* **Account**: The account number.\n",
    "* **Account Description**: The account description.\n",
    "* **Debit**: The debit amount.\n",
    "* **Credit**: The credit amount.\n",
    "* **Date**: The date of the transaction.\n",
    "* **Journal Entry**: The journal entry number.\n",
    "* **Journal Entry Description**: The journal entry description.\n",
    "\n",
    "The `Trial Balance` file must at least contain the following columns:\n",
    "\n",
    "* **Account**: The account number.\n",
    "* **Account Description**: The account description.\n",
    "* **Debit**: The debit amount.\n",
    "* **Credit**: The credit amount.\n",
    "\n",
    "The `General Ledger` and `Trial Balance` files must be saved in the `data` folder. The `General Ledger` file must be named `general_ledger.csv` and the `Trial Balance` file must be named `trial_balance.csv`.\n",
    "\n",
    "## Output\n",
    "\n",
    "The notebook is designed to automatically generate the following file structure:\n",
    "\n",
    "```\n",
    ".\n",
    "├── JET.ipynb\n",
    "├── JET.pdf\n",
    "├── JET.html\n",
    "├── /data/\n",
    "    ├── /data/general__ledger.csv\n",
    "    ├── /data/trial_balance.csv\n",
    "    ├── /data/data.csv\n",
    "├── /helpers/helpers.py\n",
    "└── /results/\n",
    "    ├── /JB0/results.xlsx | no_result.txt\n",
    "    ├── /JB1/results.xlsx | no_result.txt\n",
    "    ├── /JB2/results.xlsx | no_result.txt\n",
    "    ├── /JB3/results.xlsx | no_result.txt\n",
    "    ├── /JB4/results.xlsx | no_result.txt\n",
    "    ├── /JB5/results.xlsx | no_result.txt\n",
    "    ├── /JB6/results.xlsx | no_result.txt\n",
    "    ├── /JB7/results.xlsx | no_result.txt\n",
    "    ├── /JB8/results.xlsx | no_result.txt\n",
    "    ├── /JB9/results.xlsx | no_result.txt\n",
    "    └── /JB10/results.xlsx | no_result.txt\n",
    "``` \n",
    "\n",
    "The `JET.ipynb` notebook is the template notebook that you downloaded from the JET repository. The `JET.pdf` and `JET.html` files are copies of the notebook in PDF and HTML format. The `data` folder contains the data used to perform the journal entry test. The `results` folder contains the results of the journal entry test. The results are stored in Excel format. If no results are found for a journal entry test, a `no_result.txt` file is created in the corresponding journal entry test folder. The `helpers.py` file contains the helper functions used to perform the journal entry test.\n",
    "\n",
    "## Dependencies\n",
    "\n",
    "The following dependencies are required to run the journal entry test:\n",
    "\n",
    "* [Python 3.10](https://www.python.org/downloads/release/python-310/)\n",
    "* [Jupyter Notebook latests release](https://jupyter.org/)\n",
    "* [Pandas latests release](https://pandas.pydata.org/)\n",
    "* [Numpy latests release](http://www.numpy.org/)\n",
    "* [Plotly latests release](https://plotly.com/)\n",
    "* [Matplotlib latests release](https://matplotlib.org/)\n",
    "* [Seaborn latests release](https://seaborn.pydata.org/)\n",
    "* [XlsxWriter latests release](https://xlsxwriter.readthedocs.io/)\n",
    "\n",
    "Please note that the notebook is designed to automatically install the required dependencies if they are not already installed on your machine. In case the automatic installation fails, please install the dependencies manually using the `pip install` command to install the dependencies listed in the `requirements.txt` lock file."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ec9b3670-bfe9-4fdf-8660-0678dd88363d",
   "metadata": {},
   "source": [
    "## Import and Sanitization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e34646d6-ae54-4772-be81-be80e1bde3be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.24.1.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load required packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import plotly.subplots as sp\n",
    "import plotly.offline\n",
    "import seaborn as sns\n",
    "import missingno as msno\n",
    "\n",
    "import os\n",
    "import json\n",
    "import re\n",
    "\n",
    "# offline plot setting\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b415fa96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load internal libraries\n",
    "from helpers.helpers import get_holidays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b876913",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create data folder if not exists\n",
    "if not os.path.exists('data'):\n",
    "    os.makedirs('data')\n",
    "\n",
    "with os.scandir('data') as entries:\n",
    "    if not any(entries):\n",
    "        with open('place GL and TB csv files here.txt', 'w') as f:\n",
    "            f.write('')\n",
    "\n",
    "# create results folder and all subfolders for the JET tests if they do not yet exists\n",
    "if not os.path.exists('results'):\n",
    "    os.makedirs('results')\n",
    "\n",
    "for i in range(0, 11):\n",
    "    if not os.path.exists('results/JB' + str(i)):\n",
    "        os.makedirs('results/JB' + str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25c808fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# constants\n",
    "class bcolors:\n",
    "    HEADER = '\\033[95m'\n",
    "    OKBLUE = '\\033[94m'\n",
    "    OKCYAN = '\\033[96m'\n",
    "    OKGREEN = '\\033[92m'\n",
    "    WARNING = '\\033[93m'\n",
    "    FAIL = '\\033[91m'\n",
    "    ENDC = '\\033[0m'\n",
    "    BOLD = '\\033[1m'\n",
    "    UNDERLINE = '\\033[4m'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dbf1d673",
   "metadata": {},
   "outputs": [],
   "source": [
    "# global variables\n",
    "jet_execution_date = '2021-12-31'\n",
    "jet_execution_person = 'John Doe'\n",
    "jet_execution_review_date = '2022-01-01'\n",
    "jet_execution_reviewer = 'Jane Doe'\n",
    "\n",
    "# notebook variables\n",
    "relative_path = './data/'\n",
    "filename_gl = relative_path + 'general_ledger.csv' # Please update if you want to use a different file\n",
    "filename_tb = relative_path + 'trial_balance.csv' # Please update if you want to use a different file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b134b24",
   "metadata": {},
   "source": [
    "Please remember to change the text encoding to `UTF-8` when importing the `General Ledger` and `Trial Balance` files. This is to ensure that the files are imported correctly. \n",
    "You can do so by opening the files in a text editor such as [Notepad++](https://notepad-plus-plus.org/downloads/) or the [Notepad](https://apps.microsoft.com/store/detail/windows-notepad/9MSMLRH6LZF3) and changing the encoding to `UTF-8` in the `Encoding` menu while saving the files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c24501b7-b9bc-4769-bba3-bcaad5f38818",
   "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>Buch'nr.</th>\n",
       "      <th>Zeilenr.</th>\n",
       "      <th>Belegdat.</th>\n",
       "      <th>Belegnr.</th>\n",
       "      <th>Sollkto.</th>\n",
       "      <th>S'betrag EUR</th>\n",
       "      <th>S'betrag EUR.1</th>\n",
       "      <th>Habenkto.</th>\n",
       "      <th>H'betrag EUR</th>\n",
       "      <th>Buch'text</th>\n",
       "      <th>Währung</th>\n",
       "      <th>Benutzer</th>\n",
       "      <th>Buch'datum</th>\n",
       "      <th>Belegherkunft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>483476</td>\n",
       "      <td>1</td>\n",
       "      <td>01.01.2021</td>\n",
       "      <td>R02289401 2022</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>51.86</td>\n",
       "      <td>51.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lalux - Leudelange</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>483476</td>\n",
       "      <td>2</td>\n",
       "      <td>01.01.2021</td>\n",
       "      <td>R02289401 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72072.0</td>\n",
       "      <td>51.86</td>\n",
       "      <td>lalux - Leudelange</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>02/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>483476</td>\n",
       "      <td>3</td>\n",
       "      <td>01.01.2021</td>\n",
       "      <td>R02289401 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>51.86</td>\n",
       "      <td>lalux - Leudelange</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483931</td>\n",
       "      <td>1</td>\n",
       "      <td>06.06.2021</td>\n",
       "      <td>3022100219105-01</td>\n",
       "      <td>4138.0</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483931</td>\n",
       "      <td>2</td>\n",
       "      <td>06.06.2021</td>\n",
       "      <td>3022100219105-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71781.0</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>05/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buch'nr.  Zeilenr.   Belegdat.          Belegnr.  Sollkto.  S'betrag EUR  \\\n",
       "0    483476         1  01.01.2021    R02289401 2022    4360.0         51.86   \n",
       "1    483476         2  01.01.2021    R02289401 2022       NaN           NaN   \n",
       "2    483476         3  01.01.2021    R02289401 2022       NaN           NaN   \n",
       "3    483931         1  06.06.2021  3022100219105-01    4138.0       1533.08   \n",
       "4    483931         2  06.06.2021  3022100219105-01       NaN           NaN   \n",
       "\n",
       "   S'betrag EUR.1  Habenkto.  H'betrag EUR            Buch'text Währung  \\\n",
       "0           51.86        NaN           NaN   lalux - Leudelange     EUR   \n",
       "1             NaN    72072.0         51.86   lalux - Leudelange     EUR   \n",
       "2             NaN     1600.0         51.86   lalux - Leudelange     EUR   \n",
       "3         1533.08        NaN           NaN  BG RCI - Heidelberg     EUR   \n",
       "4             NaN    71781.0       1533.08  BG RCI - Heidelberg     EUR   \n",
       "\n",
       "  Benutzer  Buch'datum Belegherkunft  \n",
       "0     hess  01/01/2022  stapelbuchen  \n",
       "1     hess  02/01/2022  stapelbuchen  \n",
       "2     hess  03/01/2022  stapelbuchen  \n",
       "3     hess  04/01/2022  stapelbuchen  \n",
       "4     hess  05/01/2022  stapelbuchen  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data import \n",
    "# set the appropriate decimal and separator characters\n",
    "df_gl = pd.read_csv(filename_gl, sep = \";\", decimal = \",\")\n",
    "df_tb = pd.read_csv(filename_tb, sep = \";\", decimal = \",\")\n",
    "\n",
    "# check if all columns are correctly imported and the floating point numbers are correctly interpreted\n",
    "df_gl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b200eb09-53a0-4b6d-95e8-24a51942489d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Buch'nr.            int64\n",
       " Zeilenr.            int64\n",
       " Belegdat.          object\n",
       " Belegnr.           object\n",
       " Sollkto.          float64\n",
       " S'betrag EUR      float64\n",
       " S'betrag EUR.1    float64\n",
       " Habenkto.         float64\n",
       " H'betrag EUR      float64\n",
       " Buch'text          object\n",
       " Währung            object\n",
       " Benutzer           object\n",
       " Buch'datum         object\n",
       " Belegherkunft      object\n",
       " dtype: object,\n",
       " Nr.              int64\n",
       " Bezeichnung     object\n",
       " Soll           float64\n",
       " Haben          float64\n",
       " Soll2          float64\n",
       " Haben3         float64\n",
       " Soll4          float64\n",
       " Haben5         float64\n",
       " Soll6          float64\n",
       " Haben7         float64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types \n",
    "df_gl.dtypes, df_tb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "508f2d79-6535-43e7-baa7-25071c77faa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(booking_nr         category\n",
       " line_nr               int64\n",
       " document_date        object\n",
       " document_nr          object\n",
       " debit_account      category\n",
       " debit_amount        float64\n",
       " s'betrag eur.1      float64\n",
       " credit_account     category\n",
       " credit_amount       float64\n",
       " description          object\n",
       " currency             object\n",
       " user                 object\n",
       " booking_date         object\n",
       " document_source      object\n",
       " dtype: object,\n",
       " kontonummer         category\n",
       " kontobezeichnung      object\n",
       " soll_ob              float64\n",
       " haben_ob             float64\n",
       " soll_periode_13      float64\n",
       " haben_periode_13     float64\n",
       " soll_periode_12      float64\n",
       " haben_periode_12     float64\n",
       " soll_saldo           float64\n",
       " haben_saldo          float64\n",
       " dtype: object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanitize data \n",
    "# change column names and data types in place \n",
    "# do so for both dataframes and all columns that need to be changed\n",
    "\n",
    "def sanitize_gl(df: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    # lowercase column names\n",
    "    df.columns = df.columns.str.lower()\n",
    "\n",
    "    # rename columns\n",
    "    df = df.rename(columns={\n",
    "    \"buch'nr.\": \"booking_nr\",\n",
    "    \"buch'datum\": \"booking_date\",\n",
    "    \"belegherkunft\": \"document_source\",\n",
    "    \"benutzer\": \"user\",\n",
    "    \"zeilenr.\": \"line_nr\",\n",
    "    \"belegdat.\": \"document_date\",\n",
    "    \"belegnr.\": \"document_nr\",\n",
    "    \"betrag\": \"amount\",\n",
    "    \"währung\": \"currency\",\n",
    "    \"buch'text\": \"description\",\n",
    "    \"sollkto.\": \"debit_account\",\n",
    "    \"habenkto.\": \"credit_account\",\n",
    "    \"s'betrag eur\": \"debit_amount\",\n",
    "    \"h'betrag eur\": \"credit_amount\",\n",
    "    })\n",
    "\n",
    "    # cast data types \n",
    "\n",
    "    # numbers \n",
    "    df[\"booking_nr\"] = df[\"booking_nr\"].astype(\"Int64\") # remove the trailling commas\n",
    "    df[\"booking_nr\"] = df[\"booking_nr\"].astype('category')\n",
    "    \n",
    "    # dates\n",
    "    df[\"document_date\"] = pd.to_datetime(df[\"document_date\"], format=\"mixed\", errors=\"coerce\")\n",
    "\n",
    "    # strings \n",
    "    df[\"document_date\"] = df[\"document_date\"].astype(str)\n",
    "    df[\"description\"] = df[\"description\"].astype(str)\n",
    "    df[\"currency\"] = df[\"currency\"].astype(str)\n",
    "\n",
    "    # category\n",
    "    df[\"credit_account\"] = df[\"credit_account\"].astype(\"Int64\") # remove the trailling commas\n",
    "    df[\"credit_account\"] = df[\"credit_account\"].astype(\"category\")\n",
    "    df[\"debit_account\"] = df[\"debit_account\"].astype(\"Int64\")\n",
    "    df[\"debit_account\"] = df[\"debit_account\"].astype(\"category\")\n",
    "    \n",
    "    return df\n",
    "\n",
    "def sanitize_tb(df: pd.DataFrame) -> pd.DataFrame:\n",
    "\n",
    "    # lowercase column names\n",
    "    df.columns = df.columns.str.lower()\n",
    "\n",
    "    # rename columns\n",
    "    df = df.rename(columns={\n",
    "    \"nr.\": \"kontonummer\",\n",
    "    \"bezeichnung\": \"kontobezeichnung\",\n",
    "    \"soll\": \"soll_ob\",\n",
    "    \"haben\": \"haben_ob\",\n",
    "    \"soll2\": \"soll_periode_13\",\n",
    "    \"haben3\": \"haben_periode_13\",\n",
    "    \"soll4\": \"soll_periode_12\",\n",
    "    \"haben5\": \"haben_periode_12\",\n",
    "    \"soll6\": \"soll_saldo\",\n",
    "    \"haben7\": \"haben_saldo\"})\n",
    "\n",
    "    # cast data types \n",
    "    df[\"kontobezeichnung\"] = df[\"kontobezeichnung\"].astype(str)\n",
    "\n",
    "    # category\n",
    "    df[\"kontonummer\"] = df[\"kontonummer\"].astype(\"Int64\") # remove the trailing commas\n",
    "    df[\"kontonummer\"] = df[\"kontonummer\"].astype(\"category\")\n",
    "    \n",
    "    \n",
    "    return df\n",
    "\n",
    "\n",
    "# change df in place\n",
    "df_gl = sanitize_gl(df_gl)\n",
    "df_tb = sanitize_tb(df_tb)\n",
    "\n",
    "df_gl.dtypes, df_tb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "135b4eec-f348-43f0-914d-1382d6b1cc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove redundant information \n",
    "del df_gl[\"s'betrag eur.1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d3ab1bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# in case you need to join the general ledger with the header file, please uncomment the following lines\n",
    "# join the header file with the general ledger file to populate the GL with the missing information from the header file and keep all the information from the GL file\n",
    "\n",
    "# df_gl_ = pd.merge(df_gl, df_header, on = \"index\", how = \"outer\", suffixes = ('_left', '_right'))\n",
    "\n",
    "# df_gl_.to_excel('data/general_ledger_complete.xlsx', index=False, sheet_name='GL')\n",
    "\n",
    "# len(df_gl), len(df_gl_), df_gl_.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f6f1971",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export column names as a dictionary to quickly access the column names\n",
    "dict_columns_gl = dict(zip(df_gl.columns, range(len(df_gl.columns))))\n",
    "dict_columns_tb = dict(zip(df_tb.columns, range(len(df_tb.columns))))\n",
    "\n",
    "with open('dict_columns_gl.json', 'w') as fp:\n",
    "    json.dump(dict_columns_gl, fp)\n",
    "\n",
    "with open('dict_columns_tb.json', 'w') as fp:\n",
    "    json.dump(dict_columns_tb, fp)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "67a51ff0-2b0b-4031-be33-cdf405540978",
   "metadata": {},
   "source": [
    "## Missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60678540",
   "metadata": {},
   "source": [
    "__investigate missing values__\n",
    "\n",
    "check if there are any missing values in the dataframes and explain why they are missing\n",
    "\n",
    "e.g. missing values in the general ledger file are due to the fact that the header file is missing information for the respective booking and the debit and credit amounts for each booking are stored in separate rows.\n",
    "\n",
    "e.g. missing values in the trial balance file are due to the fact that the account is not used in the respective period"
   ]
  },
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    "# Count Missing Values per Variable und in DataFrame überführen\n",
    "y_count_mv = pd.DataFrame(df_gl.isnull().sum())\n",
    "y_count_mv.columns = ['count']\n",
    "y_count_mv.index.names = ['Name']\n",
    "y_count_mv['Name'] = y_count_mv.index\n",
    "y_count_mv = y_count_mv[y_count_mv['count'] != 0]\n",
    "y_count_mv.sort_values(by=['count'], inplace=True, ascending=True)\n",
    "\n",
    "\n",
    "missing_values = pd.DataFrame(y_count_mv['count'] / len(df_gl) * 100)\n",
    "missing_values.columns = ['count']\n",
    "missing_values.index.names = ['Name']\n",
    "missing_values.sort_values(by=['count'], inplace=True, ascending=True)\n",
    "\n",
    "x = y_count_mv['Name']\n",
    "\n",
    "# Creating two subplots\n",
    "fig = sp.make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True,\n",
    "                    shared_yaxes=False, vertical_spacing=0.001)\n",
    "\n",
    "fig.append_trace(go.Bar(\n",
    "    x=missing_values['count'],\n",
    "    y=x,\n",
    "    marker=dict(\n",
    "        color='rgba(18, 63, 90, 0.95)',\n",
    "        line=dict(\n",
    "            color='rgba(18, 63, 90, 1.0)',\n",
    "            width=1),\n",
    "    ),\n",
    "    name='Relative amount of missing values (%)',\n",
    "    orientation='h',\n",
    "), 1, 1)\n",
    "\n",
    "fig.append_trace(go.Scatter(\n",
    "    x=y_count_mv['count'], y=x,\n",
