Evaporating Inventory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Evaporating Inventory Incident"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have been contracted for your first investigation. The [COO](https://en.wikipedia.org/wiki/Chief_operating_officer) of a company has heard rumors about irregularities in the inventory. Nothing specific but enough to get her curious. \n",
    "\n",
    "You have requested a copy of the data from the company's SAP system. You focus on the **material documents**. \n",
    "\n",
    "Your objective is to analyze the data to identify and understand the irregularities. **Can you establish sufficient evidence for fraud?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:50:25.650751Z",
     "start_time": "2018-04-26T22:50:25.644938Z"
    }
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-20T18:28:35.627732Z",
     "start_time": "2018-04-20T18:28:35.612855Z"
    }
   },
   "source": [
    "Some initialization to make life easier. **Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:43.311124Z",
     "start_time": "2018-05-11T21:57:43.292145Z"
    }
   },
   "outputs": [],
   "source": [
    "#Allow multiple outputs for each cell\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "#Show simple plots in the notebook\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the following libraries:\n",
    "* [Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics.\n",
    "* [Altair](https://altair-viz.github.io) is a visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:43.331899Z",
     "start_time": "2018-05-11T21:57:43.317945Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import altair as alt\n",
    "from altair import *\n",
    "import numpy as np\n",
    "#Format number without any decimals\n",
    "pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "#Show altair plots in the notebook\n",
    "alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received two tables from the SAP system, which contain the information of the **material movements** in the company:\n",
    "2. The header information of the material documents are stored in the table `MKPF`.\n",
    "1. The line item information of the material documents is stored in the table `MSEG`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.570640Z",
     "start_time": "2018-05-11T21:57:43.338369Z"
    }
   },
   "outputs": [],
   "source": [
    "mkpf_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MKPK_EI.csv')\n",
    "mseg_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MSEG_EI.csv',low_memory=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:41:25.227421Z",
     "start_time": "2018-05-11T04:41:25.221887Z"
    }
   },
   "source": [
    "## Understanding the tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The MKPF table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:41:43.547267Z",
     "start_time": "2018-05-11T04:41:43.535290Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `MKPF`:\n",
    "* `MBLNR` - Contains the material document number.\n",
    "* `USNAM` - Contains the inventory employee who posted the material document."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The MSEG table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T04:42:32.460329Z",
     "start_time": "2018-05-11T04:42:32.445833Z"
    }
   },
   "source": [
    "For our purposes, we use the following columns from `MSEG`:\n",
    "* `MBLNR` - Contains the material document number.\n",
    "* `BWART` - Contains the movement type of the line item. This [link](https://wiki.scn.sap.com/wiki/display/ERPLO/Movement+types) contains information about the movement types.\n",
    "* `MATNR` - Contains the material id of the material moved.\n",
    "* `LGORT` - Contains the storage location.\n",
    "* `DMBTR` - Contains the value of the material movement.\n",
    "* `MENGE` - Contains the volume of the movement in units of the material. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the company"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After a meeting you understand that the company has four locations:\n",
    "* `DL00` - the factory in Dallas, TX\n",