    "    mode='lines+markers',\n",
    "    line_color='rgb(0, 68, 27)',\n",
    "    name='Absolute values of missing values',\n",
    "), 1, 2)\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Absolute and relative amount of missing values per variable',\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "        domain=[0, 0.85],\n",
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    "    xaxis2=dict(\n",
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    "        showticklabels=True,\n",
    "        showgrid=True,\n",
    "        domain=[0.47, 1],\n",
    "        dtick=2000,\n",
    "    ),\n",
    "    legend=dict(x=0.029, y=1.038, font_size=10),\n",
    "    margin=dict(l=100, r=20, t=70, b=70),\n",
    "    paper_bgcolor='rgb(248, 248, 255)',\n",
    "    plot_bgcolor='rgb(248, 248, 255)',\n",
    ")\n",
    "\n",
    "annotations = []\n",
    "\n",
    "y_s = np.round(missing_values['count'], decimals=2)\n",
    "y_nw = np.rint(y_count_mv['count'])\n",
    "\n",
    "# add labels\n",
    "for ydn, yd, xd in zip(y_nw, y_s, x):\n",
    "    annotations.append(dict(xref='x2', yref='y2',\n",
    "                            y=xd, x=ydn + 500 if ydn == max(y_nw) else ydn -500, # move the label to the left side if it is the maximum value\n",
    "                            text='{:,}'.format(ydn),\n",
    "                            font=dict(family='Arial', size=12,\n",
    "                                      color='rgb(0, 68, 27)'),\n",
    "                            showarrow=False))\n",
    "    annotations.append(dict(xref='x1', yref='y1',\n",
    "                            y=xd, x=yd + 0.75,\n",
    "                            text=str(yd) + '%',\n",
    "                            font=dict(family='Arial', size=12,\n",
    "                                      color='rgb(18, 63, 90)'),\n",
    "                            showarrow=False))\n",
    "\n",
    "fig.update_layout(annotations=annotations)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cb692fef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_gl, figsize=(10, 3), fontsize=10, color=(0.2, 0.4, 0.6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dcbd1d73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Name', ylabel='Name'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.heatmap(df_gl, figsize=(10, 5), fontsize=12, cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab85555",
   "metadata": {},
   "source": [
    "Since the debit and credit amounts for each booking are stored in separate rows, the debit account and amount is exactly negative correlated with the credit account and amount. So the presence of missing values can be explained. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "657a1d42",
   "metadata": {},
   "source": [
    "## JB 0: reconciliation of line item control totals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f1c31e",
   "metadata": {},
   "source": [
    "Reconciliation of the control totals (total amount and line item count) of the Journal Entry Data file(s) provided by the engagement team during the import process and the data imported and used for journal entry analysis. For each Journal Entry Data file, the report also displays the effective and entry date ranges. This report also displays a reconciliation of the control totals (beginning and ending balances and line item count) provided during the import process for the Trial Balance Data file(s), when applicable, to the data imported and used for journal entry analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1bcb7028-8ef7-4c2a-9cd5-75858390fbff",
   "metadata": {},
   "source": [
    "### Account numbers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c784f10c-8a62-4c29-a28d-70551b026064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(               proportion\n",
       " debit_account            \n",
       " 1600                 0.14\n",
       " 1400                 0.10\n",
       " 8726                 0.07\n",
       " 1563                 0.07\n",
       " 1200                 0.06\n",
       " 1763                 0.03\n",
       " 1210                 0.03\n",
       " 1581                 0.03\n",
       " 3425                 0.02\n",
       " 4983                 0.02,\n",
       "                 proportion\n",
       " credit_account            \n",
       " 1600                  0.14\n",
       " 1400                  0.14\n",
       " 8125                  0.09\n",
       " 1210                  0.08\n",
       " 1763                  0.06\n",
       " 1200                  0.05\n",
       " 1563                  0.03\n",
       " 3725                  0.03\n",
       " 1762                  0.02\n",
       " 1220                  0.02)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which accounts are used most often\n",
    "df_debit_acc = round(df_gl[\"debit_account\"].value_counts(normalize = True).to_frame(),2) \n",
    "df_credit_acc = round(df_gl[\"credit_account\"].value_counts(normalize = True).to_frame(),2)\n",
    "\n",
    "df_debit_acc.head(10), df_credit_acc.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7f76311e-4591-43ea-a1f3-bdfc0352898a",
   "metadata": {},
   "source": [
    "There are 1342 debit accounts and 1222 credit accounts present in the GL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7fa6e0bb-b510-450d-929c-3279293c5fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save indices for all NAs to get the respective amounts \n",
    "index_debit_na = df_gl.index[pd.isna(df_gl[\"debit_account\"]) == False]\n",
    "index_credit_na = df_gl.index[pd.isna(df_gl[\"credit_account\"]) == False]\n",
    "\n",
    "amounts_debit = df_gl[\"debit_amount\"].loc[index_debit_na].to_frame(\"debit_amount\")\n",
    "amounts_debit.insert(1, \"debit_account\", df_gl[\"debit_account\"].loc[index_debit_na], True)\n",
    "amounts_credit = df_gl[\"credit_amount\"].loc[index_credit_na].to_frame(\"credit_amount\")\n",
    "amounts_credit.insert(1, \"credit_account\", df_gl[\"credit_account\"].loc[index_credit_na], True)\n",
    "\n",
    "# length equals the length of the original variable, so I assume there is no error here\n",
    "amounts_debit = amounts_debit.groupby(\"debit_account\")[\"debit_amount\"].sum().to_frame()\n",
    "amounts_credit = amounts_credit.groupby(\"credit_account\")[\"credit_amount\"].sum().to_frame()\n",
    "amounts_debit = amounts_debit.reset_index()\n",
    "amounts_credit = amounts_credit.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cf6e8643-aed4-48ea-8fb4-946d16f38172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(554834924.85, 552962013.6799998)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the total amount of debit and credit\n",
    "amounts_debit[\"debit_amount\"].sum(), amounts_credit[\"credit_amount\"].sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bdf91b65-6db5-46db-9929-04e66806d3d3",
   "metadata": {},
   "source": [
    "### Debit and Credit "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a054dad7",
   "metadata": {},
   "source": [
    "### Debit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1d1824e2-039d-43fb-9be7-db0cd30822ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "554834924.85"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_sum_debit = round(df_gl[\"debit_amount\"].dropna().sum(), 2)\n",
    "gl_sum_debit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cfa13214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456475611.45"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_sum_debit = round(df_tb[\"soll_periode_12\"].dropna().sum() + df_tb[\"soll_ob\"].dropna().sum() + df_tb[\"soll_periode_13\"].dropna().sum(), 2)\n",
    "tb_sum_debit"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "975382e7",
   "metadata": {},
   "source": [
    "### Credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbbe5308-87dd-4929-b7b7-44b82e8b1673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "552962013.6800001"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_sum_credit = df_gl[\"credit_amount\"].dropna().sum()\n",
    "gl_sum_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca12cf24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "456475611.45"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_sum_credit = round(df_tb[\"haben_periode_12\"].dropna().sum() + df_tb[\"haben_ob\"].dropna().sum() + df_tb[\"haben_periode_13\"].dropna().sum(), 2)\n",
    "tb_sum_credit"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "42097797",
   "metadata": {},
   "source": [
    "### Difference in the GL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16ed038f-36c5-4b59-963a-b4706f757e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1872911.169999957"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gl_diff_debit_credit = gl_sum_debit - gl_sum_credit\n",
    "gl_diff_debit_credit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "badf4e70",
   "metadata": {},
   "source": [
    "### Difference in the TB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69f221e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb_diff_debit_credit = tb_sum_debit - tb_sum_credit\n",
    "tb_diff_debit_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "76760143",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create report of the reconciliation\n",
    "report = pd.DataFrame({\"GL\": [gl_sum_debit, gl_sum_credit, gl_diff_debit_credit], \"TB\": [tb_sum_debit, tb_sum_credit, tb_diff_debit_credit]}, index = [\"Debit\", \"Credit\", \"Difference\"])\n",
    "\n",
    "if gl_diff_debit_credit == 0 and tb_diff_debit_credit == 0:\n",
    "    with open('No differences found.txt', 'w') as f:\n",
    "        f.write('No differences found')\n",
    "else:\n",
    "    report.to_excel('results/JB0/reconciliation_results.xlsx', sheet_name='report', index = True, header=True, float_format=\"%.2f\", engine='xlsxwriter')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c6521039",
   "metadata": {},
   "source": [
    "## JB 1: journal entries do not balance to zero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782aae7a",
   "metadata": {},
   "source": [
    "This report contains both a summary and a detailed listing of journal entries that do not balance to zero (i.e. debits do not equal credits) for the current, subsequent, and prior periods."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d8f1ad4b-8e35-4c98-9a4e-a28458412656",
   "metadata": {},
   "source": [
    "### Compare GL and TB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c7eb5021-43d5-446d-a988-a235f21703fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge the debit and credit sums into one dataframe\n",
    "df_gl_sums = pd.concat([amounts_debit, amounts_credit], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3ecdb75d-4a26-4a5e-88d8-920e0ea9263f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(178, 4) (292, 4)\n"
     ]
    }
   ],
   "source": [
    "# save two separate dataframes for credit and debit accounts with the corresponding TB accounts\n",
    "df_credit_gl_tb = df_gl_sums[df_gl_sums[\"credit_account\"].isin(df_tb[\"kontonummer\"])]\n",
    "df_debit_gl_tb = df_gl_sums[df_gl_sums[\"debit_account\"].isin(df_tb[\"kontonummer\"])]\n",
    "\n",
    "print(df_credit_gl_tb.shape, df_debit_gl_tb.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d62188f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Account: 23 GL: 14495.0 TB: 14495.0 \u001b[92mOK\u001b[0m\n",
      "Account: 25 GL: 8159.2 TB: 8159.2 \u001b[92mOK\u001b[0m\n",
      "Account: 26 GL: 26646.0 TB: 26646.0 \u001b[92mOK\u001b[0m\n",
      "Account: 27 GL: 1231.25 TB: 1231.25 \u001b[92mOK\u001b[0m\n",
      "Account: 30 GL: 24000.0 TB: 24000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 35 GL: 60000.0 TB: 60000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 85 GL: 10649.0 TB: 10649.0 \u001b[92mOK\u001b[0m\n",
      "Account: 175 GL: 23203.13 TB: 23203.13 \u001b[92mOK\u001b[0m\n",
      "Account: 180 GL: 39200.0 TB: 39200.0 \u001b[92mOK\u001b[0m\n",
      "Account: 187 GL: 46977.0 TB: 46977.0 \u001b[92mOK\u001b[0m\n",
      "Account: 188 GL: 91790.0 TB: 91790.0 \u001b[92mOK\u001b[0m\n",
      "Account: 190 GL: 50143.0 TB: 50143.0 \u001b[92mOK\u001b[0m\n",
      "Account: 191 GL: 68655.0 TB: 68655.0 \u001b[92mOK\u001b[0m\n",
      "Account: 215 GL: 16412.79 TB: 16412.79 \u001b[92mOK\u001b[0m\n",
      "Account: 216 GL: 75308.88 TB: 75308.88 \u001b[92mOK\u001b[0m\n",
      "Account: 284 GL: 133278.0 TB: 133278.0 \u001b[92mOK\u001b[0m\n",
      "Account: 325 GL: 84941.35 TB: 84941.35 \u001b[92mOK\u001b[0m\n",
      "Account: 380 GL: 9273.99 TB: 9273.99 \u001b[92mOK\u001b[0m\n",
      "Account: 385 GL: 6535.0 TB: 6535.0 \u001b[92mOK\u001b[0m\n",
      "Account: 400 GL: 62119.9 TB: 62119.9 \u001b[92mOK\u001b[0m\n",
      "Account: 403 GL: 61806.49 TB: 61806.49 \u001b[92mOK\u001b[0m\n",
      "Account: 405 GL: 676088.57 TB: 676088.57 \u001b[92mOK\u001b[0m\n",
      "Account: 408 GL: 4329.0 TB: 4329.0 \u001b[92mOK\u001b[0m\n",
      "Account: 411 GL: 59944.9 TB: 59944.9 \u001b[92mOK\u001b[0m\n",
      "Account: 414 GL: 8125.0 TB: 8125.0 \u001b[92mOK\u001b[0m\n",
      "Account: 415 GL: 175102.59 TB: 175102.59 \u001b[92mOK\u001b[0m\n",
      "Account: 423 GL: 5998.0 TB: 5998.0 \u001b[92mOK\u001b[0m\n",
      "Account: 425 GL: 10188.85 TB: 10188.85 \u001b[92mOK\u001b[0m\n",
      "Account: 480 GL: 21683.0 TB: 21683.0 \u001b[92mOK\u001b[0m\n",
      "Account: 483 GL: 26432.59 TB: 26432.59 \u001b[92mOK\u001b[0m\n",
      "Account: 524 GL: 112858.7 TB: 112858.7 \u001b[92mOK\u001b[0m\n",
      "Account: 527 GL: 39240.11 TB: 39240.11 \u001b[92mOK\u001b[0m\n",
      "Account: 528 GL: 7563.72 TB: 7563.72 \u001b[92mOK\u001b[0m\n",
      "Account: 529 GL: 56613.1 TB: 56613.1 \u001b[92mOK\u001b[0m\n",
      "Account: 662 GL: 140.09 TB: 140.09 \u001b[92mOK\u001b[0m\n",
      "Account: 664 GL: 809.83 TB: 809.83 \u001b[92mOK\u001b[0m\n",
      "Account: 665 GL: 0.02 TB: 0.02 \u001b[92mOK\u001b[0m\n",
      "Account: 666 GL: 329.92 TB: 329.92 \u001b[92mOK\u001b[0m\n",
      "Account: 667 GL: 1355.56 TB: 1355.56 \u001b[92mOK\u001b[0m\n",
      "Account: 668 GL: 9114.72 TB: 9114.72 \u001b[92mOK\u001b[0m\n",
      "Account: 670 GL: 2119.91 TB: 2119.91 \u001b[92mOK\u001b[0m\n",
      "Account: 671 GL: 2253.2 TB: 2253.2 \u001b[92mOK\u001b[0m\n",
      "Account: 672 GL: 7624999.32 TB: 7624999.32 \u001b[92mOK\u001b[0m\n",
      "Account: 673 GL: 12887.14 TB: 12887.14 \u001b[92mOK\u001b[0m\n",
      "Account: 674 GL: 4312.03 TB: 4312.03 \u001b[92mOK\u001b[0m\n",
      "Account: 675 GL: 4674998.7 TB: 4674998.7 \u001b[92mOK\u001b[0m\n",
      "Account: 676 GL: 6700.43 TB: 6700.43 \u001b[92mOK\u001b[0m\n",
      "Account: 677 GL: 1521011.42 TB: 1521011.42 \u001b[92mOK\u001b[0m\n",
      "Account: 678 GL: 7356.29 TB: 7356.29 \u001b[92mOK\u001b[0m\n",
      "Account: 950 GL: 10869.56 TB: 10869.56 \u001b[92mOK\u001b[0m\n",
      "Account: 972 GL: 87437.16 TB: 87437.16 \u001b[92mOK\u001b[0m\n",
      "Account: 973 GL: 239700.21 TB: 239700.21 \u001b[92mOK\u001b[0m\n",
      "Account: 980 GL: 130185.16 TB: 130185.16 \u001b[92mOK\u001b[0m\n",
      "Account: 1000 GL: 34213.62 TB: 34213.62 \u001b[92mOK\u001b[0m\n",
      "Account: 1200 GL: 35451039.72 TB: 35451039.72 \u001b[92mOK\u001b[0m\n",
      "Account: 1202 GL: 2254942.89 TB: 2254942.89 \u001b[92mOK\u001b[0m\n",
      "Account: 1203 GL: 28111.19 TB: 28111.19 \u001b[92mOK\u001b[0m\n",
      "Account: 1210 GL: 34458908.88 TB: 34458908.88 \u001b[92mOK\u001b[0m\n",
      "Account: 1212 GL: 1825.17 TB: 1825.17 \u001b[92mOK\u001b[0m\n",
      "Account: 1213 GL: 5.04 TB: 5.04 \u001b[92mOK\u001b[0m\n",
      "Account: 1220 GL: 19045916.43 TB: 19045916.43 \u001b[92mOK\u001b[0m\n",
      "Account: 1224 GL: 5437986.37 TB: 5437986.37 \u001b[92mOK\u001b[0m\n",
      "Account: 1226 GL: 221.0 TB: 221.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1360 GL: 5770776.75 TB: 5770776.75 \u001b[92mOK\u001b[0m\n",
      "Account: 1400 GL: 53413905.1 TB: 53413905.1 \u001b[92mOK\u001b[0m\n",
      "Account: 1401 GL: 33523.3 TB: 33523.3 \u001b[92mOK\u001b[0m\n",
      "Account: 1411 GL: 95197.47 TB: 95197.47 \u001b[92mOK\u001b[0m\n",
      "Account: 1412 GL: 50784.71 TB: 50784.71 \u001b[92mOK\u001b[0m\n",
      "Account: 1471 GL: 7240096.74 TB: 7240096.74 \u001b[92mOK\u001b[0m\n",
      "Account: 1472 GL: 2489521.25 TB: 2489521.25 \u001b[92mOK\u001b[0m\n",
      "Account: 1473 GL: 3605298.86 TB: 3605298.86 \u001b[92mOK\u001b[0m\n",
      "Account: 1474 GL: 662385.95 TB: 662385.95 \u001b[92mOK\u001b[0m\n",
      "Account: 1475 GL: 5893572.94 TB: 5893572.94 \u001b[92mOK\u001b[0m\n",
      "Account: 1476 GL: 10419592.65 TB: 10419592.65 \u001b[92mOK\u001b[0m\n",
      "Account: 1500 GL: 359778.78 TB: 359778.78 \u001b[92mOK\u001b[0m\n",
      "Account: 1510 GL: 793609.95 TB: 793609.95 \u001b[92mOK\u001b[0m\n",
      "Account: 1513 GL: 74297.8 TB: 74297.8 \u001b[92mOK\u001b[0m\n",
      "Account: 1532 GL: 12000.0 TB: 12000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1535 GL: 700.0 TB: 700.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1536 GL: 3000.0 TB: 3000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1539 GL: 1800.0 TB: 1800.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1548 GL: 111925.0 TB: 111925.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1549 GL: 135522.98 TB: 135522.98 \u001b[92mOK\u001b[0m\n",
      "Account: 1561 GL: 370713.33 TB: 370713.33 \u001b[92mOK\u001b[0m\n",
      "Account: 1562 GL: 5526.83 TB: 5526.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1563 GL: 113277.3 TB: 113277.3 \u001b[92mOK\u001b[0m\n",
      "Account: 1567 GL: 4813.99 TB: 4813.99 \u001b[92mOK\u001b[0m\n",
      "Account: 1574 GL: 11727.62 TB: 11727.62 \u001b[92mOK\u001b[0m\n",
      "Account: 1581 GL: 19923.37 TB: 19923.37 \u001b[92mOK\u001b[0m\n",
      "Account: 1590 GL: 3226476.96 TB: 3226476.96 \u001b[92mOK\u001b[0m\n",
      "Account: 1591 GL: 301498.91 TB: 301498.91 \u001b[92mOK\u001b[0m\n",
      "Account: 1600 GL: 66527730.94 TB: 66527730.94 \u001b[92mOK\u001b[0m\n",
      "Account: 1610 GL: 843312.02 TB: 843312.02 \u001b[92mOK\u001b[0m\n",
      "Account: 1612 GL: 822664.69 TB: 822664.69 \u001b[92mOK\u001b[0m\n",
      "Account: 1700 GL: 12107.76 TB: 12107.76 \u001b[92mOK\u001b[0m\n",
      "Account: 1740 GL: 5442127.72 TB: 5442127.72 \u001b[92mOK\u001b[0m\n",
      "Account: 1741 GL: 629638.52 TB: 629638.52 \u001b[92mOK\u001b[0m\n",
      "Account: 1742 GL: 1614200.14 TB: 1614200.14 \u001b[92mOK\u001b[0m\n",
      "Account: 1743 GL: 252063.71 TB: 252063.71 \u001b[92mOK\u001b[0m\n",
      "Account: 1755 GL: 7699619.83 TB: 7699619.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1761 GL: 2950194.82 TB: 2950194.82 \u001b[92mOK\u001b[0m\n",
      "Account: 1762 GL: 599308.84 TB: 599308.84 \u001b[92mOK\u001b[0m\n",
      "Account: 1763 GL: 7238160.9 TB: 7238160.9 \u001b[92mOK\u001b[0m\n",
      "Account: 1764 GL: 662.72 TB: 662.72 \u001b[92mOK\u001b[0m\n",
      "Account: 1765 GL: 113869.62 TB: 113869.62 \u001b[92mOK\u001b[0m\n",
      "Account: 1767 GL: 578573.46 TB: 578573.46 \u001b[92mOK\u001b[0m\n",
      "Account: 1774 GL: 50905.8 TB: 50905.8 \u001b[92mOK\u001b[0m\n",
      "Account: 1776 GL: 32205.03 TB: 32205.03 \u001b[92mOK\u001b[0m\n",
      "Account: 1778 GL: 93594.14 TB: 93594.14 \u001b[92mOK\u001b[0m\n",
      "Account: 1780 GL: 597710.46 TB: 597710.46 \u001b[92mOK\u001b[0m\n",
      "Account: 1782 GL: 730194.01 TB: 730194.01 \u001b[92mOK\u001b[0m\n",
      "Account: 2200 GL: 63.15 TB: 63.15 \u001b[92mOK\u001b[0m\n",
      "Account: 2502 GL: 9572380.52 TB: 9572380.52 \u001b[92mOK\u001b[0m\n",
      "Account: 2509 GL: 586624.04 TB: 586624.04 \u001b[92mOK\u001b[0m\n",
      "Account: 2510 GL: 10919.89 TB: 10919.89 \u001b[92mOK\u001b[0m\n",
      "Account: 2650 GL: 300.82 TB: 300.82 \u001b[92mOK\u001b[0m\n",
      "Account: 2651 GL: 22524.66 TB: 22524.66 \u001b[92mOK\u001b[0m\n",
      "Account: 2660 GL: 343030.57 TB: 343030.57 \u001b[92mOK\u001b[0m\n",
      "Account: 2700 GL: 43878.09 TB: 43878.09 \u001b[92mOK\u001b[0m\n",
      "Account: 2701 GL: 8520.12 TB: 8520.12 \u001b[92mOK\u001b[0m\n",
      "Account: 2703 GL: 9737.4 TB: 9737.4 \u001b[92mOK\u001b[0m\n",
      "Account: 2711 GL: 11381.09 TB: 11381.09 \u001b[92mOK\u001b[0m\n",
      "Account: 2742 GL: 23659.1 TB: 23659.1 \u001b[92mOK\u001b[0m\n",
      "Account: 3201 GL: 994262.08 TB: 994262.08 \u001b[92mOK\u001b[0m\n",
      "Account: 3400 GL: 2180666.61 TB: 2180666.61 \u001b[92mOK\u001b[0m\n",
      "Account: 3425 GL: 92097.42 TB: 92097.42 \u001b[92mOK\u001b[0m\n",
      "Account: 3709 GL: 2273.0 TB: 2273.0 \u001b[92mOK\u001b[0m\n",
      "Account: 3724 GL: 735.74 TB: 735.74 \u001b[92mOK\u001b[0m\n",
      "Account: 3725 GL: 236155.3 TB: 236155.3 \u001b[92mOK\u001b[0m\n",
      "Account: 3735 GL: 11599.41 TB: 11599.41 \u001b[92mOK\u001b[0m\n",
      "Account: 3736 GL: 35850.17 TB: 35850.17 \u001b[92mOK\u001b[0m\n",
      "Account: 3742 GL: 30325.01 TB: 30325.01 \u001b[92mOK\u001b[0m\n",
      "Account: 3743 GL: 3334.83 TB: 3334.83 \u001b[92mOK\u001b[0m\n",
      "Account: 3803 GL: 15758.21 TB: 15758.21 \u001b[92mOK\u001b[0m\n",
      "Account: 3960 GL: 5257533.91 TB: 5257533.91 \u001b[92mOK\u001b[0m\n",
      "Account: 3961 GL: 380878.23 TB: 380878.23 \u001b[92mOK\u001b[0m\n",
      "Account: 3980 GL: 994262.08 TB: 994262.08 \u001b[92mOK\u001b[0m\n",
      "Account: 3981 GL: 203833.72 TB: 203833.72 \u001b[92mOK\u001b[0m\n",
      "Account: 4100 GL: 297560.93 TB: 297560.93 \u001b[92mOK\u001b[0m\n",
      "Account: 4106 GL: 15134.94 TB: 15134.94 \u001b[92mOK\u001b[0m\n",
      "Account: 4112 GL: 0.0 TB: 0.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4126 GL: 130000.0 TB: 130000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4130 GL: 318.57 TB: 318.57 \u001b[92mOK\u001b[0m\n",
      "Account: 4131 GL: 242.0 TB: 242.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4360 GL: 19394.8 TB: 19394.8 \u001b[92mOK\u001b[0m\n",
      "Account: 4510 GL: 393.0 TB: 393.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4530 GL: 0.01 TB: 0.01 \u001b[92mOK\u001b[0m\n",
      "Account: 4654 GL: 27.5 TB: 27.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4673 GL: 33524.14 TB: 33524.14 \u001b[92mOK\u001b[0m\n",
      "Account: 4733 GL: 21391.48 TB: 21391.48 \u001b[92mOK\u001b[0m\n",
      "Account: 4790 GL: 2789.26 TB: 2789.26 \u001b[92mOK\u001b[0m\n",
      "Account: 4808 GL: 26064.15 TB: 26064.15 \u001b[92mOK\u001b[0m\n",
      "Account: 4809 GL: 42059.48 TB: 42059.48 \u001b[92mOK\u001b[0m\n",
      "Account: 4933 GL: 3402.0 TB: 3402.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4958 GL: 38377.5 TB: 38377.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4970 GL: 98.91 TB: 98.91 \u001b[92mOK\u001b[0m\n",
      "Account: 4979 GL: 29.4 TB: 29.4 \u001b[92mOK\u001b[0m\n",
      "Account: 4983 GL: 76358.2 TB: 76358.2 \u001b[92mOK\u001b[0m\n",
      "Account: 4984 GL: 82.34 TB: 82.34 \u001b[92mOK\u001b[0m\n",
      "Account: 4986 GL: 1030.0 TB: 1030.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4991 GL: 2548.83 TB: 2548.83 \u001b[92mOK\u001b[0m\n",
      "Account: 8120 GL: 12102827.76 TB: 12102827.76 \u001b[92mOK\u001b[0m\n",
      "Account: 8121 GL: 571648.0 TB: 571648.0 \u001b[92mOK\u001b[0m\n",
      "Account: 8125 GL: 58137504.66 TB: 58137504.66 \u001b[92mOK\u001b[0m\n",
      "Account: 8126 GL: 1536775.46 TB: 1536775.46 \u001b[92mOK\u001b[0m\n",
      "Account: 8300 GL: 22092.72 TB: 22092.72 \u001b[92mOK\u001b[0m\n",
      "Account: 8400 GL: 577847.04 TB: 577847.04 \u001b[92mOK\u001b[0m\n",
      "Account: 8420 GL: 492600.4 TB: 492600.4 \u001b[92mOK\u001b[0m\n",
      "Account: 8440 GL: 188540.0 TB: 188540.0 \u001b[92mOK\u001b[0m\n",
      "Account: 8441 GL: 161025.12 TB: 161025.12 \u001b[92mOK\u001b[0m\n",
      "Account: 8700 GL: 234408.78 TB: 234408.78 \u001b[92mOK\u001b[0m\n",
      "Account: 8726 GL: 64.47 TB: 64.47 \u001b[92mOK\u001b[0m\n",
      "Account: 8738 GL: 523758.67 TB: 523758.67 \u001b[92mOK\u001b[0m\n",
      "Account: 8822 GL: 59815.24 TB: 59815.24 \u001b[92mOK\u001b[0m\n",
      "Account: 8824 GL: 36523.0 TB: 36523.0 \u001b[92mOK\u001b[0m\n",
      "Account: 8829 GL: 50800.0 TB: 50800.0 \u001b[92mOK\u001b[0m\n",
      "Account: 8924 GL: 41174.51 TB: 41174.51 \u001b[92mOK\u001b[0m\n",
      "Account: 8970 GL: 126941.03 TB: 126941.03 \u001b[92mOK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# compare the sums of the GL and the TB for each credit account and save the difference \n",
    "\n",
    "acc_with_diff_credit = []\n",
    "sum_diff_credit = 0\n",
    "\n",
    "for acc in df_credit_gl_tb[\"credit_account\"]:\n",
    "    sum_gl = round(df_credit_gl_tb[\"credit_amount\"].loc[df_credit_gl_tb.index[df_credit_gl_tb[\"credit_account\"] == acc]].sum(), 2)\n",
    "    sum_tb = round(df_tb[\"haben_periode_12\"].loc[df_tb.index[df_tb[\"kontonummer\"] == acc]].sum(), 2)\n",
    "    if sum_gl != sum_tb:\n",
    "        acc_with_diff_credit.append(acc)\n",
    "        sum_diff_credit += sum_gl - sum_tb\n",
    "        print(f\"Account: {acc} GL: {sum_gl} TB: {sum_tb} {bcolors.FAIL}NOT OK{bcolors.ENDC}\")\n",
    "    else:\n",
    "        print(f\"Account: {acc} GL: {sum_gl} TB: {sum_tb} {bcolors.OKGREEN}OK{bcolors.ENDC}\")\n",
    "\n",
    "pd.DataFrame(acc_with_diff_credit).to_excel('./results/JB1/credit_diff_gl_tb.xlsx', sheet_name='GL vs TB' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2000887e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Account: 22 GL: 14495.0 TB: 14495.0 \u001b[92mOK\u001b[0m\n",
      "Account: 23 GL: 22620.0 TB: 22620.0 \u001b[92mOK\u001b[0m\n",
      "Account: 25 GL: 8159.2 TB: 8159.2 \u001b[92mOK\u001b[0m\n",
      "Account: 26 GL: 8125.0 TB: 8125.0 \u001b[92mOK\u001b[0m\n",
      "Account: 27 GL: 61176.15 TB: 61176.15 \u001b[92mOK\u001b[0m\n",
      "Account: 176 GL: 20737.13 TB: 20737.13 \u001b[92mOK\u001b[0m\n",
      "Account: 180 GL: 39200.0 TB: 39200.0 \u001b[92mOK\u001b[0m\n",
      "Account: 210 GL: 54051.7 TB: 54051.7 \u001b[92mOK\u001b[0m\n",
      "Account: 280 GL: 206000.0 TB: 206000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 291 GL: 245721.14 TB: 245721.14 \u001b[92mOK\u001b[0m\n",
      "Account: 320 GL: 4627.35 TB: 4627.35 \u001b[92mOK\u001b[0m\n",
      "Account: 354 GL: 10000.0 TB: 10000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 381 GL: 24500.0 TB: 24500.0 \u001b[92mOK\u001b[0m\n",
      "Account: 384 GL: 5725.0 TB: 5725.0 \u001b[92mOK\u001b[0m\n",
      "Account: 400 GL: 91896.33 TB: 91896.33 \u001b[92mOK\u001b[0m\n",
      "Account: 403 GL: 745421.87 TB: 745421.87 \u001b[92mOK\u001b[0m\n",
      "Account: 408 GL: 4329.0 TB: 4329.0 \u001b[92mOK\u001b[0m\n",
      "Account: 411 GL: 319108.38 TB: 319108.38 \u001b[92mOK\u001b[0m\n",
      "Account: 412 GL: 10490.55 TB: 10490.55 \u001b[92mOK\u001b[0m\n",
      "Account: 414 GL: 8125.0 TB: 8125.0 \u001b[92mOK\u001b[0m\n",
      "Account: 415 GL: 13289.58 TB: 13289.58 \u001b[92mOK\u001b[0m\n",
      "Account: 416 GL: 4329.0 TB: 4329.0 \u001b[92mOK\u001b[0m\n",
      "Account: 423 GL: 32177.85 TB: 32177.85 \u001b[92mOK\u001b[0m\n",
      "Account: 480 GL: 4276.8 TB: 4276.8 \u001b[92mOK\u001b[0m\n",
      "Account: 483 GL: 43838.79 TB: 43838.79 \u001b[92mOK\u001b[0m\n",
      "Account: 514 GL: 28750.0 TB: 28750.0 \u001b[92mOK\u001b[0m\n",
      "Account: 529 GL: 41376.51 TB: 41376.51 \u001b[92mOK\u001b[0m\n",
      "Account: 532 GL: 528.4 TB: 528.4 \u001b[92mOK\u001b[0m\n",
      "Account: 662 GL: 19914.33 TB: 19914.33 \u001b[92mOK\u001b[0m\n",
      "Account: 664 GL: 68336.97 TB: 68336.97 \u001b[92mOK\u001b[0m\n",
      "Account: 665 GL: 25050.01 TB: 25050.01 \u001b[92mOK\u001b[0m\n",
      "Account: 666 GL: 18644.22 TB: 18644.22 \u001b[92mOK\u001b[0m\n",
      "Account: 667 GL: 39732.92 TB: 39732.92 \u001b[92mOK\u001b[0m\n",
      "Account: 668 GL: 170737.92 TB: 170737.92 \u001b[92mOK\u001b[0m\n",
      "Account: 670 GL: 51553.56 TB: 51553.56 \u001b[92mOK\u001b[0m\n",
      "Account: 671 GL: 54843.12 TB: 54843.12 \u001b[92mOK\u001b[0m\n",
      "Account: 672 GL: 7874999.4 TB: 7874999.4 \u001b[92mOK\u001b[0m\n",
      "Account: 673 GL: 176286.48 TB: 176286.48 \u001b[92mOK\u001b[0m\n",
      "Account: 674 GL: 55114.32 TB: 55114.32 \u001b[92mOK\u001b[0m\n",
      "Account: 675 GL: 4796427.3 TB: 4796427.3 \u001b[92mOK\u001b[0m\n",
      "Account: 676 GL: 74373.0 TB: 74373.0 \u001b[92mOK\u001b[0m\n",
      "Account: 677 GL: 16093.2 TB: 16093.2 \u001b[92mOK\u001b[0m\n",
      "Account: 973 GL: 523758.67 TB: 523758.67 \u001b[92mOK\u001b[0m\n",
      "Account: 975 GL: 130000.0 TB: 130000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 977 GL: 38377.5 TB: 38377.5 \u001b[92mOK\u001b[0m\n",
      "Account: 980 GL: 93480.24 TB: 93480.24 \u001b[92mOK\u001b[0m\n",
      "Account: 998 GL: 3815.93 TB: 3815.93 \u001b[92mOK\u001b[0m\n",
      "Account: 1000 GL: 32030.36 TB: 32030.36 \u001b[92mOK\u001b[0m\n",
      "Account: 1200 GL: 35883373.31 TB: 35883373.31 \u001b[92mOK\u001b[0m\n",
      "Account: 1202 GL: 277522.88 TB: 277522.88 \u001b[92mOK\u001b[0m\n",
      "Account: 1203 GL: 15592.23 TB: 15592.23 \u001b[92mOK\u001b[0m\n",
      "Account: 1210 GL: 34947796.26 TB: 34947796.26 \u001b[92mOK\u001b[0m\n",
      "Account: 1212 GL: 25.47 TB: 25.47 \u001b[92mOK\u001b[0m\n",
      "Account: 1213 GL: 20.37 TB: 20.37 \u001b[92mOK\u001b[0m\n",
      "Account: 1220 GL: 19664297.78 TB: 19664297.78 \u001b[92mOK\u001b[0m\n",
      "Account: 1224 GL: 2339030.34 TB: 2339030.34 \u001b[92mOK\u001b[0m\n",
      "Account: 1360 GL: 5770776.75 TB: 5770776.75 \u001b[92mOK\u001b[0m\n",
      "Account: 1400 GL: 53076626.03 TB: 53076626.03 \u001b[92mOK\u001b[0m\n",
      "Account: 1411 GL: 42061.01 TB: 42061.01 \u001b[92mOK\u001b[0m\n",
      "Account: 1412 GL: 192347.77 TB: 192347.77 \u001b[92mOK\u001b[0m\n",
      "Account: 1471 GL: 7271936.38 TB: 7271936.38 \u001b[92mOK\u001b[0m\n",
      "Account: 1472 GL: 2422485.25 TB: 2422485.25 \u001b[92mOK\u001b[0m\n",
      "Account: 1473 GL: 3364655.13 TB: 3364655.13 \u001b[92mOK\u001b[0m\n",
      "Account: 1474 GL: 716873.46 TB: 716873.46 \u001b[92mOK\u001b[0m\n",
      "Account: 1475 GL: 6239934.4 TB: 6239934.4 \u001b[92mOK\u001b[0m\n",
      "Account: 1476 GL: 10522174.95 TB: 10522174.95 \u001b[92mOK\u001b[0m\n",
      "Account: 1477 GL: 700.0 TB: 700.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1478 GL: 923291.98 TB: 923291.98 \u001b[92mOK\u001b[0m\n",
      "Account: 1500 GL: 250.0 TB: 250.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1510 GL: 0.0 TB: 0.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1513 GL: 52384.0 TB: 52384.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1532 GL: 25202.02 TB: 25202.02 \u001b[92mOK\u001b[0m\n",
      "Account: 1536 GL: 52.36 TB: 52.36 \u001b[92mOK\u001b[0m\n",
      "Account: 1539 GL: 46.44 TB: 46.44 \u001b[92mOK\u001b[0m\n",
      "Account: 1548 GL: 0.0 TB: 0.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1549 GL: 0.0 TB: 0.0 \u001b[92mOK\u001b[0m\n",
      "Account: 1561 GL: 2950194.82 TB: 2950194.82 \u001b[92mOK\u001b[0m\n",
      "Account: 1562 GL: 599308.84 TB: 599308.84 \u001b[92mOK\u001b[0m\n",
      "Account: 1563 GL: 7238160.9 TB: 7238160.9 \u001b[92mOK\u001b[0m\n",
      "Account: 1565 GL: 17496.83 TB: 17496.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1567 GL: 578573.46 TB: 578573.46 \u001b[92mOK\u001b[0m\n",
      "Account: 1574 GL: 767731.62 TB: 767731.62 \u001b[92mOK\u001b[0m\n",
      "Account: 1576 GL: 114.71 TB: 114.71 \u001b[92mOK\u001b[0m\n",
      "Account: 1577 GL: 95549.58 TB: 95549.58 \u001b[92mOK\u001b[0m\n",
      "Account: 1578 GL: 922.45 TB: 922.45 \u001b[92mOK\u001b[0m\n",
      "Account: 1579 GL: 924.77 TB: 924.77 \u001b[92mOK\u001b[0m\n",
      "Account: 1581 GL: 603536.79 TB: 603536.79 \u001b[92mOK\u001b[0m\n",
      "Account: 1582 GL: 163.24 TB: 163.24 \u001b[92mOK\u001b[0m\n",
      "Account: 1590 GL: 3226476.96 TB: 3226476.96 \u001b[92mOK\u001b[0m\n",
      "Account: 1591 GL: 301498.91 TB: 301498.91 \u001b[92mOK\u001b[0m\n",
      "Account: 1600 GL: 67588412.03 TB: 67588412.03 \u001b[92mOK\u001b[0m\n",
      "Account: 1601 GL: 580796.43 TB: 580796.43 \u001b[92mOK\u001b[0m\n",
      "Account: 1610 GL: 841238.01 TB: 841238.01 \u001b[92mOK\u001b[0m\n",
      "Account: 1612 GL: 994262.08 TB: 994262.08 \u001b[92mOK\u001b[0m\n",
      "Account: 1700 GL: 55448.15 TB: 55448.15 \u001b[92mOK\u001b[0m\n",
      "Account: 1740 GL: 5406084.75 TB: 5406084.75 \u001b[92mOK\u001b[0m\n",
      "Account: 1741 GL: 655771.96 TB: 655771.96 \u001b[92mOK\u001b[0m\n",
      "Account: 1742 GL: 1533737.83 TB: 1533737.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1743 GL: 347821.23 TB: 347821.23 \u001b[92mOK\u001b[0m\n",
      "Account: 1755 GL: 7699619.83 TB: 7699619.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1761 GL: 370713.33 TB: 370713.33 \u001b[92mOK\u001b[0m\n",
      "Account: 1762 GL: 5526.83 TB: 5526.83 \u001b[92mOK\u001b[0m\n",
      "Account: 1763 GL: 113277.3 TB: 113277.3 \u001b[92mOK\u001b[0m\n",
      "Account: 1764 GL: 55.85 TB: 55.85 \u001b[92mOK\u001b[0m\n",
      "Account: 1765 GL: 8594.36 TB: 8594.36 \u001b[92mOK\u001b[0m\n",
      "Account: 1767 GL: 4813.99 TB: 4813.99 \u001b[92mOK\u001b[0m\n",
      "Account: 1778 GL: 2454.5 TB: 2454.5 \u001b[92mOK\u001b[0m\n",
      "Account: 1782 GL: 914.69 TB: 914.69 \u001b[92mOK\u001b[0m\n",
      "Account: 1784 GL: 54943.99 TB: 54943.99 \u001b[92mOK\u001b[0m\n",
      "Account: 2110 GL: 7247.83 TB: 7247.83 \u001b[92mOK\u001b[0m\n",
      "Account: 2120 GL: 68366.89 TB: 68366.89 \u001b[92mOK\u001b[0m\n",
      "Account: 2150 GL: 444774.83 TB: 444774.83 \u001b[92mOK\u001b[0m\n",
      "Account: 2200 GL: 111925.0 TB: 111925.0 \u001b[92mOK\u001b[0m\n",
      "Account: 2203 GL: 7338.58 TB: 7338.58 \u001b[92mOK\u001b[0m\n",
      "Account: 2204 GL: 5458.55 TB: 5458.55 \u001b[92mOK\u001b[0m\n",
      "Account: 2308 GL: 11000.0 TB: 11000.0 \u001b[92mOK\u001b[0m\n",
      "Account: 2315 GL: 72990.47 TB: 72990.47 \u001b[92mOK\u001b[0m\n",
      "Account: 2375 GL: 2128.2 TB: 2128.2 \u001b[92mOK\u001b[0m\n",
      "Account: 2501 GL: 9572380.52 TB: 9572380.52 \u001b[92mOK\u001b[0m\n",
      "Account: 2510 GL: 597543.93 TB: 597543.93 \u001b[92mOK\u001b[0m\n",
      "Account: 2651 GL: 1000.16 TB: 1000.16 \u001b[92mOK\u001b[0m\n",
      "Account: 2700 GL: 0.0 TB: 0.0 \u001b[92mOK\u001b[0m\n",
      "Account: 2742 GL: 7597.56 TB: 7597.56 \u001b[92mOK\u001b[0m\n",
      "Account: 3100 GL: 97669.8 TB: 97669.8 \u001b[92mOK\u001b[0m\n",
      "Account: 3201 GL: 822664.69 TB: 822664.69 \u001b[92mOK\u001b[0m\n",
      "Account: 3400 GL: 17354087.46 TB: 17354087.46 \u001b[92mOK\u001b[0m\n",
      "Account: 3410 GL: 676105.55 TB: 676105.55 \u001b[92mOK\u001b[0m\n",
      "Account: 3415 GL: 3944009.65 TB: 3944009.65 \u001b[92mOK\u001b[0m\n",
      "Account: 3425 GL: 27720296.41 TB: 27720296.41 \u001b[92mOK\u001b[0m\n",
      "Account: 3426 GL: 2181774.25 TB: 2181774.25 \u001b[92mOK\u001b[0m\n",
      "Account: 3709 GL: 2273.0 TB: 2273.0 \u001b[92mOK\u001b[0m\n",
      "Account: 3724 GL: 30.9 TB: 30.9 \u001b[92mOK\u001b[0m\n",
      "Account: 3725 GL: 1190.53 TB: 1190.53 \u001b[92mOK\u001b[0m\n",
      "Account: 3736 GL: 232.2 TB: 232.2 \u001b[92mOK\u001b[0m\n",
      "Account: 3742 GL: 3695.55 TB: 3695.55 \u001b[92mOK\u001b[0m\n",
      "Account: 3743 GL: 478.0 TB: 478.0 \u001b[92mOK\u001b[0m\n",
      "Account: 3800 GL: 245361.97 TB: 245361.97 \u001b[92mOK\u001b[0m\n",
      "Account: 3802 GL: 341508.13 TB: 341508.13 \u001b[92mOK\u001b[0m\n",
      "Account: 3803 GL: 1322757.16 TB: 1322757.16 \u001b[92mOK\u001b[0m\n",
      "Account: 3960 GL: 994262.08 TB: 994262.08 \u001b[92mOK\u001b[0m\n",
      "Account: 3980 GL: 5257533.91 TB: 5257533.91 \u001b[92mOK\u001b[0m\n",
      "Account: 3981 GL: 507819.26 TB: 507819.26 \u001b[92mOK\u001b[0m\n",
      "Account: 4100 GL: 6603664.51 TB: 6603664.51 \u001b[92mOK\u001b[0m\n",
      "Account: 4101 GL: 73287.12 TB: 73287.12 \u001b[92mOK\u001b[0m\n",
      "Account: 4103 GL: 150662.64 TB: 150662.64 \u001b[92mOK\u001b[0m\n",
      "Account: 4106 GL: 599443.25 TB: 599443.25 \u001b[92mOK\u001b[0m\n",
      "Account: 4108 GL: 185215.05 TB: 185215.05 \u001b[92mOK\u001b[0m\n",
      "Account: 4111 GL: 340.0 TB: 340.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4112 GL: 28083.82 TB: 28083.82 \u001b[92mOK\u001b[0m\n",
      "Account: 4115 GL: 207840.33 TB: 207840.33 \u001b[92mOK\u001b[0m\n",
      "Account: 4120 GL: 2280.0 TB: 2280.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4122 GL: 1960.0 TB: 1960.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4123 GL: 27955.27 TB: 27955.27 \u001b[92mOK\u001b[0m\n",
      "Account: 4130 GL: 881013.99 TB: 881013.99 \u001b[92mOK\u001b[0m\n",
      "Account: 4131 GL: 81491.0 TB: 81491.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4134 GL: 185538.63 TB: 185538.63 \u001b[92mOK\u001b[0m\n",
      "Account: 4138 GL: 4119.83 TB: 4119.83 \u001b[92mOK\u001b[0m\n",
      "Account: 4193 GL: 10869.56 TB: 10869.56 \u001b[92mOK\u001b[0m\n",
      "Account: 4200 GL: 18351.93 TB: 18351.93 \u001b[92mOK\u001b[0m\n",
      "Account: 4210 GL: 38558.08 TB: 38558.08 \u001b[92mOK\u001b[0m\n",
      "Account: 4240 GL: 1194368.77 TB: 1194368.77 \u001b[92mOK\u001b[0m\n",
      "Account: 4241 GL: -7575.45 TB: -7575.45 \u001b[92mOK\u001b[0m\n",
      "Account: 4242 GL: 17556.0 TB: 17556.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4252 GL: 51522.17 TB: 51522.17 \u001b[92mOK\u001b[0m\n",
      "Account: 4260 GL: 35208.1 TB: 35208.1 \u001b[92mOK\u001b[0m\n",
      "Account: 4262 GL: 9278.7 TB: 9278.7 \u001b[92mOK\u001b[0m\n",
      "Account: 4263 GL: 44968.81 TB: 44968.81 \u001b[92mOK\u001b[0m\n",
      "Account: 4281 GL: -476.18 TB: -476.18 \u001b[92mOK\u001b[0m\n",
      "Account: 4320 GL: 126914.6 TB: 126914.6 \u001b[92mOK\u001b[0m\n",
      "Account: 4360 GL: 311458.54 TB: 311458.54 \u001b[92mOK\u001b[0m\n",
      "Account: 4361 GL: 10990.68 TB: 10990.68 \u001b[92mOK\u001b[0m\n",
      "Account: 4380 GL: 44331.52 TB: 44331.52 \u001b[92mOK\u001b[0m\n",
      "Account: 4510 GL: 4213.0 TB: 4213.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4520 GL: 34548.88 TB: 34548.88 \u001b[92mOK\u001b[0m\n",
      "Account: 4530 GL: 201970.8 TB: 201970.8 \u001b[92mOK\u001b[0m\n",
      "Account: 4531 GL: 127595.73 TB: 127595.73 \u001b[92mOK\u001b[0m\n",
      "Account: 4532 GL: 15186.03 TB: 15186.03 \u001b[92mOK\u001b[0m\n",
      "Account: 4533 GL: 37481.62 TB: 37481.62 \u001b[92mOK\u001b[0m\n",
      "Account: 4540 GL: 4745.87 TB: 4745.87 \u001b[92mOK\u001b[0m\n",
      "Account: 4541 GL: 3887.56 TB: 3887.56 \u001b[92mOK\u001b[0m\n",
      "Account: 4542 GL: 8126.17 TB: 8126.17 \u001b[92mOK\u001b[0m\n",
      "Account: 4543 GL: 17812.65 TB: 17812.65 \u001b[92mOK\u001b[0m\n",
      "Account: 4550 GL: 1158.96 TB: 1158.96 \u001b[92mOK\u001b[0m\n",
      "Account: 4552 GL: 8811.85 TB: 8811.85 \u001b[92mOK\u001b[0m\n",
      "Account: 4560 GL: 10990.62 TB: 10990.62 \u001b[92mOK\u001b[0m\n",
      "Account: 4561 GL: 611.8 TB: 611.8 \u001b[92mOK\u001b[0m\n",
      "Account: 4563 GL: 4072.83 TB: 4072.83 \u001b[92mOK\u001b[0m\n",
      "Account: 4574 GL: 100.34 TB: 100.34 \u001b[92mOK\u001b[0m\n",
      "Account: 4580 GL: 563.63 TB: 563.63 \u001b[92mOK\u001b[0m\n",
      "Account: 4583 GL: 7.5 TB: 7.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4584 GL: 201.5 TB: 201.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4612 GL: 1755.38 TB: 1755.38 \u001b[92mOK\u001b[0m\n",
      "Account: 4613 GL: 10873.3 TB: 10873.3 \u001b[92mOK\u001b[0m\n",
      "Account: 4614 GL: 48985.5 TB: 48985.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4631 GL: 7250.0 TB: 7250.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4640 GL: 1848.65 TB: 1848.65 \u001b[92mOK\u001b[0m\n",
      "Account: 4642 GL: 31596.37 TB: 31596.37 \u001b[92mOK\u001b[0m\n",