    "* `MI00` - the factory in Miami, FL\n",
    "* `SD00` - the factory in San Diego, CA\n",
    "* `SC00` - the factory in Santa Clara, CA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Furthermore, the company has the following **groups** of raw materials in its storage locations (`RM00`) across the locations in the company code `US00`. You keep a record of the groups in the following variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Marvelous Materials:**\n",
    "* `ADAMANTIUM`\n",
    "* `CARBONITE`\n",
    "* `KRYPTONITE`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.586554Z",
     "start_time": "2018-05-11T21:57:44.580038Z"
    }
   },
   "outputs": [],
   "source": [
    "marvel = ['ADAMANTIUM', 'CARBONITE', 'KRYPTONITE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Boring Metals:**\n",
    "* `GOLD` \n",
    "* `IRON`\n",
    "* `PALLADIUM`\n",
    "* `SILVER`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.612821Z",
     "start_time": "2018-05-11T21:57:44.602269Z"
    }
   },
   "outputs": [],
   "source": [
    "boring = ['GOLD', 'IRON', 'PALLADIUM', 'SILVER']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gems:**\n",
    "* `ANGOLAN AMETHYST`\n",
    "* `AUSTRALIAN AMETHYST`\n",
    "* `BOTSWANA BLACK PE`\n",
    "* `BRITAIN BLACK PEAR`\n",
    "* `CANADIAN CRYSTAL`\n",
    "* `CONGOCRYSTAL`\n",
    "* `DIAMOND`\n",
    "* `ROMANIAN RUBY`\n",
    "* `RUBY`\n",
    "* `RUSSIAN RUBY`\n",
    "* `SA SAPPHIRE`\n",
    "* `SWISS SAPPHIRE`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.636413Z",
     "start_time": "2018-05-11T21:57:44.625416Z"
    }
   },
   "outputs": [],
   "source": [
    "gems = ['ANGOLAN AMETHYST','AUSTRALIAN AMETHYST', 'BOTSWANA BLACK PE',\\\n",
    "        'BRITAIN BLACK PEAR', 'CANADIAN CRYSTAL', 'CONGOCRYSTAL', \\\n",
    "        'DIAMOND', 'ROMANIAN RUBY', 'RUBY', \\\n",
    "        'RUSSIAN RUBY', 'SA SAPPHIRE','SWISS SAPPHIRE']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Essentials:**\n",
    "* `CHRONIUM`\n",
    "* `CONCRETE`\n",
    "* `ICAN HEADMASK`\n",
    "* `MAGICDUST`\n",
    "* `ORANGE`\n",
    "* `PAPER`\n",
    "* `ROSE ESSENTIAL OIL`\n",
    "* `TEST`\n",
    "* `WALLET`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.652767Z",
     "start_time": "2018-05-11T21:57:44.642285Z"
    }
   },
   "outputs": [],
   "source": [
    "essentials = ['CHRONIUM', 'CONCRETE',  'ICAN HEADMASK', \\\n",
    "              'MAGICDUST', 'ORANGE',  'PAPER', \\\n",
    "              'ROSE ESSENTIAL OIL',  'TEST', 'WALLET']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Consumables:**\n",
    "* `AAA LUBE` \n",
    "* `BLUEPAINT`\n",
    "* `BOLT1000`\n",
    "* `HEXNT`\n",
    "* `LIQUID`\n",
    "* `ZTESTHEXNT`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.670536Z",
     "start_time": "2018-05-11T21:57:44.659859Z"
    }
   },
   "outputs": [],
   "source": [
    "consumables = ['AAA LUBE', 'BLUEPAINT', 'BOLT1000', \\\n",
    "               'HEXNT','LIQUID','ZTESTHEXNT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your investigation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce the tables to the relevant columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your objective:** Focus on the columns that are important to your investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.691177Z",
     "start_time": "2018-05-11T21:57:44.676623Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg = mseg_table.loc[:,['MBLNR','BWART','MATNR','WERKS','LGORT','DMBTR','MENGE']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.706041Z",
     "start_time": "2018-05-11T21:57:44.696076Z"
    }
   },
   "outputs": [],
   "source": [
    "mkpf = mkpf_table.loc[:,['MBLNR', 'USNAM']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.736445Z",
     "start_time": "2018-05-11T21:57:44.712638Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg = pd.merge(mseg, mkpf, how='left', on='MBLNR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Include the material group information to the `MATNR` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.782953Z",
     "start_time": "2018-05-11T21:57:44.740940Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg['GROUP'] = 'MARVEL'\n",
    "mseg.loc[mseg['MATNR'].isin(boring), 'GROUP'] = 'BORING'\n",
    "mseg.loc[mseg['MATNR'].isin(gems), 'GROUP'] = 'GEMS'\n",
    "mseg.loc[mseg['MATNR'].isin(essentials), 'GROUP'] = 'ESSENTIALS'\n",
    "mseg.loc[mseg['MATNR'].isin(consumables), 'GROUP'] = 'CONSUMABLES'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Develop an overview of the material movements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your objective:** Identify potential irregularities in the material movement data.\n",
    "\n",
    "Think along the following lines:\n",
    "\n",
    "* What are the differences between the locations?\n",
    "* What are the differences between the movement types?\n",
    "* What are the relationships between the movement types?\n",
    "\n",
    "**Please note**: You cannot have numbers as column headers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.800264Z",
     "start_time": "2018-05-11T21:57:44.787616Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg['BWART'] = mseg['BWART'].map({101: 'GR', 261: 'GI', 333: 'SA', 551: 'SC'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.841859Z",
     "start_time": "2018-05-11T21:57:44.804581Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg_pivot = mseg.pivot_table(index=['WERKS','GROUP'], columns='BWART', values='MENGE', aggfunc='sum').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.862128Z",
     "start_time": "2018-05-11T21:57:44.846991Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg_pivot['utilization'] = mseg_pivot['GI'] / mseg_pivot['GR']\n",
    "mseg_pivot['scrapping'] = mseg_pivot['SC'] / mseg_pivot['GR']\n",
    "mseg_pivot['sampling'] = mseg_pivot['SA'] / mseg_pivot['GR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.904720Z",
     "start_time": "2018-05-11T21:57:44.875140Z"
    }
   },
   "outputs": [],
   "source": [
    "h = mseg_pivot.groupby(['WERKS', 'GROUP'])\\\n",
    "[['utilization', 'scrapping', 'sampling']].mean().reset_index().\\\n",
    "sort_values('sampling', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.927961Z",
     "start_time": "2018-05-11T21:57:44.910435Z"
    }
   },
   "outputs": [],
   "source": [
    "h = mseg_pivot.groupby(['WERKS', 'GROUP'])\\\n",
    "[['utilization', 'scrapping', 'sampling']].mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:44.961764Z",
     "start_time": "2018-05-11T21:57:44.932402Z"
    }
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>BWART</th>\n",
       "      <th>WERKS</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>utilization</th>\n",
       "      <th>scrapping</th>\n",
       "      <th>sampling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DL00</td>\n",
       "      <td>BORING</td>\n",
       "      <td>0.8840</td>\n",
       "      <td>0.0278</td>\n",
       "      <td>0.0159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DL00</td>\n",
       "      <td>CONSUMABLES</td>\n",
       "      <td>0.8820</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DL00</td>\n",
       "      <td>ESSENTIALS</td>\n",
       "      <td>0.8845</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DL00</td>\n",
       "      <td>GEMS</td>\n",
       "      <td>0.9039</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DL00</td>\n",
       "      <td>MARVEL</td>\n",
       "      <td>0.8920</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MI00</td>\n",
       "      <td>BORING</td>\n",
       "      <td>0.9127</td>\n",
       "      <td>0.0102</td>\n",
       "      <td>0.0159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MI00</td>\n",
       "      <td>CONSUMABLES</td>\n",
       "      <td>0.9217</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.0196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MI00</td>\n",
       "      <td>ESSENTIALS</td>\n",
       "      <td>0.9146</td>\n",
       "      <td>0.0286</td>\n",
       "      <td>0.0163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MI00</td>\n",
       "      <td>GEMS</td>\n",