      "Account: 4643 GL: 27089.95 TB: 27089.95 \u001b[92mOK\u001b[0m\n",
      "Account: 4644 GL: 13273.62 TB: 13273.62 \u001b[92mOK\u001b[0m\n",
      "Account: 4645 GL: 302.91 TB: 302.91 \u001b[92mOK\u001b[0m\n",
      "Account: 4650 GL: 4312.49 TB: 4312.49 \u001b[92mOK\u001b[0m\n",
      "Account: 4651 GL: 35.52 TB: 35.52 \u001b[92mOK\u001b[0m\n",
      "Account: 4652 GL: 42.69 TB: 42.69 \u001b[92mOK\u001b[0m\n",
      "Account: 4653 GL: 6.53 TB: 6.53 \u001b[92mOK\u001b[0m\n",
      "Account: 4654 GL: 3310.68 TB: 3310.68 \u001b[92mOK\u001b[0m\n",
      "Account: 4660 GL: 512.45 TB: 512.45 \u001b[92mOK\u001b[0m\n",
      "Account: 4664 GL: 504.0 TB: 504.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4671 GL: 2774.61 TB: 2774.61 \u001b[92mOK\u001b[0m\n",
      "Account: 4673 GL: 400978.55 TB: 400978.55 \u001b[92mOK\u001b[0m\n",
      "Account: 4674 GL: 1287.28 TB: 1287.28 \u001b[92mOK\u001b[0m\n",
      "Account: 4675 GL: 13204.16 TB: 13204.16 \u001b[92mOK\u001b[0m\n",
      "Account: 4710 GL: 5040.0 TB: 5040.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4713 GL: 1899779.85 TB: 1899779.85 \u001b[92mOK\u001b[0m\n",
      "Account: 4714 GL: 13525.2 TB: 13525.2 \u001b[92mOK\u001b[0m\n",
      "Account: 4717 GL: 7885.28 TB: 7885.28 \u001b[92mOK\u001b[0m\n",
      "Account: 4718 GL: 265219.14 TB: 265219.14 \u001b[92mOK\u001b[0m\n",
      "Account: 4730 GL: 1026955.21 TB: 1026955.21 \u001b[92mOK\u001b[0m\n",
      "Account: 4732 GL: 1361750.13 TB: 1361750.13 \u001b[92mOK\u001b[0m\n",
      "Account: 4733 GL: 914591.26 TB: 914591.26 \u001b[92mOK\u001b[0m\n",
      "Account: 4763 GL: 9034.51 TB: 9034.51 \u001b[92mOK\u001b[0m\n",
      "Account: 4764 GL: 1480.54 TB: 1480.54 \u001b[92mOK\u001b[0m\n",
      "Account: 4781 GL: 15541.79 TB: 15541.79 \u001b[92mOK\u001b[0m\n",
      "Account: 4782 GL: 576036.12 TB: 576036.12 \u001b[92mOK\u001b[0m\n",
      "Account: 4784 GL: 4500.0 TB: 4500.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4790 GL: 37451.64 TB: 37451.64 \u001b[92mOK\u001b[0m\n",
      "Account: 4799 GL: 4929.79 TB: 4929.79 \u001b[92mOK\u001b[0m\n",
      "Account: 4800 GL: 252107.63 TB: 252107.63 \u001b[92mOK\u001b[0m\n",
      "Account: 4802 GL: 221.2 TB: 221.2 \u001b[92mOK\u001b[0m\n",
      "Account: 4803 GL: 189096.96 TB: 189096.96 \u001b[92mOK\u001b[0m\n",
      "Account: 4804 GL: 109371.57 TB: 109371.57 \u001b[92mOK\u001b[0m\n",
      "Account: 4805 GL: 1792.74 TB: 1792.74 \u001b[92mOK\u001b[0m\n",
      "Account: 4807 GL: 364692.39 TB: 364692.39 \u001b[92mOK\u001b[0m\n",
      "Account: 4808 GL: 282584.34 TB: 282584.34 \u001b[92mOK\u001b[0m\n",
      "Account: 4809 GL: 34741.63 TB: 34741.63 \u001b[92mOK\u001b[0m\n",
      "Account: 4816 GL: 34716.81 TB: 34716.81 \u001b[92mOK\u001b[0m\n",
      "Account: 4817 GL: 153062.43 TB: 153062.43 \u001b[92mOK\u001b[0m\n",
      "Account: 4822 GL: 102521.0 TB: 102521.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4830 GL: 1422962.88 TB: 1422962.88 \u001b[92mOK\u001b[0m\n",
      "Account: 4900 GL: 10199.65 TB: 10199.65 \u001b[92mOK\u001b[0m\n",
      "Account: 4901 GL: 16714.98 TB: 16714.98 \u001b[92mOK\u001b[0m\n",
      "Account: 4910 GL: 6002.22 TB: 6002.22 \u001b[92mOK\u001b[0m\n",
      "Account: 4911 GL: 1183.53 TB: 1183.53 \u001b[92mOK\u001b[0m\n",
      "Account: 4920 GL: 24426.23 TB: 24426.23 \u001b[92mOK\u001b[0m\n",
      "Account: 4922 GL: 7252.47 TB: 7252.47 \u001b[92mOK\u001b[0m\n",
      "Account: 4923 GL: 13355.6 TB: 13355.6 \u001b[92mOK\u001b[0m\n",
      "Account: 4930 GL: 128.21 TB: 128.21 \u001b[92mOK\u001b[0m\n",
      "Account: 4932 GL: 273.33 TB: 273.33 \u001b[92mOK\u001b[0m\n",
      "Account: 4933 GL: 80786.52 TB: 80786.52 \u001b[92mOK\u001b[0m\n",
      "Account: 4934 GL: 407.65 TB: 407.65 \u001b[92mOK\u001b[0m\n",
      "Account: 4942 GL: 490.98 TB: 490.98 \u001b[92mOK\u001b[0m\n",
      "Account: 4943 GL: 209.09 TB: 209.09 \u001b[92mOK\u001b[0m\n",
      "Account: 4944 GL: 33.5 TB: 33.5 \u001b[92mOK\u001b[0m\n",
      "Account: 4951 GL: 30383.93 TB: 30383.93 \u001b[92mOK\u001b[0m\n",
      "Account: 4952 GL: 34959.2 TB: 34959.2 \u001b[92mOK\u001b[0m\n",
      "Account: 4953 GL: 35110.15 TB: 35110.15 \u001b[92mOK\u001b[0m\n",
      "Account: 4954 GL: 604.54 TB: 604.54 \u001b[92mOK\u001b[0m\n",
      "Account: 4957 GL: 102922.65 TB: 102922.65 \u001b[92mOK\u001b[0m\n",
      "Account: 4960 GL: 14178.0 TB: 14178.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4962 GL: 1855.2 TB: 1855.2 \u001b[92mOK\u001b[0m\n",
      "Account: 4965 GL: 99082.16 TB: 99082.16 \u001b[92mOK\u001b[0m\n",
      "Account: 4967 GL: 4560.0 TB: 4560.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4968 GL: 2880.02 TB: 2880.02 \u001b[92mOK\u001b[0m\n",
      "Account: 4969 GL: 74755.75 TB: 74755.75 \u001b[92mOK\u001b[0m\n",
      "Account: 4970 GL: 36940.54 TB: 36940.54 \u001b[92mOK\u001b[0m\n",
      "Account: 4971 GL: 840.0 TB: 840.0 \u001b[92mOK\u001b[0m\n",
      "Account: 4979 GL: 16504.92 TB: 16504.92 \u001b[92mOK\u001b[0m\n",
      "Account: 4980 GL: 86277.53 TB: 86277.53 \u001b[92mOK\u001b[0m\n",
      "Account: 4981 GL: 3610.97 TB: 3610.97 \u001b[92mOK\u001b[0m\n",
      "Account: 4982 GL: 53404.37 TB: 53404.37 \u001b[92mOK\u001b[0m\n",
      "Account: 4983 GL: 1073087.88 TB: 1073087.88 \u001b[92mOK\u001b[0m\n",
      "Account: 4984 GL: 14745.61 TB: 14745.61 \u001b[92mOK\u001b[0m\n",
      "Account: 4985 GL: 819.56 TB: 819.56 \u001b[92mOK\u001b[0m\n",
      "Account: 4986 GL: 148231.43 TB: 148231.43 \u001b[92mOK\u001b[0m\n",
      "Account: 4987 GL: 8741.19 TB: 8741.19 \u001b[92mOK\u001b[0m\n",
      "Account: 4988 GL: 90147.48 TB: 90147.48 \u001b[92mOK\u001b[0m\n",
      "Account: 4989 GL: 13066.67 TB: 13066.67 \u001b[92mOK\u001b[0m\n",
      "Account: 4991 GL: 34074.81 TB: 34074.81 \u001b[92mOK\u001b[0m\n",
      "Account: 4992 GL: 375.31 TB: 375.31 \u001b[92mOK\u001b[0m\n",
      "Account: 4993 GL: 2190.65 TB: 2190.65 \u001b[92mOK\u001b[0m\n",
      "Account: 8120 GL: 93167.69 TB: 93167.69 \u001b[92mOK\u001b[0m\n",
      "Account: 8125 GL: 412582.09 TB: 412582.09 \u001b[92mOK\u001b[0m\n",
      "Account: 8300 GL: 1861.79 TB: 1861.79 \u001b[92mOK\u001b[0m\n",
      "Account: 8400 GL: 50555.03 TB: 50555.03 \u001b[92mOK\u001b[0m\n",
      "Account: 8700 GL: 145982.18 TB: 145982.18 \u001b[92mOK\u001b[0m\n",
      "Account: 8705 GL: 13806.61 TB: 13806.61 \u001b[92mOK\u001b[0m\n",
      "Account: 8726 GL: 962117.48 TB: 962117.48 \u001b[92mOK\u001b[0m\n",
      "Account: 8737 GL: 12918.58 TB: 12918.58 \u001b[92mOK\u001b[0m\n",
      "Account: 8738 GL: 239700.21 TB: 239700.21 \u001b[92mOK\u001b[0m\n",
      "Account: 8739 GL: 87437.16 TB: 87437.16 \u001b[92mOK\u001b[0m\n",
      "Account: 8740 GL: 1283850.17 TB: 1283850.17 \u001b[92mOK\u001b[0m\n",
      "Account: 8741 GL: 15378.96 TB: 15378.96 \u001b[92mOK\u001b[0m\n",
      "Account: 8970 GL: 203833.72 TB: 203833.72 \u001b[92mOK\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# compare the sums of the GL and the TB for each debit account and save the difference\n",
    "\n",
    "acc_with_diff_debit = []\n",
    "sum_diff_debit = 0\n",
    "\n",
    "for acc in df_debit_gl_tb[\"debit_account\"]:\n",
    "    sum_gl = round(df_debit_gl_tb[\"debit_amount\"].loc[df_debit_gl_tb.index[df_debit_gl_tb[\"debit_account\"] == acc]].sum(), 2)\n",
    "    sum_tb = round(df_tb[\"soll_periode_12\"].loc[df_tb.index[df_tb[\"kontonummer\"] == acc]].sum(), 2)\n",
    "    if sum_gl != sum_tb:\n",
    "        acc_with_diff_debit.append(acc)\n",
    "        sum_diff_debit += sum_gl - sum_tb\n",
    "        print(f\"Account: {acc} GL: {sum_gl} TB: {sum_tb} {bcolors.FAIL}NOT OK{bcolors.ENDC}\")\n",
    "    else:\n",
    "        print(f\"Account: {acc} GL: {sum_gl} TB: {sum_tb} {bcolors.OKGREEN}OK{bcolors.ENDC}\")\n",
    "\n",
    "pd.DataFrame(acc_with_diff_debit).to_excel('./results/JB1/debit_diff_gl_tb.xlsx', sheet_name='GL vs TB' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4f34138c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the difference between the sum of debit and credit accounts in the GL and the TB is identical and therefore balanced between the GL and the TB\n",
    "\n",
    "print(np.abs(sum_diff_debit), np.abs(sum_diff_credit))\n",
    "\n",
    "plt.bar(x=[\"Credit\", \"Debit\"], height=[np.abs(sum_diff_credit), np.abs(sum_diff_debit)], color=[\"black\", \"gray\"], width=0.5, align=\"center\", edgecolor=\"black\")\n",
    "plt.title(\"Difference between debit and credit accounts\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2523ab59",
   "metadata": {},
   "source": [
    "### Compare all accounts in the GL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9642bd3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Account: 22 Debit: 14495.0 Credit: 0.0 \u001b[91m Diff: 14495.0\u001b[0m\n",
      "Account: 23 Debit: 22620.0 Credit: 14495.0 \u001b[91m Diff: 8125.0\u001b[0m\n",
      "Account: 26 Debit: 8125.0 Credit: 26646.0 \u001b[91m Diff: -18521.0\u001b[0m\n",
      "Account: 27 Debit: 61176.15 Credit: 1231.25 \u001b[91m Diff: 59944.9\u001b[0m\n",
      "Account: 176 Debit: 20737.13 Credit: 0.0 \u001b[91m Diff: 20737.13\u001b[0m\n",
      "Account: 210 Debit: 54051.7 Credit: 0.0 \u001b[91m Diff: 54051.7\u001b[0m\n",
      "Account: 280 Debit: 206000.0 Credit: 0.0 \u001b[91m Diff: 206000.0\u001b[0m\n",
      "Account: 291 Debit: 245721.14 Credit: 0.0 \u001b[91m Diff: 245721.14\u001b[0m\n",
      "Account: 320 Debit: 4627.35 Credit: 0.0 \u001b[91m Diff: 4627.35\u001b[0m\n",
      "Account: 354 Debit: 10000.0 Credit: 0.0 \u001b[91m Diff: 10000.0\u001b[0m\n",
      "Account: 381 Debit: 24500.0 Credit: 0.0 \u001b[91m Diff: 24500.0\u001b[0m\n",
      "Account: 384 Debit: 5725.0 Credit: 0.0 \u001b[91m Diff: 5725.0\u001b[0m\n",
      "Account: 400 Debit: 91896.33 Credit: 62119.9 \u001b[91m Diff: 29776.43\u001b[0m\n",
      "Account: 403 Debit: 745421.87 Credit: 61806.49 \u001b[91m Diff: 683615.38\u001b[0m\n",
      "Account: 411 Debit: 319108.38 Credit: 59944.9 \u001b[91m Diff: 259163.48\u001b[0m\n",
      "Account: 412 Debit: 10490.55 Credit: 0.0 \u001b[91m Diff: 10490.55\u001b[0m\n",
      "Account: 415 Debit: 13289.58 Credit: 175102.59 \u001b[91m Diff: -161813.01\u001b[0m\n",
      "Account: 416 Debit: 4329.0 Credit: 0.0 \u001b[91m Diff: 4329.0\u001b[0m\n",
      "Account: 423 Debit: 32177.85 Credit: 5998.0 \u001b[91m Diff: 26179.85\u001b[0m\n",
      "Account: 480 Debit: 4276.8 Credit: 21683.0 \u001b[91m Diff: -17406.2\u001b[0m\n",
      "Account: 483 Debit: 43838.79 Credit: 26432.59 \u001b[91m Diff: 17406.2\u001b[0m\n",
      "Account: 514 Debit: 28750.0 Credit: 0.0 \u001b[91m Diff: 28750.0\u001b[0m\n",
      "Account: 529 Debit: 41376.51 Credit: 56613.1 \u001b[91m Diff: -15236.589999999997\u001b[0m\n",
      "Account: 532 Debit: 528.4 Credit: 0.0 \u001b[91m Diff: 528.4\u001b[0m\n",
      "Account: 662 Debit: 19914.33 Credit: 140.09 \u001b[91m Diff: 19774.24\u001b[0m\n",
      "Account: 664 Debit: 68336.97 Credit: 809.83 \u001b[91m Diff: 67527.14\u001b[0m\n",
      "Account: 665 Debit: 25050.01 Credit: 0.02 \u001b[91m Diff: 25049.989999999998\u001b[0m\n",
      "Account: 666 Debit: 18644.22 Credit: 329.92 \u001b[91m Diff: 18314.300000000003\u001b[0m\n",
      "Account: 667 Debit: 39732.92 Credit: 1355.56 \u001b[91m Diff: 38377.36\u001b[0m\n",
      "Account: 668 Debit: 170737.92 Credit: 9114.72 \u001b[91m Diff: 161623.2\u001b[0m\n",
      "Account: 670 Debit: 51553.56 Credit: 2119.91 \u001b[91m Diff: 49433.649999999994\u001b[0m\n",
      "Account: 671 Debit: 54843.12 Credit: 2253.2 \u001b[91m Diff: 52589.920000000006\u001b[0m\n",
      "Account: 672 Debit: 7874999.4 Credit: 7624999.32 \u001b[91m Diff: 250000.08000000007\u001b[0m\n",
      "Account: 673 Debit: 176286.48 Credit: 12887.14 \u001b[91m Diff: 163399.34000000003\u001b[0m\n",
      "Account: 674 Debit: 55114.32 Credit: 4312.03 \u001b[91m Diff: 50802.29\u001b[0m\n",
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      "Account: 73032 Debit: 31038.2 Credit: 32612.41 \u001b[91m Diff: -1574.2099999999991\u001b[0m\n",
      "Account: 73044 Debit: 119720.0 Credit: 86400.0 \u001b[91m Diff: 33320.0\u001b[0m\n",
      "Account: 73047 Debit: 104077.8 Credit: 97389.8 \u001b[91m Diff: 6688.0\u001b[0m\n",
      "Account: 73049 Debit: 5523.59 Credit: 5718.04 \u001b[91m Diff: -194.44999999999982\u001b[0m\n",
      "Account: 73064 Debit: 65518.88 Credit: 66312.08 \u001b[91m Diff: -793.2000000000044\u001b[0m\n",
      "Account: 73070 Debit: 31275.87 Credit: 33999.11 \u001b[91m Diff: -2723.2400000000016\u001b[0m\n",
      "Account: 73079 Debit: 32170.82 Credit: 30902.11 \u001b[91m Diff: 1268.7099999999991\u001b[0m\n",
      "Account: 73088 Debit: 16928.79 Credit: 16646.79 \u001b[91m Diff: 282.0\u001b[0m\n",
      "Account: 73096 Debit: 4365.5 Credit: 4917.65 \u001b[91m Diff: -552.1499999999996\u001b[0m\n",
      "Account: 73098 Debit: 22385.25 Credit: 23137.25 \u001b[91m Diff: -752.0\u001b[0m\n",
      "Account: 73099 Debit: 16469.0 Credit: 0.0 \u001b[91m Diff: 16469.0\u001b[0m\n",
      "Account: 73102 Debit: 2517.32 Credit: 2729.0 \u001b[91m Diff: -211.67999999999984\u001b[0m\n",
      "Account: 73108 Debit: 5693.2 Credit: 0.0 \u001b[91m Diff: 5693.2\u001b[0m\n",
      "Account: 73112 Debit: 519145.69 Credit: 546314.3 \u001b[91m Diff: -27168.610000000044\u001b[0m\n",
      "Account: 73117 Debit: 233193.6 Credit: 299318.4 \u001b[91m Diff: -66124.80000000002\u001b[0m\n",
      "Account: 73126 Debit: 291.5 Credit: 749.5 \u001b[91m Diff: -458.0\u001b[0m\n",
      "Account: 73128 Debit: 19964.0 Credit: 12694.0 \u001b[91m Diff: 7270.0\u001b[0m\n",
      "Account: 73134 Debit: 5850.0 Credit: 5843.0 \u001b[91m Diff: 7.0\u001b[0m\n",
      "Account: 73137 Debit: 236106.29 Credit: 250390.89 \u001b[91m Diff: -14284.600000000006\u001b[0m\n",
      "Account: 73138 Debit: 54.0 Credit: 27.0 \u001b[91m Diff: 27.0\u001b[0m\n",
      "Account: 73141 Debit: 9439.69 Credit: 10337.85 \u001b[91m Diff: -898.1599999999999\u001b[0m\n",
      "Account: 73143 Debit: 147481.4 Credit: 146806.4 \u001b[91m Diff: 675.0\u001b[0m\n",
      "Account: 73149 Debit: 2040.0 Credit: 4080.0 \u001b[91m Diff: -2040.0\u001b[0m\n",
      "Account: 73162 Debit: 37355.0 Credit: 38405.0 \u001b[91m Diff: -1050.0\u001b[0m\n",
      "Account: 73182 Debit: 3024.0 Credit: 3892.0 \u001b[91m Diff: -868.0\u001b[0m\n",
      "Account: 73191 Debit: 821050.64 Credit: 887775.29 \u001b[91m Diff: -66724.65000000002\u001b[0m\n",
      "Account: 73203 Debit: 5800.0 Credit: 6065.0 \u001b[91m Diff: -265.0\u001b[0m\n",
      "Account: 73207 Debit: 40365.0 Credit: 44811.0 \u001b[91m Diff: -4446.0\u001b[0m\n",
      "Account: 73210 Debit: 24738.0 Credit: 31518.0 \u001b[91m Diff: -6780.0\u001b[0m\n",
      "Account: 73216 Debit: 115531.65 Credit: 142821.9 \u001b[91m Diff: -27290.25\u001b[0m\n",
      "Account: 73236 Debit: 35325.34 Credit: 52829.12 \u001b[91m Diff: -17503.780000000006\u001b[0m\n",
      "Account: 79999 Debit: 33523.3 Credit: 0.0 \u001b[91m Diff: 33523.3\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "gl_acc = []\n",
    "gl_acc_diff = []\n",
    "df_gl_acc_diff = pd.DataFrame()\n",
    "\n",
    "\n",
    "for acc in df_gl_sums[\"debit_account\"]:\n",
    "    sum_debit = round(df_gl_sums[\"debit_amount\"].loc[df_gl_sums.index[df_gl_sums[\"debit_account\"] == acc]].sum(), 2)\n",
    "    sum_credit = round(df_gl_sums[\"credit_amount\"].loc[df_gl_sums.index[df_gl_sums[\"credit_account\"] == acc]].sum(), 2)\n",
    "    diff = sum_debit - sum_credit\n",
    "    gl_acc.append(acc)\n",
    "    gl_acc_diff.append(diff)\n",
    "    if diff != 0:\n",
    "        print(f\"Account: {acc} Debit: {sum_debit} Credit: {sum_credit} {bcolors.FAIL} Diff: {diff}{bcolors.ENDC}\")\n",
    "\n",
    "df_gl_acc_diff[\"konto\"] = gl_acc\n",
    "df_gl_acc_diff[\"debit\"] = [round(df_gl_sums[\"debit_amount\"].loc[df_gl_sums.index[df_gl_sums[\"debit_account\"] == acc]].sum(), 2) for acc in gl_acc]\n",
    "df_gl_acc_diff[\"credit\"] = [round(df_gl_sums[\"credit_amount\"].loc[df_gl_sums.index[df_gl_sums[\"credit_account\"] == acc]].sum(), 2) for acc in gl_acc]\n",
    "df_gl_acc_diff[\"diff\"] = gl_acc_diff\n",
    "\n",
    "\n",
    "df_gl_acc_diff = df_gl_acc_diff.drop(df_gl_acc_diff[(df_gl_acc_diff[\"konto\"].isna()) | (df_gl_acc_diff[\"diff\"] == 0)].index).reset_index(drop=True)\n",
    "\n",
    "if df_gl_acc_diff.empty == False:\n",
    "    df_gl_acc_diff.to_excel('./results/JB1/gl_acc_diff.xlsx', sheet_name='GL acc difference' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB1/no_gl_acc_diff.txt', 'w') as f:\n",
    "        f.write('No difference between debit and credit accounts in the GL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8c81ad41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19087149.740000013"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the difference for all accounts in the GL is ~19,089,361.26M EUR\n",
    "# I cannot explain this difference and therefore I will not investigate it further\n",
    "\n",
    "df_gl_acc_diff[\"diff\"].sum()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0d5735c8",
   "metadata": {},
   "source": [
    "### Compare all individual bookings in the GL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "019f7e7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 123707\n",
      "Number of unique booking numbers: 39212\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
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       "      <td>72072</td>\n",
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       "      <th>3</th>\n",
       "      <td>483931</td>\n",
       "      <td>4138</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>483931</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71781</td>\n",
       "      <td>1533.08</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr debit_account  debit_amount credit_account  credit_amount\n",
       "0        483476          4360         51.86           <NA>            NaN\n",
       "1        483476          <NA>           NaN          72072          51.86\n",
       "2        483476          <NA>           NaN           1600          51.86\n",
       "3        483931          4138       1533.08           <NA>            NaN\n",
       "4        483931          <NA>           NaN          71781        1533.08"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prepare dataset with the booking number, account number, debit and credit amount\n",
    "\n",
    "df_gl_acc_nr = df_gl[[\"booking_nr\", \"debit_account\", \"debit_amount\", \"credit_account\", \"credit_amount\"]].copy().reset_index(drop=True)\n",
    "df_gl_acc_nr[\"credit_account\"] = df_gl_acc_nr[\"credit_account\"].astype(str)\n",
    "df_gl_acc_nr[\"debit_account\"] = df_gl_acc_nr[\"debit_account\"].astype(str)\n",
    "\n",
    "print(f\"Number of rows: {len(df_gl_acc_nr)}\")\n",
    "print(f\"Number of unique booking numbers: {len(df_gl_acc_nr['booking_nr'].unique())}\")\n",
    "\n",
    "df_gl_acc_nr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "552ddb5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>booking_nr</th>\n",
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       "      <td>392.00</td>\n",
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       "      <td>1563</td>\n",
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       "      <td>1763</td>\n",
       "      <td>33.32</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3425</td>\n",
       "      <td>1936.26</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1563</td>\n",
       "      <td>329.16</td>\n",
       "      <td>1763</td>\n",
       "      <td>329.16</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483171</td>\n",
       "      <td>4983</td>\n",
       "      <td>416.00</td>\n",
       "      <td>722011600</td>\n",
       "      <td>832.00</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>39207</th>\n",
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       "      <td>-6750.00</td>\n",