       "      <td>0.9225</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.0130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MI00</td>\n",
       "      <td>MARVEL</td>\n",
       "      <td>0.9118</td>\n",
       "      <td>0.0411</td>\n",
       "      <td>0.0097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SC00</td>\n",
       "      <td>BORING</td>\n",
       "      <td>0.8849</td>\n",
       "      <td>0.0451</td>\n",
       "      <td>0.0250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SC00</td>\n",
       "      <td>CONSUMABLES</td>\n",
       "      <td>0.8959</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>0.0144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SC00</td>\n",
       "      <td>ESSENTIALS</td>\n",
       "      <td>0.8822</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.0157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SC00</td>\n",
       "      <td>GEMS</td>\n",
       "      <td>0.9056</td>\n",
       "      <td>0.0279</td>\n",
       "      <td>0.0153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SC00</td>\n",
       "      <td>MARVEL</td>\n",
       "      <td>0.8908</td>\n",
       "      <td>0.0190</td>\n",
       "      <td>0.0254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>SD00</td>\n",
       "      <td>BORING</td>\n",
       "      <td>0.8860</td>\n",
       "      <td>0.0253</td>\n",
       "      <td>0.0197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SD00</td>\n",
       "      <td>CONSUMABLES</td>\n",
       "      <td>0.8893</td>\n",
       "      <td>0.0178</td>\n",
       "      <td>0.0102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SD00</td>\n",
       "      <td>ESSENTIALS</td>\n",
       "      <td>0.9206</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SD00</td>\n",
       "      <td>GEMS</td>\n",
       "      <td>0.9334</td>\n",
       "      <td>0.0143</td>\n",
       "      <td>0.0173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SD00</td>\n",
       "      <td>MARVEL</td>\n",
       "      <td>0.9384</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "BWART WERKS        GROUP  utilization  scrapping  sampling\n",
       "0      DL00       BORING       0.8840     0.0278    0.0159\n",
       "1      DL00  CONSUMABLES       0.8820     0.0140    0.0246\n",
       "2      DL00   ESSENTIALS       0.8845     0.0232    0.0219\n",
       "3      DL00         GEMS       0.9039     0.0155    0.0221\n",
       "4      DL00       MARVEL       0.8920     0.0095    0.0100\n",
       "5      MI00       BORING       0.9127     0.0102    0.0159\n",
       "6      MI00  CONSUMABLES       0.9217     0.0284    0.0196\n",
       "7      MI00   ESSENTIALS       0.9146     0.0286    0.0163\n",
       "8      MI00         GEMS       0.9225     0.0108    0.0130\n",
       "9      MI00       MARVEL       0.9118     0.0411    0.0097\n",
       "10     SC00       BORING       0.8849     0.0451    0.0250\n",
       "11     SC00  CONSUMABLES       0.8959     0.0269    0.0144\n",
       "12     SC00   ESSENTIALS       0.8822     0.0190    0.0157\n",
       "13     SC00         GEMS       0.9056     0.0279    0.0153\n",
       "14     SC00       MARVEL       0.8908     0.0190    0.0254\n",
       "15     SD00       BORING       0.8860     0.0253    0.0197\n",
       "16     SD00  CONSUMABLES       0.8893     0.0178    0.0102\n",
       "17     SD00   ESSENTIALS       0.9206     0.0207    0.0205\n",
       "18     SD00         GEMS       0.9334     0.0143    0.0173\n",
       "19     SD00       MARVEL       0.9384     0.0155    0.0109"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.020765Z",
     "start_time": "2018-05-11T21:57:44.967880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"f2eea82d-808b-4bd2-9c64-efdd011a5436\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed .vega-actions > a {\n",
       "    transition: opacity 200ms ease-in;\n",
       "    opacity: 0.3;\n",
       "    margin-right: 0.6em;\n",
       "    color: #444;\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions > a:hover {\n",
       "    color: #000;\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       ".vega-embed:hover .vega-actions > a {\n",
       "    opacity: 1;\n",
       "    transition: 0s;\n",
       "}\n",
       "\n",
       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
       "    font-size: 1.2em;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#f2eea82d-808b-4bd2-9c64-efdd011a5436"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"values\": [{\"WERKS\": \"DL00\", \"GROUP\": \"BORING\", \"utilization\": 0.8839667977282656, \"scrapping\": 0.027810023091805528, \"sampling\": 0.015927104786868877}, {\"WERKS\": \"DL00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.881980362796179, \"scrapping\": 0.014042564874791177, \"sampling\": 0.024634152007755195}, {\"WERKS\": \"DL00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8845320256689672, \"scrapping\": 0.023156556483835817, \"sampling\": 0.021891270129555638}, {\"WERKS\": \"DL00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9038695842877333, \"scrapping\": 0.015497831278327405, \"sampling\": 0.02212583459232906}, {\"WERKS\": \"DL00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.892024198525023, \"scrapping\": 0.009477081204665703, \"sampling\": 0.009952553895335309}, {\"WERKS\": \"MI00\", \"GROUP\": \"BORING\", \"utilization\": 0.912652589904322, \"scrapping\": 0.01020408163265306, \"sampling\": 0.015907055662911816}, {\"WERKS\": \"MI00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.9216676571697372, \"scrapping\": 0.028383687989432766, \"sampling\": 0.019641360888218752}, {\"WERKS\": \"MI00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9145750551188949, \"scrapping\": 0.028624456596205827, \"sampling\": 0.016336368505623994}, {\"WERKS\": \"MI00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9224895945312716, \"scrapping\": 0.010832713139832962, \"sampling\": 0.012955153118884203}, {\"WERKS\": \"MI00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9118120876119468, \"scrapping\": 0.04109193887672322, \"sampling\": 0.009685998092892431}, {\"WERKS\": \"SC00\", \"GROUP\": \"BORING\", \"utilization\": 0.884905131871858, \"scrapping\": 0.04505314679129012, \"sampling\": 0.02503280211112913}, {\"WERKS\": \"SC00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8958756528987014, \"scrapping\": 0.02688547548086578, \"sampling\": 0.014365104330502254}, {\"WERKS\": \"SC00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8821791058258754, \"scrapping\": 0.01895716249088598, \"sampling\": 0.015653531723243495}, {\"WERKS\": \"SC00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9056490946125241, \"scrapping\": 0.027899762674490312, \"sampling\": 0.015286005269747539}, {\"WERKS\": \"SC00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.8907814563118557, \"scrapping\": 0.019016123366161524, \"sampling\": 0.02535281220094245}, {\"WERKS\": \"SD00\", \"GROUP\": \"BORING\", \"utilization\": 0.8859575656058069, \"scrapping\": 0.025265214963707427, \"sampling\": 0.01968174204355109}, {\"WERKS\": \"SD00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8892718843948639, \"scrapping\": 0.01775896351920327, \"sampling\": 0.01015856013947367}, {\"WERKS\": \"SD00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9206269370887934, \"scrapping\": 0.020669077266054048, \"sampling\": 0.020471969985318906}, {\"WERKS\": \"SD00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9334429963305074, \"scrapping\": 0.01429836770846514, \"sampling\": 0.017293011092833946}, {\"WERKS\": \"SD00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9384164664215231, \"scrapping\": 0.015484175199032803, \"sampling\": 0.010936143955773832}]}, \"mark\": {\"type\": \"bar\", \"stroke\": \"transparent\"}, \"encoding\": {\"column\": {\"type\": \"nominal\", \"field\": \"WERKS\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"\"}, \"field\": \"GROUP\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Utilization\"}, \"field\": \"utilization\", \"scale\": {\"domain\": [0, 1]}}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.3.0.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#f2eea82d-808b-4bd2-9c64-efdd011a5436\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#f2eea82d-808b-4bd2-9c64-efdd011a5436"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega3": "#f2eea82d-808b-4bd2-9c64-efdd011a5436"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "util = alt.Chart(h).mark_bar(stroke='transparent').encode(\n",