       "      <td>723241600</td>\n",
       "      <td>-13500.00</td>\n",
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       "      <th>39208</th>\n",
       "      <td>538495</td>\n",
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       "      <td>1762</td>\n",
       "      <td>-1147.50</td>\n",
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       "      <th>39209</th>\n",
       "      <td>538731</td>\n",
       "      <td>1536</td>\n",
       "      <td>4.51</td>\n",
       "      <td>2650</td>\n",
       "      <td>4.51</td>\n",
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       "    <tr>\n",
       "      <th>39210</th>\n",
       "      <td>538768</td>\n",
       "      <td>1536</td>\n",
       "      <td>6.68</td>\n",
       "      <td>2650</td>\n",
       "      <td>6.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39211</th>\n",
       "      <td>540032</td>\n",
       "      <td>1500</td>\n",
       "      <td>250.00</td>\n",
       "      <td>1536</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39212 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name  booking_nr debit_account  debit_amount credit_account  credit_amount\n",
       "0         483163          4988        196.00      725551600         392.00\n",
       "1         483164          1563         33.32           1763          33.32\n",
       "2         483168          3425       1936.26      706951600        3872.52\n",
       "3         483169          1563        329.16           1763         329.16\n",
       "4         483171          4983        416.00      722011600         832.00\n",
       "...          ...           ...           ...            ...            ...\n",
       "39207     538494          4953      -6750.00      723241600      -13500.00\n",
       "39208     538495          1562      -1147.50           1762       -1147.50\n",
       "39209     538731          1536          4.51           2650           4.51\n",
       "39210     538768          1536          6.68           2650           6.68\n",
       "39211     540032          1500        250.00           1536         250.00\n",
       "\n",
       "[39212 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate the sum of debit and credit amounts for each booking number\n",
    "# the amount of unique booking numbers is identical to length of the dataset\n",
    "\n",
    "df_gl_acc_nr_sums = df_gl_acc_nr.groupby([\"booking_nr\"]).sum().reset_index()\n",
    "df_gl_acc_nr_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1cd742f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the difference: 1872911.169999987\n",
      "Number of booking numbers with difference: 29166\n",
      "Amounts equal the overall difference: True\n"
     ]
    },
    {
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       "      <td>2035626.02</td>\n",
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       "      <td>2501</td>\n",
       "      <td>1017813.01</td>\n",
       "      <td>299981476</td>\n",
       "      <td>2035626.02</td>\n",
       "      <td>-1017813.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39212 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name  booking_nr debit_account  debit_amount credit_account  credit_amount  \\\n",
       "0         502979     299981476    2035626.02           2502     1017813.01   \n",
       "1         508897     299981476    2015558.76           2502     1007779.38   \n",
       "2         489814     299981476    1953102.06           2502      976551.03   \n",
       "3         495762     299981476    1923330.38           2502      961665.19   \n",
       "4         493141     299981476    1631947.94           2502      815973.97   \n",
       "...          ...           ...           ...            ...            ...   \n",
       "39207     493139          2501     815973.97      299981476     1631947.94   \n",
       "39208     495760          2501     961665.19      299981476     1923330.38   \n",
       "39209     489812          2501     976551.03      299981476     1953102.06   \n",
       "39210     508895          2501    1007779.38      299981476     2015558.76   \n",
       "39211     502977          2501    1017813.01      299981476     2035626.02   \n",
       "\n",
       "Name         diff  \n",
       "0      1017813.01  \n",
       "1      1007779.38  \n",
       "2       976551.03  \n",
       "3       961665.19  \n",
       "4       815973.97  \n",
       "...           ...  \n",
       "39207  -815973.97  \n",
       "39208  -961665.19  \n",
       "39209  -976551.03  \n",
       "39210 -1007779.38  \n",
       "39211 -1017813.01  \n",
       "\n",
       "[39212 rows x 6 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the sum of debit and credit amounts for each booking number \n",
    "\n",
    "df_gl_acc_nr_sums[\"diff\"] = df_gl_acc_nr_sums[\"debit_amount\"] - df_gl_acc_nr_sums[\"credit_amount\"]\n",
    "df_gl_acc_nr_sums = df_gl_acc_nr_sums.sort_values(by=[\"diff\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "print(f\"Sum of the difference: {df_gl_acc_nr_sums['diff'].sum()}\")\n",
    "print(f\"Number of booking numbers with difference: {len(df_gl_acc_nr_sums[df_gl_acc_nr_sums['diff'] != 0])}\")\n",
    "print(f\"Amounts equal the overall difference: {round(df_gl_acc_nr_sums['diff'].sum(), 2) == round(gl_diff_debit_credit, 2)}\")\n",
    "\n",
    "df_gl_acc_nr_sums.to_excel('./results/JB1/sums_per_booking_nr.xlsx', sheet_name='Sums per booking number' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "\n",
    "df_gl_acc_nr_sums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a918bc8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the difference: 12424032.63\n",
      "Number of booking numbers with difference: 8409\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
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       "      <th>0</th>\n",
       "      <td>502979</td>\n",
       "      <td>299981476</td>\n",
       "      <td>2035626.02</td>\n",
       "      <td>2502</td>\n",
       "      <td>1017813.01</td>\n",
       "      <td>1017813.01</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>489814</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1953102.06</td>\n",
       "      <td>2502</td>\n",
       "      <td>976551.03</td>\n",
       "      <td>976551.03</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>493141</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1631947.94</td>\n",
       "      <td>2502</td>\n",
       "      <td>815973.97</td>\n",
       "      <td>815973.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>499593</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1613692.94</td>\n",
       "      <td>2502</td>\n",
       "      <td>806846.47</td>\n",
       "      <td>806846.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>512669</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1518827.48</td>\n",
       "      <td>2502</td>\n",
       "      <td>759413.74</td>\n",
       "      <td>759413.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>8405</th>\n",
       "      <td>489502</td>\n",
       "      <td>1601</td>\n",
       "      <td>580796.43</td>\n",
       "      <td>599991400</td>\n",
       "      <td>1161592.86</td>\n",
       "      <td>-580796.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8406</th>\n",
       "      <td>493522</td>\n",
       "      <td>12102150</td>\n",
       "      <td>746886.99</td>\n",
       "      <td>203461475203461475</td>\n",
       "      <td>1493773.98</td>\n",
       "      <td>-746886.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8407</th>\n",
       "      <td>507384</td>\n",
       "      <td>2501</td>\n",
       "      <td>789678.37</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1579356.74</td>\n",
       "      <td>-789678.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8408</th>\n",
       "      <td>495760</td>\n",
       "      <td>2501</td>\n",
       "      <td>961665.19</td>\n",
       "      <td>299981476</td>\n",
       "      <td>1923330.38</td>\n",
       "      <td>-961665.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8409</th>\n",
       "      <td>508895</td>\n",
       "      <td>2501</td>\n",
       "      <td>1007779.38</td>\n",
       "      <td>299981476</td>\n",
       "      <td>2015558.76</td>\n",
       "      <td>-1007779.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8410 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr debit_account  debit_amount      credit_account  \\\n",
       "0        502979     299981476    2035626.02                2502   \n",
       "1        489814     299981476    1953102.06                2502   \n",
       "2        493141     299981476    1631947.94                2502   \n",
       "3        499593     299981476    1613692.94                2502   \n",
       "4        512669     299981476    1518827.48                2502   \n",
       "...         ...           ...           ...                 ...   \n",
       "8405     489502          1601     580796.43           599991400   \n",
       "8406     493522      12102150     746886.99  203461475203461475   \n",
       "8407     507384          2501     789678.37           299981476   \n",
       "8408     495760          2501     961665.19           299981476   \n",
       "8409     508895          2501    1007779.38           299981476   \n",
       "\n",
       "Name  credit_amount        diff  \n",
       "0        1017813.01  1017813.01  \n",
       "1         976551.03   976551.03  \n",
       "2         815973.97   815973.97  \n",
       "3         806846.47   806846.47  \n",
       "4         759413.74   759413.74  \n",
       "...             ...         ...  \n",
       "8405     1161592.86  -580796.43  \n",
       "8406     1493773.98  -746886.99  \n",
       "8407     1579356.74  -789678.37  \n",
       "8408     1923330.38  -961665.19  \n",
       "8409     2015558.76 -1007779.38  \n",
       "\n",
       "[8410 rows x 6 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclude equal bookings with the same debit and credit amount\n",
    "\n",
    "df_gl_acc_nr_sums_diff = df_gl_acc_nr_sums\n",
    "df_gl_acc_nr_sums_diff[\"diff\"] = df_gl_acc_nr_sums_diff[\"diff\"].astype(int)\n",
    "df_gl_acc_nr_sums_diff[\"diff\"] = np.abs(df_gl_acc_nr_sums_diff[\"diff\"])\n",
    "df_gl_acc_nr_sums_diff = df_gl_acc_nr_sums_diff.sort_values(by=[\"diff\"], ascending=False).reset_index(drop=True).drop_duplicates(subset=[\"diff\"], keep=\"first\").reset_index(drop=True)\n",
    "\n",
    "# recalculate the difference to invert the creation of absolute values\n",
    "df_gl_acc_nr_sums_diff[\"diff\"] = df_gl_acc_nr_sums_diff[\"debit_amount\"] - df_gl_acc_nr_sums_diff[\"credit_amount\"]\n",
    "df_gl_acc_nr_sums_diff = df_gl_acc_nr_sums_diff.sort_values(by=[\"diff\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "\n",
    "print(f\"Sum of the difference: {round(df_gl_acc_nr_sums_diff['diff'].sum(),2)}\")\n",
    "print(f\"Number of booking numbers with difference: {len(df_gl_acc_nr_sums_diff[df_gl_acc_nr_sums_diff['diff'] != 0])}\")\n",
    "\n",
    "df_gl_acc_nr_sums_diff.to_excel('./results/JB1/sums_per_booking_nr_diff.xlsx', sheet_name='Sums per booking number' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "\n",
    "df_gl_acc_nr_sums_diff"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d62b5c4b",
   "metadata": {},
   "source": [
    "### GL only with TB accounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cac7e095",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 91158\n",
      "Get relative number of rows: 73.69 %\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>line_nr</th>\n",
       "      <th>document_date</th>\n",
       "      <th>document_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>currency</th>\n",
       "      <th>user</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>document_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>483476</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>R02289401 2022</td>\n",
       "      <td>4360</td>\n",
       "      <td>51.86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lalux - Leudelange</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>01/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>483476</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>R02289401 2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600</td>\n",
       "      <td>51.86</td>\n",
       "      <td>lalux - Leudelange</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>03/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>483931</td>\n",
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       "      <td>2021-06-06</td>\n",
       "      <td>3022100219105-01</td>\n",
       "      <td>4138</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>04/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483931</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>3022100219105-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600</td>\n",
       "      <td>1533.08</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>06/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>483993</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>3022100219105-02</td>\n",
       "      <td>4138</td>\n",
       "      <td>1533.05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>07/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>483993</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>3022100219105-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600</td>\n",
       "      <td>1533.05</td>\n",
       "      <td>BG RCI - Heidelberg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>09/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>484553</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-09-18</td>\n",
       "      <td>JSH210001-1195</td>\n",
       "      <td>3400</td>\n",
       "      <td>65963.28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sanhu - Nanjing (CN)</td>\n",
       "      <td>USD</td>\n",
       "      <td>hess</td>\n",
       "      <td>10/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>484553</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-09-18</td>\n",
       "      <td>JSH210001-1195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600</td>\n",
       "      <td>65963.28</td>\n",
       "      <td>Sanhu - Nanjing (CN)</td>\n",
       "      <td>USD</td>\n",
       "      <td>hess</td>\n",
       "      <td>12/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>484554</td>\n",
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       "      <td>2021-09-18</td>\n",
       "      <td>JSH210001-1195</td>\n",
       "      <td>1561</td>\n",
       "      <td>11213.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sanhu - Nanjing (CN)</td>\n",
       "      <td>USD</td>\n",
       "      <td>hess</td>\n",
       "      <td>13/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>484554</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-09-18</td>\n",
       "      <td>JSH210001-1195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1761</td>\n",
       "      <td>11213.76</td>\n",
       "      <td>Sanhu - Nanjing (CN)</td>\n",
       "      <td>USD</td>\n",
       "      <td>hess</td>\n",
       "      <td>14/01/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr  line_nr document_date       document_nr debit_account  \\\n",
       "0        483476        1    2021-01-01    R02289401 2022          4360   \n",
       "1        483476        3    2021-01-01    R02289401 2022           NaN   \n",
       "2        483931        1    2021-06-06  3022100219105-01          4138   \n",
       "3        483931        3    2021-06-06  3022100219105-01           NaN   \n",
       "4        483993        1    2021-06-06  3022100219105-02          4138   \n",
       "5        483993        3    2021-06-06  3022100219105-02           NaN   \n",
       "6        484553        1    2021-09-18    JSH210001-1195          3400   \n",
       "7        484553        3    2021-09-18    JSH210001-1195           NaN   \n",
       "8        484554        1    2021-09-18    JSH210001-1195          1561   \n",
       "9        484554        2    2021-09-18    JSH210001-1195           NaN   \n",
       "\n",
       "Name  debit_amount credit_account  credit_amount           description  \\\n",
       "0            51.86            NaN            NaN    lalux - Leudelange   \n",
       "1              NaN           1600          51.86    lalux - Leudelange   \n",
       "2          1533.08            NaN            NaN   BG RCI - Heidelberg   \n",
       "3              NaN           1600        1533.08   BG RCI - Heidelberg   \n",
       "4          1533.05            NaN            NaN   BG RCI - Heidelberg   \n",
       "5              NaN           1600        1533.05   BG RCI - Heidelberg   \n",
       "6         65963.28            NaN            NaN  Sanhu - Nanjing (CN)   \n",
       "7              NaN           1600       65963.28  Sanhu - Nanjing (CN)   \n",
       "8         11213.76            NaN            NaN  Sanhu - Nanjing (CN)   \n",
       "9              NaN           1761       11213.76  Sanhu - Nanjing (CN)   \n",
       "\n",
       "Name currency  user booking_date document_source  \n",
       "0         EUR  hess   01/01/2022    stapelbuchen  \n",
       "1         EUR  hess   03/01/2022    stapelbuchen  \n",
       "2         EUR  hess   04/01/2022    stapelbuchen  \n",
       "3         EUR  hess   06/01/2022    stapelbuchen  \n",
       "4         EUR  hess   07/01/2022    stapelbuchen  \n",
       "5         EUR  hess   09/01/2022    stapelbuchen  \n",
       "6         USD  hess   10/01/2022    stapelbuchen  \n",
       "7         USD  hess   12/01/2022    stapelbuchen  \n",
       "8         USD  hess   13/01/2022    stapelbuchen  \n",
       "9         USD  hess   14/01/2022    stapelbuchen  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract all account numbers from the GL which are present in the TB\n",
    "\n",
    "df_gl_tb_acc = df_gl.loc[df_gl[\"debit_account\"].isin(df_tb[\"kontonummer\"]) | df_gl[\"credit_account\"].isin(df_tb[\"kontonummer\"])]\n",
    "df_gl_tb_acc = df_gl_tb_acc.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of rows: {len(df_gl_tb_acc)}\")\n",
    "print(f\"Get relative number of rows: {round(len(df_gl_tb_acc) / len(df_gl) * 100, 2)} %\")\n",
    "\n",
    "df_gl_tb_acc.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3b00605c",
   "metadata": {},
   "source": [
    "### Credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5c77f0ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the GL: 401866597.23\n",
      "Sum of is equal to the sum of the TB: True\n"
     ]
    }
   ],
   "source": [
    "gl_tb_acc_credit = round(df_gl_tb_acc[\"debit_amount\"].sum(), 2)\n",
    "\n",
    "print(f\"Sum of the GL: {gl_tb_acc_credit}\")\n",
    "print(f\"Sum of is equal to the sum of the TB: {gl_tb_acc_credit == round(df_tb['soll_periode_12'].sum(), 2)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "efae8658",
   "metadata": {},
   "source": [
    "### Debit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1212ebb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of the GL: 401866597.23\n",
      "Sum is equal to the sum of the TB: True\n"
     ]
    }
   ],
   "source": [
    "gl_tb_acc_debit = round(df_gl_tb_acc[\"credit_amount\"].sum(), 2)\n",
    "\n",
    "print(f\"Sum of the GL: {gl_tb_acc_debit}\")\n",
    "print(f\"Sum is equal to the sum of the TB: {gl_tb_acc_debit == round(df_tb['haben_periode_12'].sum(), 2)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5b33701d",
   "metadata": {},
   "source": [
    "### Difference "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be0c5a21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Credit: 401866597.23 Debit: 401866597.23 Diff: 0.0\n",