    "    x=alt.X('GROUP', axis=alt.Axis(title='')),\n",
    "    y=alt.Y('utilization', scale=alt.Scale(domain=(0, 1)), axis=alt.Axis(title='Utilization')),\n",
    "    column='WERKS'\n",
    ")\n",
    "util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.086214Z",
     "start_time": "2018-05-11T21:57:45.028810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"da1c56b3-2a8d-4900-98be-328359f7953a\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed .vega-actions > a {\n",
       "    transition: opacity 200ms ease-in;\n",
       "    opacity: 0.3;\n",
       "    margin-right: 0.6em;\n",
       "    color: #444;\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions > a:hover {\n",
       "    color: #000;\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       ".vega-embed:hover .vega-actions > a {\n",
       "    opacity: 1;\n",
       "    transition: 0s;\n",
       "}\n",
       "\n",
       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
       "    font-size: 1.2em;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#da1c56b3-2a8d-4900-98be-328359f7953a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"values\": [{\"WERKS\": \"DL00\", \"GROUP\": \"BORING\", \"utilization\": 0.8839667977282656, \"scrapping\": 0.027810023091805528, \"sampling\": 0.015927104786868877}, {\"WERKS\": \"DL00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.881980362796179, \"scrapping\": 0.014042564874791177, \"sampling\": 0.024634152007755195}, {\"WERKS\": \"DL00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8845320256689672, \"scrapping\": 0.023156556483835817, \"sampling\": 0.021891270129555638}, {\"WERKS\": \"DL00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9038695842877333, \"scrapping\": 0.015497831278327405, \"sampling\": 0.02212583459232906}, {\"WERKS\": \"DL00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.892024198525023, \"scrapping\": 0.009477081204665703, \"sampling\": 0.009952553895335309}, {\"WERKS\": \"MI00\", \"GROUP\": \"BORING\", \"utilization\": 0.912652589904322, \"scrapping\": 0.01020408163265306, \"sampling\": 0.015907055662911816}, {\"WERKS\": \"MI00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.9216676571697372, \"scrapping\": 0.028383687989432766, \"sampling\": 0.019641360888218752}, {\"WERKS\": \"MI00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9145750551188949, \"scrapping\": 0.028624456596205827, \"sampling\": 0.016336368505623994}, {\"WERKS\": \"MI00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9224895945312716, \"scrapping\": 0.010832713139832962, \"sampling\": 0.012955153118884203}, {\"WERKS\": \"MI00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9118120876119468, \"scrapping\": 0.04109193887672322, \"sampling\": 0.009685998092892431}, {\"WERKS\": \"SC00\", \"GROUP\": \"BORING\", \"utilization\": 0.884905131871858, \"scrapping\": 0.04505314679129012, \"sampling\": 0.02503280211112913}, {\"WERKS\": \"SC00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8958756528987014, \"scrapping\": 0.02688547548086578, \"sampling\": 0.014365104330502254}, {\"WERKS\": \"SC00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8821791058258754, \"scrapping\": 0.01895716249088598, \"sampling\": 0.015653531723243495}, {\"WERKS\": \"SC00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9056490946125241, \"scrapping\": 0.027899762674490312, \"sampling\": 0.015286005269747539}, {\"WERKS\": \"SC00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.8907814563118557, \"scrapping\": 0.019016123366161524, \"sampling\": 0.02535281220094245}, {\"WERKS\": \"SD00\", \"GROUP\": \"BORING\", \"utilization\": 0.8859575656058069, \"scrapping\": 0.025265214963707427, \"sampling\": 0.01968174204355109}, {\"WERKS\": \"SD00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8892718843948639, \"scrapping\": 0.01775896351920327, \"sampling\": 0.01015856013947367}, {\"WERKS\": \"SD00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9206269370887934, \"scrapping\": 