      "Credit 0.0 True\n",
      "Debit 0.0 False\n"
     ]
    }
   ],
   "source": [
    "# the difference is equal to the difference of the sum \n",
    "\n",
    "print(f\"Credit: {gl_tb_acc_credit} Debit: {gl_tb_acc_debit} Diff: {gl_tb_acc_debit - gl_tb_acc_credit}\")\n",
    "print(f\"Credit {round((df_tb['haben_periode_12'].sum() - gl_tb_acc_credit), 2)} {df_tb['haben_periode_12'].sum() - gl_tb_acc_credit == sum_diff_credit}\")\n",
    "print(f\"Debit {round((df_tb['soll_periode_12'].sum() - gl_tb_acc_debit), 2)} {df_tb['soll_periode_12'].sum() - gl_tb_acc_debit == sum_diff_credit}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "87403f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize the results of the reconciliation of the GL only with TB accounts\n",
    "\n",
    "df_reconciliation_gl_tb_acc = pd.DataFrame({\"GL\": [gl_tb_acc_credit, gl_tb_acc_debit, gl_tb_acc_debit - gl_tb_acc_credit], \"TB\": [df_tb['soll_periode_12'].sum(), df_tb['haben_periode_12'].sum(), df_tb['haben_periode_12'].sum() - df_tb['soll_periode_12'].sum()]}, index=[\"Credit\", \"Debit\", \"Diff\"])\n",
    "\n",
    "df_reconciliation_gl_tb_acc.to_excel('./results/JB0/reconciliation_gl_tb_acc.xlsx', sheet_name='reconciliation w tb acc' ,index=True, header=True, float_format=\"%.2f\", engine='xlsxwriter')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5843a452",
   "metadata": {},
   "source": [
    "## JB 2: Number of transactions / preparer ID"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7264c92",
   "metadata": {},
   "source": [
    "This report contains a summary of the total current period journal entry transactions by Account Class and the Preparer-ID over a defined time frame (for example, by month or period). Account Classes are defined by the user as an input parameter."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac41dcb4",
   "metadata": {},
   "source": [
    "### Total amounts per preparer ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "917380cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number users: 2\n",
      "Number users with difference: 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>Name</th>\n",
       "      <th>user</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>1.346261e+08</td>\n",
       "      <td>1.330098e+08</td>\n",
       "      <td>1616292.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hess</td>\n",
       "      <td>4.202088e+08</td>\n",
       "      <td>4.199522e+08</td>\n",
       "      <td>256618.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name       user  debit_amount  credit_amount        diff\n",
       "0     lohnsdorf  1.346261e+08   1.330098e+08  1616292.47\n",
       "1          hess  4.202088e+08   4.199522e+08   256618.70"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the total debit amount per preparer ID \n",
    "\n",
    "df_userid_totals = df_gl.groupby([\"user\"])\n",
    "df_userid_totals = df_userid_totals[[\"debit_amount\", \"credit_amount\"]].sum().reset_index()\n",
    "df_userid_totals[\"diff\"] = df_userid_totals[\"debit_amount\"] - df_userid_totals[\"credit_amount\"]\n",
    "df_userid_totals = df_userid_totals.sort_values(by=[\"diff\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "df_userid_totals.to_excel(\"./results/JB2/userid_totals.xlsx\" , sheet_name='User ID totals' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "\n",
    "print(f\"Number users: {len(df_userid_totals)}\")\n",
    "print(f\"Number users with difference: {len(df_userid_totals[df_userid_totals['diff'] != 0])}\")\n",
    "\n",
    "df_userid_totals"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "287998d7",
   "metadata": {},
   "source": [
    "### Debit amounts per preparer ID and account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "dd8dabfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>user</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>soll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hess</td>\n",
       "      <td>1600</td>\n",
       "      <td>53548310.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hess</td>\n",
       "      <td>1400</td>\n",
       "      <td>41013226.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hess</td>\n",
       "      <td>1210</td>\n",
       "      <td>28211447.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hess</td>\n",
       "      <td>1200</td>\n",
       "      <td>28120290.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hess</td>\n",
       "      <td>3425</td>\n",
       "      <td>22302711.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>79999</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2680</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>4281</td>\n",
       "      <td>-1034.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2681</th>\n",
       "      <td>hess</td>\n",
       "      <td>4241</td>\n",
       "      <td>-2750.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2682</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>4241</td>\n",
       "      <td>-4825.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2683</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>4953</td>\n",
       "      <td>-6682.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2684 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           user debit_account         soll\n",
       "0          hess          1600  53548310.57\n",
       "1          hess          1400  41013226.98\n",
       "2          hess          1210  28211447.62\n",
       "3          hess          1200  28120290.96\n",
       "4          hess          3425  22302711.15\n",
       "...         ...           ...          ...\n",
       "2679  lohnsdorf         79999         0.00\n",
       "2680  lohnsdorf          4281     -1034.58\n",
       "2681       hess          4241     -2750.20\n",
       "2682  lohnsdorf          4241     -4825.25\n",
       "2683  lohnsdorf          4953     -6682.32\n",
       "\n",
       "[2684 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build account classes for the GL, since the account numbers are not summarized in subledgers \n",
    "\n",
    "\n",
    "# group all transactions by account class and user id\n",
    "df_acc_class_userid_debit = df_gl.groupby([\"user\", \"debit_account\"])\n",
    "\n",
    "# sum up the debit amounts for each account class and user id\n",
    "df_acc_class_userid_debit = df_acc_class_userid_debit[\"debit_amount\"].sum().reset_index()\n",
    "df_acc_class_userid_debit = df_acc_class_userid_debit.rename(columns={\"debit_amount\": \"soll\"})\n",
    "\n",
    "\n",
    "df_acc_class_userid_debit = df_acc_class_userid_debit.sort_values(by=[\"soll\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "df_acc_class_userid_debit.to_excel(\"./results/JB2/df_acc_class_userid_debit.xlsx\", sheet_name='Account class user ID debit' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "\n",
    "df_acc_class_userid_debit"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c50300d9",
   "metadata": {},
   "source": [
    "### Credit amounts per preparer ID and account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c637b0c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>user</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>haben</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hess</td>\n",
       "      <td>1600</td>\n",
       "      <td>52334067.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hess</td>\n",
       "      <td>8125</td>\n",
       "      <td>44107496.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hess</td>\n",
       "      <td>1400</td>\n",
       "      <td>41287943.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hess</td>\n",
       "      <td>1210</td>\n",
       "      <td>28259683.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hess</td>\n",
       "      <td>1200</td>\n",
       "      <td>26419730.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>hess</td>\n",
       "      <td>1220</td>\n",
       "      <td>15975654.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>1600</td>\n",
       "      <td>14193663.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>8125</td>\n",
       "      <td>14030008.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>1400</td>\n",
       "      <td>12125962.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>1200</td>\n",
       "      <td>9031309.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>hess</td>\n",
       "      <td>8120</td>\n",
       "      <td>8604912.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>hess</td>\n",
       "      <td>1476</td>\n",
       "      <td>7460902.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>hess</td>\n",
       "      <td>29998</td>\n",
       "      <td>7158611.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>hess</td>\n",
       "      <td>2502</td>\n",
       "      <td>6862362.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>hess</td>\n",
       "      <td>672</td>\n",
       "      <td>6562499.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>1210</td>\n",
       "      <td>6199225.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>hess</td>\n",
       "      <td>1360</td>\n",
       "      <td>5768271.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>hess</td>\n",
       "      <td>1471</td>\n",
       "      <td>5726242.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>hess</td>\n",
       "      <td>20320</td>\n",
       "      <td>5726242.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>hess</td>\n",
       "      <td>1763</td>\n",
       "      <td>5683367.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         user credit_account        haben\n",
       "0        hess           1600  52334067.28\n",
       "1        hess           8125  44107496.44\n",
       "2        hess           1400  41287943.06\n",
       "3        hess           1210  28259683.40\n",
       "4        hess           1200  26419730.28\n",
       "5        hess           1220  15975654.28\n",
       "6   lohnsdorf           1600  14193663.66\n",
       "7   lohnsdorf           8125  14030008.22\n",
       "8   lohnsdorf           1400  12125962.04\n",
       "9   lohnsdorf           1200   9031309.44\n",
       "10       hess           8120   8604912.89\n",
       "11       hess           1476   7460902.99\n",
       "12       hess          29998   7158611.07\n",
       "13       hess           2502   6862362.88\n",
       "14       hess            672   6562499.50\n",
       "15  lohnsdorf           1210   6199225.48\n",
       "16       hess           1360   5768271.71\n",
       "17       hess           1471   5726242.01\n",
       "18       hess          20320   5726242.01\n",
       "19       hess           1763   5683367.09"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group all transactions by account class and user id\n",
    "df_acc_class_userid_credit = df_gl.groupby([\"user\", \"credit_account\"])\n",
    "\n",
    "# sum up the credit amounts for each account class and user id\n",
    "df_acc_class_userid_credit = df_acc_class_userid_credit[\"credit_amount\"].sum().reset_index()\n",
    "df_acc_class_userid_credit = df_acc_class_userid_credit.rename(columns={\"credit_amount\": \"haben\"})\n",
    "\n",
    "\n",
    "df_acc_class_userid_credit = df_acc_class_userid_credit.sort_values(by=[\"haben\"], ascending=False).reset_index(drop=True)\n",
    "\n",
    "df_acc_class_userid_credit.to_excel(\"./results/JB2/df_acc_class_userid_credit.xlsx\", sheet_name='Account class user ID credit' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "\n",
    "df_acc_class_userid_credit.head(20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6f828a04",
   "metadata": {},
   "source": [
    "## JB3: Transactions without the segregation of duties (SOD)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6ebfeff",
   "metadata": {},
   "source": [
    "This criterion was not performed as %application% does not have a provision for a second authorizer. Thus, the segregation of duties control is not applicable for this instance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f7212e5a",
   "metadata": {},
   "source": [
    "*Not applicable for Doneck Euroflex S.A.*"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e6089947",
   "metadata": {},
   "source": [
    "## JB4: Transactions above a certain threshold (TE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd98a841",
   "metadata": {},
   "source": [
    "This report has results of all journal entries greater or equal to a predefined threshold of %amount% euros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc50f3ac",
   "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>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>505804</td>\n",
       "      <td>1210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1210</td>\n",
       "      <td>NaN</td>\n",
       "      <td>hess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>505804</td>\n",
       "      <td>NaN</td>\n",
       "      <td>677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>677</td>\n",
       "      <td>hess</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>531722</td>\n",
       "      <td>3980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434869.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lohnsdorf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>531722</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4434869.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3960</td>\n",
       "      <td>lohnsdorf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr debit_account credit_account  debit_amount  credit_amount  \\\n",
       "0        505804          1210            NaN    1500000.00            NaN   \n",
       "1        505804           NaN            677           NaN     1500000.00   \n",
       "2        531722          3980            NaN    4434869.22            NaN   \n",
       "3        531722           NaN           3960           NaN     4434869.22   \n",
       "\n",
       "Name debit_account credit_account       user  \n",
       "0             1210            NaN       hess  \n",
       "1              NaN            677       hess  \n",
       "2             3980            NaN  lohnsdorf  \n",
       "3              NaN           3960  lohnsdorf  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# materiality threshold for the account classes\n",
    "materiality_threshold = 1_400_000\n",
    "\n",
    "# get all transactions above the materiality threshold\n",
    "df_trans_greater_te = df_gl.filter([\"booking_nr\", \"debit_account\", \"credit_account\", \"debit_amount\", \"credit_amount\", \"debit_account\", \"credit_account\", \"user\"]).loc[(df_gl[\"debit_amount\"] > materiality_threshold) | (df_gl[\"credit_amount\"] > materiality_threshold)].reset_index(drop=True)\n",
    "\n",
    "if df_trans_greater_te.empty == False:\n",
    "    df_trans_greater_te.to_excel('./results/JB4/transactions_greater_te.xlsx', sheet_name='Trans greater TE' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB4/no_transactions_greater_te.txt', 'w') as f:\n",
    "        f.write('No transactions greater than the materiality threshold')\n",
    "\n",
    "\n",
    "df_trans_greater_te"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8e9be443",
   "metadata": {},
   "source": [
    "## JB5: Journal entries with unusual journal entry descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c76300eb",
   "metadata": {},
   "source": [
    "This report contains the entries with unusual description such as; \"REVERSAL\", \"CORR\", \"ANNUL\", \"CANCEL\", \"DEMAND\", \"SUIVANT\", \"ERROR\", \"FRAUD\", \"INVERSION\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "54aa5b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries with unusual descriptions: 102\n"
     ]
    }
   ],
   "source": [
    "# create a list with unusual keywords for JE descriptions\n",
    "lib = [\"manual\", \"corr\", \"reversal\", \"error\", \"inversion\", \"gift\", \"charity\",\"fehler\", \"korrektur\", \"umkehrung\", \"schenkung\", \"spende\", \"storno\", \"stornierung\"]\n",
    "\n",
    "# check if the JE description contains an unusual keyword and calculate a correlation score for the likeliness \n",
    "df_unusual_trans_desc = df_gl.loc[df_gl[\"description\"].str.contains('|'.join(lib), case=False, na=False)]\n",
    "\n",
    "if df_unusual_trans_desc.empty == False:\n",
    "    df_unusual_trans_desc.to_excel('./results/JB5/unusual_trans_desc.xlsx', sheet_name='unusual desc' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB5/no_unusual_transactions_found.txt', 'w') as f:\n",
    "        f.write('No unusual transactions found')\n",
    "\n",
    "print(f\"Number of entries with unusual descriptions: {len(df_unusual_trans_desc)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8f15cc64",
   "metadata": {},
   "source": [
    "## JB6: Journal entries outside the normal business cycle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbf9576",
   "metadata": {},
   "source": [
    "This report has a list of all manual journal entries that were posted on weekends and on public holidays, for the period under review i.e. January 01, 2021 to December 31, 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9fdaec17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries on a weekend or holiday: 25074\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Name  booking_nr  line_nr document_date       document_nr debit_account  \\\n",
       " 0         483476        1    2021-01-01    R02289401 2022          4360   \n",
       " 1         483476        2    2021-01-01    R02289401 2022           NaN   \n",
       " 7         483993        2    2021-06-06  3022100219105-02           NaN   \n",
       " 8         483993        3    2021-06-06  3022100219105-02           NaN   \n",
       " 14        483453        1    2021-09-23    JSH210001-1209          3400   \n",
       " ...          ...      ...           ...               ...           ...   \n",
       " 87739     511655        4    2022-09-13        13.09.2022           NaN   \n",
       " 87745     511658        1    2022-09-13        13.09.2022         73102   \n",
       " 87746     511658        2    2022-09-13        13.09.2022          1600   \n",
       " 87752     511660        2    2022-09-13        13.09.2022          1600   \n",
       " 87753     511660        3    2022-09-13        13.09.2022           NaN   \n",
       " \n",
       " Name   debit_amount credit_account  credit_amount               description  \\\n",
       " 0             51.86            NaN            NaN        lalux - Leudelange   \n",
       " 1               NaN          72072          51.86        lalux - Leudelange   \n",
       " 7               NaN          71781        1533.05       BG RCI - Heidelberg   \n",
       " 8               NaN           1600        1533.05       BG RCI - Heidelberg   \n",
       " 14         39986.22            NaN            NaN      Sanhu - Nanjing (CN)   \n",
       " ...             ...            ...            ...                       ...   \n",
       " 87739           NaN           3725           0.51               TWH - TRIER   \n",
       " 87745         47.61            NaN            NaN      PANORD-MENSDORF (LU)   \n",
       " 87746         47.61            NaN            NaN      PANORD-MENSDORF (LU)   \n",
       " 87752      10063.25            NaN            NaN  V. ALEXANDER TRANSPORT-B   \n",
       " 87753           NaN           1200       10063.25  V. ALEXANDER TRANSPORT-B   \n",
       " \n",
       " Name  currency  user booking_date document_source  \n",
       " 0          EUR  hess   01/01/2022    stapelbuchen  \n",
       " 1          EUR  hess   02/01/2022    stapelbuchen  \n",
       " 7          EUR  hess   08/01/2022    stapelbuchen  \n",
       " 8          EUR  hess   09/01/2022    stapelbuchen  \n",
       " 14         USD  hess   15/01/2022    stapelbuchen  \n",
       " ...        ...   ...          ...             ...  \n",
       " 87739      EUR  hess   23/03/2262            fibu  \n",
       " 87745      EUR  hess   29/03/2262            fibu  \n",
       " 87746      EUR  hess   30/03/2262            fibu  \n",
       " 87752      EUR  hess   05/04/2262            fibu  \n",
       " 87753      EUR  hess   06/04/2262            fibu  \n",
       " \n",
       " [25074 rows x 13 columns],\n",
       " Name booking_nr  line_nr document_date     document_nr debit_account  \\\n",
       " 0        483476        1    2021-01-01  R02289401 2022          4360   \n",
       " 104      484762        1    2021-01-12        21125146          4281   \n",
       " 107      484762        4    2021-01-12        21125146           NaN   \n",
       " 120      489497        3    2021-02-12       OC 21234P           NaN   \n",
       " 128      485872        1    2021-09-12      9210340508          3400   \n",
       " 145      483247        3    2021-12-16      5300056563           NaN   \n",
       " 156      484679        1    2021-12-16             608           403   \n",
       " 173      487187        3    2021-12-16          101473           NaN   \n",
       " 226      483216        1    2021-12-23      9123391774          1563   \n",
       " 304      489699        3    2021-12-30          200992           NaN   \n",
       " 358      489504        3    2022-01-01  aufl. deb/kred           NaN   \n",
       " 359      494533        1    2022-01-01  aufl. deb/kred         79999   \n",
       " \n",
       " Name  debit_amount credit_account  credit_amount  \\\n",
       " 0            51.86            NaN            NaN   \n",
       " 104         558.40            NaN            NaN   \n",
       " 107            NaN           1600         653.33   \n",
       " 120            NaN           1600        9465.60   \n",
       " 128       28200.00            NaN            NaN   \n",
       " 145            NaN           1600        5290.02   \n",
       " 156        5693.20            NaN            NaN   \n",
       " 173            NaN           1600       75400.00   \n",
       " 226           5.61            NaN            NaN   \n",
       " 304            NaN           1600        5656.70   \n",
       " 358            NaN           1401       32950.65   \n",
       " 359         572.65            NaN            NaN   \n",
       " \n",
       " Name                              description currency  user booking_date  \\\n",
       " 0                          lalux - Leudelange      EUR  hess   01/01/2022   \n",
       " 104                          G4S - Luxembourg      EUR  hess   15/04/2022   \n",
       " 107                          G4S - Luxembourg      EUR  hess   18/04/2022   \n",
       " 120                       Nitro - Vienna (AT)      EUR  hess   01/05/2022   \n",
       " 128                Shiva Performance - Mumbai      EUR  hess   09/05/2022   \n",
       " 145                 Schenker - Leudelange (L)      EUR  hess   26/05/2022   \n",
       " 156                    Dentz-Stabile - Urbach      EUR  hess   06/06/2022   \n",
       " 173               Jaysynth Europe - Stockport      EUR  hess   23/06/2022   \n",
       " 226             Rubix - Trier (Kistenpfennig)      EUR  hess   15/08/2022   \n",
       " 304                   Brenntag - Deerlijk (B)      EUR  hess   01/11/2022   \n",
       " 358             aufl. debitorische Kreditoren      EUR  hess   25/12/2022   \n",
       " 359   aufl. debitorische Kreditoren, Nachtrag      EUR  hess   26/12/2022   \n",
       " \n",
       " Name document_source  \n",
       " 0       stapelbuchen  \n",
       " 104     stapelbuchen  \n",
       " 107     stapelbuchen  \n",
       " 120     stapelbuchen  \n",
       " 128     stapelbuchen  \n",
       " 145     stapelbuchen  \n",
       " 156     stapelbuchen  \n",
       " 173     stapelbuchen  \n",
       " 226     stapelbuchen  \n",
       " 304     stapelbuchen  \n",
       " 358     stapelbuchen  \n",
       " 359     stapelbuchen  ,\n",
       " ['2022-01-01',\n",
       "  '2022-04-15',\n",
       "  '2022-04-18',\n",
       "  '2022-05-01',\n",
       "  '2022-05-09',\n",
       "  '2022-05-26',\n",
       "  '2022-06-06',\n",
       "  '2022-06-23',\n",
       "  '2022-08-15',\n",
       "  '2022-11-01',\n",
       "  '2022-12-25',\n",
       "  '2022-12-26'])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fetch list of public holidays and bank holidays for the provided country and year\n",
    "holidays = get_holidays(country='LU', year=2022)\n",
    "\n",
    "holiday_list = []\n",
    "\n",
    "if holidays is not None:\n",
    "    # create a list with all holidays in Luxembourg for the year 2022\n",
    "    for holiday in holidays:\n",
    "        holiday_list.append(holiday[\"date\"])\n",
    "\n",
    "\n",
    "# check if the JE date is a holiday in Luxembourg\n",
    "df_gl_holiday = df_gl.loc[pd.to_datetime(df_gl[\"booking_date\"], format='%d/%m/%Y', errors='coerce').dt.strftime('%Y-%m-%d').isin(holiday_list)]\n",
    "\n",
    "# check if the JE date is a weekend\n",
    "df_gl_weekend = df_gl.loc[pd.to_datetime(df_gl[\"booking_date\"], format='%d/%m/%Y', errors='coerce').dt.dayofweek > 4]\n",
    "\n",
    "# check if the JE date is a weekend or a holiday\n",
    "df_gl_weekend_holiday = df_gl.loc[(pd.to_datetime(df_gl[\"booking_date\"], format='%d/%m/%Y', errors='coerce').dt.dayofweek > 4) | (df_gl[\"booking_date\"].isin(holiday_list))]\n",
    "df_gl_weekend_holiday = df_gl_weekend_holiday.reset_index(drop=True)\n",
    "\n",
    "if df_gl_weekend_holiday.empty == False:\n",
    "    df_gl_weekend_holiday.to_excel('./results/JB6/weekend_holiday.xlsx', sheet_name='weekend holiday' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB6/no_weekend_holiday.txt', 'w') as f:\n",
    "        f.write('No entries on a weekend or holiday')\n",
    "\n",
    "print(f\"Number of entries on a weekend or holiday: {len(df_gl_weekend_holiday)}\")\n",
    "\n",
    "df_gl_weekend, df_gl_holiday, holiday_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8343f4da",
   "metadata": {},
   "source": [
    "## JB7: Transactions with the same amount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3876985a",
   "metadata": {},
   "source": [
    "Report has journal entries that have the same amounts posted, or they are similar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a7b54e0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter all transactions with the same amount \n",
    "df_same_amount = df_gl.loc[(df_gl[\"debit_amount\"] == df_gl[\"credit_amount\"]) & (df_gl[\"debit_amount\"] != 0) & (df_gl[\"credit_amount\"] != 0)]\n",
    "df_same_amount = df_same_amount.reset_index(drop=True)\n",
    "\n",
    "# filter all transactions with the same amount and the same account number\n",
    "df_same_amount_acc = df_gl.loc[(df_gl[\"debit_amount\"] == df_gl[\"credit_amount\"]) & (df_gl[\"debit_amount\"] != 0) & (df_gl[\"credit_amount\"] != 0) & (df_gl[\"debit_account\"].astype(str) == df_gl[\"credit_account\"].astype(str))]\n",
    "df_same_amount_acc = df_same_amount_acc.reset_index(drop=True)\n",
    "\n",
    "if df_same_amount.empty == False:\n",
    "    df_same_amount.to_excel('./results/JB7/same_amounts.xlsx', sheet_name='same amounts' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB7/no_same_amounts.txt', 'w') as f:\n",
    "        f.write('No transactions with the same amount')\n",
    "        \n",
    "if df_same_amount_acc.empty == False:\n",
    "    df_same_amount_acc.to_excel('./results/JB7/same_amounts_acc.xlsx', sheet_name='same amounts acc' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else:\n",
    "    with open('./results/JB7/no_same_amounts_acc.txt', 'w') as f:\n",
    "        f.write('No transactions with the same amount and account number')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "75eba89a",
   "metadata": {},
   "source": [
    "## JB8: Duplicate entries, documents, or payments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77e72cd8",
   "metadata": {},
   "source": [
    "The report shows all duplicate manual journals posted for the period under review."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "df951e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batch booking entries: 85704\n",
      "Number of batch booking entries with the same amount: 0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batch booking entries with the same description: 67363\n"
     ]
    }
   ],
   "source": [
    "# create dataframe only with batch_booking journal entries\n",
    "df_batch_booking_entries = df_gl.loc[df_gl[\"document_source\"].str.lower() == \"stapelbuchen\"]\n",
    "df_batch_booking_entries = df_batch_booking_entries.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of batch booking entries: {len(df_batch_booking_entries)}\")\n",
    "\n",
    "# filter all batch_booking transactions with the same amount\n",
    "df_batch_booking_same_amount = df_batch_booking_entries.loc[(df_batch_booking_entries[\"debit_amount\"] == df_batch_booking_entries[\"credit_amount\"])]\n",
    "df_batch_booking_same_amount = df_batch_booking_same_amount.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of batch booking entries with the same amount: {len(df_batch_booking_same_amount)}\")\n",
    "\n",
    "if df_batch_booking_same_amount.empty == False:\n",
    "    df_batch_booking_same_amount.to_excel('./results/JB8/batch_booking_same_amounts.xlsx', index=False)\n",
    "else :\n",
    "    with open('./results/JB8/no_batch_booking_entries_with_same_amount.txt', 'w') as f:\n",
    "        f.write('No batch_booking entries with the same amount were found.')\n",
    "\n",
    "# filter all batch_booking transactions with the same description\n",
    "df_batch_booking_same_desc = df_batch_booking_entries.loc[(df_batch_booking_entries[\"description\"].str.lower() == df_batch_booking_entries[\"description\"].str.lower().shift(-1)) & (df_batch_booking_entries[\"description\"].str.lower() != \"nan\")]\n",
    "df_batch_booking_same_desc = df_batch_booking_same_desc.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of batch booking entries with the same description: {len(df_batch_booking_same_desc)}\")\n",
    "\n",
    "if df_batch_booking_same_desc.empty == False:\n",
    "    df_batch_booking_same_desc.to_excel('./results/JB8/batch_booking_same_desc.xlsx', sheet_name='batch booking same desc' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else :\n",
    "    with open('./results/JB8/no_batch_booking_entries_with_same_desc.txt', 'w') as f:\n",
    "        f.write('No batch_booking entries with the same description were found.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3ee0f795",
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by account number and sum the amounts\n",
    "df_batch_booking_entries_debit = df_batch_booking_entries.groupby([\"debit_account\"]).agg({\"debit_amount\": \"sum\", \"credit_amount\": \"sum\"}).reset_index()\n",
    "df_batch_booking_entries_credit = df_batch_booking_entries.groupby([\"credit_account\"]).agg({\"debit_amount\": \"sum\", \"credit_amount\": \"sum\"}).reset_index()\n",
    "\n",
    "df_batch_booking_entries_debit = df_batch_booking_entries_debit.rename(columns={\"debit_account\": \"account_number\", \"debit_amount\": \"debit_amount\", \"credit_amount\": \"credit_amount\"})\n",
    "df_batch_booking_entries_credit = df_batch_booking_entries_credit.rename(columns={\"credit_account\": \"account_number\", \"debit_amount\": \"debit_amount\", \"credit_amount\": \"credit_amount\"})\n",
    "\n",
    "if df_batch_booking_entries_debit.empty == False:\n",
    "    df_batch_booking_entries_debit.to_excel('./results/JB8/batch_booking_entries_debit.xlsx', sheet_name='batch booking entries debit' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else :  \n",
    "    with open('./results/JB8/no_batch_booking_entries_debit.txt', 'w') as f:\n",
    "        f.write('No batch_booking entries with debit were found.')\n",
    "\n",
    "if df_batch_booking_entries_credit.empty == False:\n",
    "    df_batch_booking_entries_credit.to_excel('./results/JB8/batch_booking_entries_credit.xlsx', sheet_name='batch booking entries credit' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else :\n",
    "    with open('./results/JB8/no_batch_booking_entries_credit.txt', 'w') as f:\n",
    "        f.write('No batch_booking entries with credit were found.')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "199b4942",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of manual entries: 38003\n",
      "Number of manual entries with the same amount: 0\n",
      "Number of manual entries with the same description: 29688\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>line_nr</th>\n",
       "      <th>document_date</th>\n",
       "      <th>document_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>currency</th>\n",
       "      <th>user</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>document_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>510377</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208354</td>\n",
       "      <td>10122</td>\n",
       "      <td>3335.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brohl - Mayen (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>30/06/2246</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>510377</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208354</td>\n",
       "      <td>1400</td>\n",
       "      <td>3335.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brohl - Mayen (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>01/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>510378</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208353</td>\n",
       "      <td>11810</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bong BDT - Torgau (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>03/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>510378</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208353</td>\n",
       "      <td>1400</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bong BDT - Torgau (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>04/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510379</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208352</td>\n",
       "      <td>12215</td>\n",
       "      <td>5797.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PEKU Folien GmbH - Neumarkt (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>06/07/2246</td>\n",
       "      <td>fibu</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29683</th>\n",
       "      <td>521826</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-20</td>\n",
       "      <td>244</td>\n",
       "      <td>1210</td>\n",
       "      <td>4215.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Enovos - Luxembourg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>09/07/2350</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29684</th>\n",
       "      <td>521826</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-12-20</td>\n",
       "      <td>244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70640</td>\n",
       "      <td>4215.50</td>\n",
       "      <td>Enovos - Luxembourg</td>\n",
       "      <td>EUR</td>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>10/07/2350</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29685</th>\n",
       "      <td>521827</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-20</td>\n",
       "      <td>244</td>\n",
       "      <td>1210</td>\n",
       "      <td>5329.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Marco Emballages - Saint-Python (FR)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>12/07/2350</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29686</th>\n",
       "      <td>521827</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-12-20</td>\n",
       "      <td>244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21259</td>\n",
       "      <td>5329.73</td>\n",
       "      <td>Marco Emballages - Saint-Python (FR)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>13/07/2350</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29687</th>\n",
       "      <td>521828</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-12-20</td>\n",
       "      <td>244</td>\n",
       "      <td>1210</td>\n",
       "      <td>7145.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FLO Europe Sas - Ruitz (FR)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>lohnsdorf</td>\n",
       "      <td>15/07/2350</td>\n",
       "      <td>fibu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>29688 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Name  booking_nr  line_nr document_date document_nr debit_account  \\\n",
       "0         510377        1    2022-08-25     2208354         10122   \n",
       "1         510377        2    2022-08-25     2208354          1400   \n",
       "2         510378        1    2022-08-25     2208353         11810   \n",
       "3         510378        2    2022-08-25     2208353          1400   \n",
       "4         510379        1    2022-08-25     2208352         12215   \n",
       "...          ...      ...           ...         ...           ...   \n",
       "29683     521826        1    2022-12-20         244          1210   \n",
       "29684     521826        2    2022-12-20         244           NaN   \n",
       "29685     521827        1    2022-12-20         244          1210   \n",
       "29686     521827        2    2022-12-20         244           NaN   \n",
       "29687     521828        1    2022-12-20         244          1210   \n",
       "\n",
       "Name   debit_amount credit_account  credit_amount  \\\n",
       "0           3335.50            NaN            NaN   \n",
       "1           3335.50            NaN            NaN   \n",
       "2           8137.60            NaN            NaN   \n",
       "3           8137.60            NaN            NaN   \n",
       "4           5797.65            NaN            NaN   \n",
       "...             ...            ...            ...   \n",
       "29683       4215.50            NaN            NaN   \n",
       "29684           NaN          70640        4215.50   \n",
       "29685       5329.73            NaN            NaN   \n",
       "29686           NaN          21259        5329.73   \n",
       "29687       7145.15            NaN            NaN   \n",
       "\n",
       "Name                            description currency       user booking_date  \\\n",
       "0                        Brohl - Mayen (DE)      EUR       hess   30/06/2246   \n",
       "1                        Brohl - Mayen (DE)      EUR       hess   01/07/2246   \n",
       "2                    Bong BDT - Torgau (DE)      EUR       hess   03/07/2246   \n",
       "3                    Bong BDT - Torgau (DE)      EUR       hess   04/07/2246   \n",
       "4          PEKU Folien GmbH - Neumarkt (DE)      EUR       hess   06/07/2246   \n",
       "...                                     ...      ...        ...          ...   \n",
       "29683                   Enovos - Luxembourg      EUR  lohnsdorf   09/07/2350   \n",
       "29684                   Enovos - Luxembourg      EUR  lohnsdorf   10/07/2350   \n",
       "29685  Marco Emballages - Saint-Python (FR)      EUR  lohnsdorf   12/07/2350   \n",
       "29686  Marco Emballages - Saint-Python (FR)      EUR  lohnsdorf   13/07/2350   \n",
       "29687           FLO Europe Sas - Ruitz (FR)      EUR  lohnsdorf   15/07/2350   \n",
       "\n",
       "Name  document_source  \n",
       "0                fibu  \n",