0.020669077266054048, \"sampling\": 0.020471969985318906}, {\"WERKS\": \"SD00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9334429963305074, \"scrapping\": 0.01429836770846514, \"sampling\": 0.017293011092833946}, {\"WERKS\": \"SD00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9384164664215231, \"scrapping\": 0.015484175199032803, \"sampling\": 0.010936143955773832}]}, \"mark\": {\"type\": \"bar\", \"stroke\": \"transparent\"}, \"encoding\": {\"column\": {\"type\": \"nominal\", \"field\": \"WERKS\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"\"}, \"field\": \"GROUP\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Scrapping\"}, \"field\": \"scrapping\", \"scale\": {\"domain\": [0, 0.05]}}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.3.0.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#da1c56b3-2a8d-4900-98be-328359f7953a\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#da1c56b3-2a8d-4900-98be-328359f7953a"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega3": "#da1c56b3-2a8d-4900-98be-328359f7953a"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scrap = alt.Chart(h).mark_bar(stroke='transparent').encode(\n",
    "    x=alt.X('GROUP', axis=alt.Axis(title='')),\n",
    "    y=alt.Y('scrapping', scale=alt.Scale(domain=(0, .05)), axis=alt.Axis(title='Scrapping')),\n",
    "    column='WERKS'\n",
    ")\n",
    "\n",
    "scrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.139254Z",
     "start_time": "2018-05-11T21:57:45.091954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"ddffd2cd-992f-44cc-85a0-ca070bce0039\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed .vega-actions > a {\n",
       "    transition: opacity 200ms ease-in;\n",
       "    opacity: 0.3;\n",
       "    margin-right: 0.6em;\n",
       "    color: #444;\n",
       "    text-decoration: none;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions > a:hover {\n",
       "    color: #000;\n",
       "    text-decoration: underline;\n",
       "}\n",
       "\n",
       ".vega-embed:hover .vega-actions > a {\n",
       "    opacity: 1;\n",
       "    transition: 0s;\n",
       "}\n",
       "\n",
       ".vega-embed .error p {\n",
       "    color: firebrick;\n",
       "    font-size: 1.2em;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#ddffd2cd-992f-44cc-85a0-ca070bce0039"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"data\": {\"values\": [{\"WERKS\": \"DL00\", \"GROUP\": \"BORING\", \"utilization\": 0.8839667977282656, \"scrapping\": 0.027810023091805528, \"sampling\": 0.015927104786868877}, {\"WERKS\": \"DL00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.881980362796179, \"scrapping\": 0.014042564874791177, \"sampling\": 0.024634152007755195}, {\"WERKS\": \"DL00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8845320256689672, \"scrapping\": 0.023156556483835817, \"sampling\": 0.021891270129555638}, {\"WERKS\": \"DL00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9038695842877333, \"scrapping\": 0.015497831278327405, \"sampling\": 0.02212583459232906}, {\"WERKS\": \"DL00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.892024198525023, \"scrapping\": 0.009477081204665703, \"sampling\": 0.009952553895335309}, {\"WERKS\": \"MI00\", \"GROUP\": \"BORING\", \"utilization\": 0.912652589904322, \"scrapping\": 0.01020408163265306, \"sampling\": 0.015907055662911816}, {\"WERKS\": \"MI00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.9216676571697372, \"scrapping\": 0.028383687989432766, \"sampling\": 0.019641360888218752}, {\"WERKS\": \"MI00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9145750551188949, \"scrapping\": 0.028624456596205827, \"sampling\": 0.016336368505623994}, {\"WERKS\": \"MI00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9224895945312716, \"scrapping\": 0.010832713139832962, \"sampling\": 0.012955153118884203}, {\"WERKS\": \"MI00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9118120876119468, \"scrapping\": 0.04109193887672322, \"sampling\": 0.009685998092892431}, {\"WERKS\": \"SC00\", \"GROUP\": \"BORING\", \"utilization\": 