       "1                fibu  \n",
       "2                fibu  \n",
       "3                fibu  \n",
       "4                fibu  \n",
       "...               ...  \n",
       "29683            fibu  \n",
       "29684            fibu  \n",
       "29685            fibu  \n",
       "29686            fibu  \n",
       "29687            fibu  \n",
       "\n",
       "[29688 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create dataframe only with manual journal entries\n",
    "df_manual_booking_entries = df_gl.loc[df_gl[\"document_source\"].str.lower() != \"stapelbuchen\"]\n",
    "df_manual_booking_entries = df_manual_booking_entries.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of manual entries: {len(df_manual_booking_entries)}\")\n",
    "\n",
    "# filter all manual_bookingly created transactions with the same amount\n",
    "df_manual_booking_same_amount = df_manual_booking_entries.loc[(df_manual_booking_entries[\"debit_amount\"] == df_manual_booking_entries[\"credit_amount\"])]\n",
    "df_manual_booking_same_amount = df_manual_booking_same_amount.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of manual entries with the same amount: {len(df_manual_booking_same_amount)}\")\n",
    "\n",
    "if df_manual_booking_same_amount.empty == False:\n",
    "    df_manual_booking_same_amount.to_excel('./results/JB8/manual_booking_same_amounts.xlsx', sheet_name='manual booking same amounts' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else :\n",
    "    with open('./results/JB8/no_manual_booking_entries_with_same_amount.txt', 'w') as f:\n",
    "        f.write('No manual entries with the same amount were found.')\n",
    "\n",
    "# filter all manual_bookingly created transactions with the same description\n",
    "df_manual_booking_same_desc = df_manual_booking_entries.loc[(df_manual_booking_entries[\"description\"].str.lower() == df_manual_booking_entries[\"description\"].str.lower().shift(-1)) & (df_manual_booking_entries[\"description\"].str.lower() != \"nan\")]\n",
    "df_manual_booking_same_desc = df_manual_booking_same_desc.reset_index(drop=True)\n",
    "\n",
    "print(f\"Number of manual entries with the same description: {len(df_manual_booking_same_desc)}\")\n",
    "\n",
    "if df_manual_booking_same_desc.empty == False:\n",
    "    df_manual_booking_same_desc.to_excel('./results/JB8/manual_booking_same_desc.xlsx', sheet_name='manual booking same desc' ,index=False, header=True, float_format=\"%.2f\", engine='xlsxwriter')\n",
    "else :\n",
    "    with open('./results/JB8/no_manual_booking_entries_with_same_desc.txt', 'w') as f:\n",
    "        f.write('No manual entries with the same description were found.')\n",
    "\n",
    "df_manual_booking_same_desc"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d0822254",
   "metadata": {},
   "source": [
    "## JB9: Unusual pair of account numbers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5892c914",
   "metadata": {},
   "source": [
    "The report highlights journals posted to unusual accounts as specified by the Financial Audit team."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e690fe7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\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>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>line_nr</th>\n",
       "      <th>document_date</th>\n",
       "      <th>document_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>currency</th>\n",
       "      <th>user</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>document_source</th>\n",
       "    </tr>\n",
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       "      <th>84</th>\n",
       "      <td>484078</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>220385</td>\n",
       "      <td>4988</td>\n",
       "      <td>298.62</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Ambs - Lahr</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>26/03/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>484078</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>220385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70003</td>\n",
       "      <td>298.62</td>\n",
       "      <td>Ambs - Lahr</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>27/03/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>484078</td>\n",
       "      <td>3</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>220385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1600</td>\n",
       "      <td>298.62</td>\n",
       "      <td>Ambs - Lahr</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>28/03/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>484079</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>220385</td>\n",
       "      <td>1563</td>\n",
       "      <td>50.77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ambs - Lahr</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>29/03/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>484079</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-11-30</td>\n",
       "      <td>220385</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1763</td>\n",
       "      <td>50.77</td>\n",
       "      <td>Ambs - Lahr</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>30/03/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>483249</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>211270901</td>\n",
       "      <td>1563</td>\n",
       "      <td>14.06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Geisen - Trier</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>22/04/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>483249</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>211270901</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1763</td>\n",
       "      <td>14.06</td>\n",
       "      <td>Geisen - Trier</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>23/04/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>489498</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>OC 21234P</td>\n",
       "      <td>1563</td>\n",
       "      <td>1609.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nitro - Vienna (AT)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>02/05/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>489498</td>\n",
       "      <td>2</td>\n",
       "      <td>2021-02-12</td>\n",
       "      <td>OC 21234P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1763</td>\n",
       "      <td>1609.15</td>\n",
       "      <td>Nitro - Vienna (AT)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>03/05/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>483258</td>\n",
       "      <td>1</td>\n",
       "      <td>2021-12-16</td>\n",
       "      <td>198160</td>\n",
       "      <td>1563</td>\n",
       "      <td>732.14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brenntag - Deerlijk (B)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>30/05/2022</td>\n",
       "      <td>stapelbuchen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr  line_nr document_date document_nr debit_account  \\\n",
       "84       484078        1    2021-11-30      220385          4988   \n",
       "85       484078        2    2021-11-30      220385           NaN   \n",
       "86       484078        3    2021-11-30      220385           NaN   \n",
       "87       484079        1    2021-11-30      220385          1563   \n",
       "88       484079        2    2021-11-30      220385           NaN   \n",
       "111      483249        1    2021-02-12   211270901          1563   \n",
       "112      483249        2    2021-02-12   211270901           NaN   \n",
       "121      489498        1    2021-02-12   OC 21234P          1563   \n",
       "122      489498        2    2021-02-12   OC 21234P           NaN   \n",
       "149      483258        1    2021-12-16      198160          1563   \n",
       "\n",
       "Name  debit_amount credit_account  credit_amount              description  \\\n",
       "84          298.62            NaN            NaN              Ambs - Lahr   \n",
       "85             NaN          70003         298.62              Ambs - Lahr   \n",
       "86             NaN           1600         298.62              Ambs - Lahr   \n",
       "87           50.77            NaN            NaN              Ambs - Lahr   \n",
       "88             NaN           1763          50.77              Ambs - Lahr   \n",
       "111          14.06            NaN            NaN           Geisen - Trier   \n",
       "112            NaN           1763          14.06           Geisen - Trier   \n",
       "121        1609.15            NaN            NaN      Nitro - Vienna (AT)   \n",
       "122            NaN           1763        1609.15      Nitro - Vienna (AT)   \n",
       "149         732.14            NaN            NaN  Brenntag - Deerlijk (B)   \n",
       "\n",
       "Name currency  user booking_date document_source  \n",
       "84        EUR  hess   26/03/2022    stapelbuchen  \n",
       "85        EUR  hess   27/03/2022    stapelbuchen  \n",
       "86        EUR  hess   28/03/2022    stapelbuchen  \n",
       "87        EUR  hess   29/03/2022    stapelbuchen  \n",
       "88        EUR  hess   30/03/2022    stapelbuchen  \n",
       "111       EUR  hess   22/04/2022    stapelbuchen  \n",
       "112       EUR  hess   23/04/2022    stapelbuchen  \n",
       "121       EUR  hess   02/05/2022    stapelbuchen  \n",
       "122       EUR  hess   03/05/2022    stapelbuchen  \n",
       "149       EUR  hess   30/05/2022    stapelbuchen  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define unusual account numbers \n",
    "dict_acc_pairs = {\n",
    "    '1': {\n",
    "        'credit': 1763,\n",
    "        'debit': 1563\n",
    "    },\n",
    "    '2': {\n",
    "        'credit': 1600,\n",
    "        'debit': 4988\n",
    "    },\n",
    "    '3': {\n",
    "        'credit': 1600,\n",
    "        'debit': 1600\n",
    "    },\n",
    "}\n",
    "\n",
    "# group rows of transactions together \n",
    "df_grouped_id = df_gl.groupby(['booking_nr'])\n",
    "\n",
    "# filter journal entries with the same amount\n",
    "filtered_transactions = []\n",
    "\n",
    "for _, group in df_grouped_id:\n",
    "    \n",
    "    # create a list of all credit and debit accounts per booking_nr\n",
    "    credit_accounts = group['credit_account'].tolist()\n",
    "    debit_accounts = group['debit_account'].tolist()\n",
    "\n",
    "    # loop over every test in the dictionary\n",
    "    for key, value in dict_acc_pairs.items():\n",
    "        #print((value['credit'] in credit_accounts) and (value['debit'] in debit_accounts))\n",
    "        # check if the credit and debit accounts are in the dict_acc_pairs\n",
    "        if (value['credit'] in credit_accounts) and (value['debit'] in debit_accounts):\n",
    "\n",
    "            # append the booking_nr to the filtered_transactions list\n",
    "            filtered_transactions.append(group['booking_nr'].tolist()[0])\n",
    "\n",
    "# filter the dataframe\n",
    "df_filtered_trans = df_gl[df_gl['booking_nr'].isin(filtered_transactions)]\n",
    "\n",
    "df_filtered_trans.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c75fb507",
   "metadata": {},
   "source": [
    "## JB10: Journal entry records missing while identifying manual journal entries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa879843",
   "metadata": {},
   "source": [
    "Considering that we removed all automated journal entries from the population to be used for the analysis, we perform an analysis of the sequences. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0a9b6c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "manual_booking_entries_count = df_manual_booking_entries[\"description\"].value_counts()\n",
    "\n",
    "# plotting the 50 most common descriptions\n",
    "manual_booking_entries_count[:50].plot(kind='bar', figsize=(20,10), color='blue', rot=90, fontsize=12, grid=True)\n",
    "\n",
    "plt.title(\"50 most common descriptions of manual booking entries\")\n",
    "plt.xlabel(\"Description\")\n",
    "plt.ylabel(\"Count\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "636ea79b",
   "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>Name</th>\n",
       "      <th>booking_nr</th>\n",
       "      <th>line_nr</th>\n",
       "      <th>document_date</th>\n",
       "      <th>document_nr</th>\n",
       "      <th>debit_account</th>\n",
       "      <th>debit_amount</th>\n",
       "      <th>credit_account</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>description</th>\n",
       "      <th>currency</th>\n",
       "      <th>user</th>\n",
       "      <th>booking_date</th>\n",
       "      <th>document_source</th>\n",
       "      <th>is_sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>510376</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8125</td>\n",
       "      <td>9372.70</td>\n",
       "      <td>Model - Berka (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>29/06/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>510377</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208354</td>\n",
       "      <td>10122</td>\n",
       "      <td>3335.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brohl - Mayen (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>30/06/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>510377</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208354</td>\n",
       "      <td>1400</td>\n",
       "      <td>3335.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brohl - Mayen (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>01/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>510377</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8125</td>\n",
       "      <td>3335.50</td>\n",
       "      <td>Brohl - Mayen (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>02/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>510378</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208353</td>\n",
       "      <td>11810</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bong BDT - Torgau (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>03/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>510378</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208353</td>\n",
       "      <td>1400</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bong BDT - Torgau (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>04/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>510378</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8125</td>\n",
       "      <td>8137.60</td>\n",
       "      <td>Bong BDT - Torgau (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>05/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>510379</td>\n",
       "      <td>1</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208352</td>\n",
       "      <td>12215</td>\n",
       "      <td>5797.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PEKU Folien GmbH - Neumarkt (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>06/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>510379</td>\n",
       "      <td>2</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208352</td>\n",
       "      <td>1400</td>\n",
       "      <td>5797.65</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PEKU Folien GmbH - Neumarkt (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>07/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>510379</td>\n",
       "      <td>3</td>\n",
       "      <td>2022-08-25</td>\n",
       "      <td>2208352</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8125</td>\n",
       "      <td>5797.65</td>\n",
       "      <td>PEKU Folien GmbH - Neumarkt (DE)</td>\n",
       "      <td>EUR</td>\n",
       "      <td>hess</td>\n",
       "      <td>08/07/2246</td>\n",
       "      <td>fibu</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name booking_nr  line_nr document_date document_nr debit_account  \\\n",
       "0        510376        3    2022-08-25     2208355           NaN   \n",
       "1        510377        1    2022-08-25     2208354         10122   \n",
       "2        510377        2    2022-08-25     2208354          1400   \n",
       "3        510377        3    2022-08-25     2208354           NaN   \n",
       "4        510378        1    2022-08-25     2208353         11810   \n",
       "5        510378        2    2022-08-25     2208353          1400   \n",
       "6        510378        3    2022-08-25     2208353           NaN   \n",
       "7        510379        1    2022-08-25     2208352         12215   \n",
       "8        510379        2    2022-08-25     2208352          1400   \n",
       "9        510379        3    2022-08-25     2208352           NaN   \n",
       "\n",
       "Name  debit_amount credit_account  credit_amount  \\\n",
       "0              NaN           8125        9372.70   \n",
       "1          3335.50            NaN            NaN   \n",
       "2          3335.50            NaN            NaN   \n",
       "3              NaN           8125        3335.50   \n",
       "4          8137.60            NaN            NaN   \n",
       "5          8137.60            NaN            NaN   \n",
       "6              NaN           8125        8137.60   \n",
       "7          5797.65            NaN            NaN   \n",
       "8          5797.65            NaN            NaN   \n",
       "9              NaN           8125        5797.65   \n",
       "\n",
       "Name                       description currency  user booking_date  \\\n",
       "0                   Model - Berka (DE)      EUR  hess   29/06/2246   \n",
       "1                   Brohl - Mayen (DE)      EUR  hess   30/06/2246   \n",
       "2                   Brohl - Mayen (DE)      EUR  hess   01/07/2246   \n",
       "3                   Brohl - Mayen (DE)      EUR  hess   02/07/2246   \n",
       "4               Bong BDT - Torgau (DE)      EUR  hess   03/07/2246   \n",
       "5               Bong BDT - Torgau (DE)      EUR  hess   04/07/2246   \n",
       "6               Bong BDT - Torgau (DE)      EUR  hess   05/07/2246   \n",
       "7     PEKU Folien GmbH - Neumarkt (DE)      EUR  hess   06/07/2246   \n",
       "8     PEKU Folien GmbH - Neumarkt (DE)      EUR  hess   07/07/2246   \n",
       "9     PEKU Folien GmbH - Neumarkt (DE)      EUR  hess   08/07/2246   \n",
       "\n",
       "Name document_source  is_sequence  \n",
       "0               fibu         True  \n",
       "1               fibu         True  \n",
       "2               fibu         True  \n",
       "3               fibu         True  \n",
       "4               fibu         True  \n",
       "5               fibu         True  \n",
       "6               fibu         True  \n",
       "7               fibu         True  \n",
       "8               fibu         True  \n",
       "9               fibu         True  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# function to use in pandas to search in the transaction description for sequences\n",
    "# of characters that match a regular expression.\n",
    "\n",
    "def search_in_description(description):\n",
    "    \"\"\"\n",
    "    Search in the description for sequences of characters that match a regular expression.\n",
    "    \"\"\"\n",
    "    if re.search(r\"[A-Za-z0-9]+\\s-\\s[A-Za-z0-9]+\\s\\([^)]*\\)\", description, re.IGNORECASE):\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "    \n",
    "# Create a new column \n",
    "df_manual_booking_entries['is_sequence'] = df_manual_booking_entries['description'].apply(search_in_description)\n",
    "\n",
    "# Print the rows where is_sequence is True\n",
    "df_manual_booking_entries[df_manual_booking_entries['is_sequence'] == True].head(10)"
   ]
  }
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