0.884905131871858, \"scrapping\": 0.04505314679129012, \"sampling\": 0.02503280211112913}, {\"WERKS\": \"SC00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8958756528987014, \"scrapping\": 0.02688547548086578, \"sampling\": 0.014365104330502254}, {\"WERKS\": \"SC00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.8821791058258754, \"scrapping\": 0.01895716249088598, \"sampling\": 0.015653531723243495}, {\"WERKS\": \"SC00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9056490946125241, \"scrapping\": 0.027899762674490312, \"sampling\": 0.015286005269747539}, {\"WERKS\": \"SC00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.8907814563118557, \"scrapping\": 0.019016123366161524, \"sampling\": 0.02535281220094245}, {\"WERKS\": \"SD00\", \"GROUP\": \"BORING\", \"utilization\": 0.8859575656058069, \"scrapping\": 0.025265214963707427, \"sampling\": 0.01968174204355109}, {\"WERKS\": \"SD00\", \"GROUP\": \"CONSUMABLES\", \"utilization\": 0.8892718843948639, \"scrapping\": 0.01775896351920327, \"sampling\": 0.01015856013947367}, {\"WERKS\": \"SD00\", \"GROUP\": \"ESSENTIALS\", \"utilization\": 0.9206269370887934, \"scrapping\": 0.020669077266054048, \"sampling\": 0.020471969985318906}, {\"WERKS\": \"SD00\", \"GROUP\": \"GEMS\", \"utilization\": 0.9334429963305074, \"scrapping\": 0.01429836770846514, \"sampling\": 0.017293011092833946}, {\"WERKS\": \"SD00\", \"GROUP\": \"MARVEL\", \"utilization\": 0.9384164664215231, \"scrapping\": 0.015484175199032803, \"sampling\": 0.010936143955773832}]}, \"mark\": {\"type\": \"bar\", \"stroke\": \"transparent\"}, \"encoding\": {\"column\": {\"type\": \"nominal\", \"field\": \"WERKS\"}, \"x\": {\"type\": \"nominal\", \"axis\": {\"title\": \"\"}, \"field\": \"GROUP\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"title\": \"Sampling\"}, \"field\": \"sampling\", \"scale\": {\"domain\": [0, 0.03]}}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.3.0.json\"};\n",
       "var opt = {};\n",
       "var selector = \"#ddffd2cd-992f-44cc-85a0-ca070bce0039\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, opt, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#ddffd2cd-992f-44cc-85a0-ca070bce0039"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "jupyter-vega3": "#ddffd2cd-992f-44cc-85a0-ca070bce0039"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample = alt.Chart(h).mark_bar(stroke='transparent').encode(\n",
    "    x=alt.X('GROUP', axis=alt.Axis(title='')),\n",
    "    y=alt.Y('sampling', scale=alt.Scale(domain=(0, .03)), axis=alt.Axis(title='Sampling')),\n",
    "    column='WERKS'\n",
    ")\n",
    "\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is the fraud?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.214998Z",
     "start_time": "2018-05-11T21:57:45.146512Z"
    }
   },
   "outputs": [],
   "source": [
    "g = mseg.groupby(['WERKS','GROUP','BWART']).agg({'MENGE':'sum','USNAM':'unique'}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.231319Z",
     "start_time": "2018-05-11T21:57:45.219392Z"
    }
   },
   "outputs": [],
   "source": [
    "g['USNAM_NO'] = g['USNAM'].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-11T21:57:45.261534Z",
     "start_time": "2018-05-11T21:57:45.238066Z"
    }
   },
   "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>WERKS</th>\n",
       "      <th>GROUP</th>\n",
       "      <th>BWART</th>\n",
       "      <th>MENGE</th>\n",
       "      <th>USNAM</th>\n",
       "      <th>USNAM_NO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SC00</td>\n",
       "      <td>MARVEL</td>\n",
       "      <td>SA</td>\n",
       "      <td>20929</td>\n",
       "      <td>[GBI-073]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   WERKS   GROUP BWART  MENGE      USNAM  USNAM_NO\n",
       "58  SC00  MARVEL    SA  20929  [GBI-073]         1"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g[g['USNAM_NO']<3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-04T06:16:12.187499Z",
     "start_time": "2018-05-04T06:16:12.178351Z"
    }
   },
   "source": [
    "**Reflect:** Can you explain the fraud?"
   ]
  }
 ],
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