Blue Paint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Blue Paint Incident"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received an anonymous tip that something 'irregular' is happening with your purchases of blue paint. Your inital meeting with the purchase manager reveals the following:\n",
    "* Your company has five vendors that deliver **blue paint** (Material ID: BLUEPAINT).\n",
    "* Each vendor has a long-running contract with a standard delivery volume of **100** liters per shipment\n",
    "* The **delivery tolerance** acceptable for good receipt of shipment of blue paint is +/- 5 percent.\n",
    "\n",
    "You have requested a copy of the data from the SAP system. You focus on the `goods receipt` events of your purchasing processes. You have received two tables:\n",
    "* `MKPF` contains the header information of the material documents.\n",
    "* `MSEG` contains the line item information of the material documents.\n",
    "\n",
    "Analyze the data to understand the irregularity. "
   ]
  },
  {
   "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": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:05.458398Z",
     "start_time": "2018-04-26T23:53:05.199117Z"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = 'all'\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pandas](https://pandas.pydata.org) is the most important workhorse in data analytics.\n",
    "[Seaborn](https://seaborn.pydata.org) is a visualization library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:06.395427Z",
     "start_time": "2018-04-26T23:53:05.861229Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "pd.set_option('display.float_format', lambda x: '%.0f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have received two tables from the SAP system that contain all the information of the `goods receipt` step:\n",
    "2. The header information is stored in the table `MKPF`.\n",
    "1. The line items are stored in the table `MSEG`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:13.526106Z",
     "start_time": "2018-04-26T23:53:06.635061Z"
    }
   },
   "outputs": [],
   "source": [
    "mkpf_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MKPF.csv')\n",
    "mseg_table = pd.read_csv('https://raw.githubusercontent.com/mschermann/forensic_accounting/master/MSEG.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both tables from with huge numbers of columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:14.957134Z",
     "start_time": "2018-04-26T23:53:14.933530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'MBLNR', 'MJAHR', 'VGART', 'BLART', 'BLAUM', 'BLDAT', 'BUDAT',\n",
       "       'CPUDT', 'CPUTM', 'AEDAT', 'USNAM', 'TCODE', 'XBLNR', 'BKTXT', 'FRATH',\n",
       "       'FRBNR', 'WEVER', 'XABLN', 'AWSYS', 'BLA2D', 'TCODE2', 'BFWMS', 'EXNUM',\n",
       "       'SPE_BUDAT_UHR', 'SPE_BUDAT_ZONE', 'LE_VBELN', 'SPE_LOGSYS',\n",
       "       'SPE_MDNUM_EWM', 'GTS_CUSREF_NO', 'FLS_RSTO', 'MSR_ACTIVE', 'KNUMV'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Index(['MANDT', 'MBLNR', 'MJAHR', 'ZEILE', 'LINE_ID', 'PARENT_ID',\n",
       "       'LINE_DEPTH', 'MAA_URZEI', 'BWART', 'XAUTO',\n",
       "       ...\n",
       "       'FSH_UMSEA', 'FSH_UMCOLL', 'FSH_UMTHEME', 'SGT_CHINT', 'OINAVNW',\n",
       "       'OICONDCOD', 'CONDI', 'WRF_CHARSTC1', 'WRF_CHARSTC2', 'WRF_CHARSTC3'],\n",
       "      dtype='object', length=209)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkpf_table.columns\n",
    "mseg_table.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the definition of all the columns in the SAP system using the transaction code `SE16`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:52:57.969779Z",
     "start_time": "2018-04-26T22:52:57.965164Z"
    }
   },
   "source": [
    "## Understanding the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:55:41.657326Z",
     "start_time": "2018-04-26T22:55:41.652350Z"
    }
   },
   "source": [
    "### The MKPF table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:54:20.318069Z",
     "start_time": "2018-04-26T22:54:20.299872Z"
    }
   },
   "source": [
    "**Your task:** Reduce the MKPF table on the two columns. Store the result in a variable called `mkpf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:16.377846Z",
     "start_time": "2018-04-26T23:53:16.366023Z"
    }
   },
   "outputs": [],
   "source": [
    "mkpf = mkpf_table.loc[:, ['MBLNR', 'USNAM']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the first five rows of the MKPF table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:18.108365Z",
     "start_time": "2018-04-26T23:53:18.078625Z"
    }
   },
   "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>MBLNR</th>\n",
       "      <th>USNAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5000000010</td>\n",
       "      <td>GBI-085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5000000011</td>\n",
       "      <td>GBI-085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000000012</td>\n",
       "      <td>GBI-085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000000013</td>\n",
       "      <td>GBI-085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5000000014</td>\n",
       "      <td>GBI-085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MBLNR    USNAM\n",
       "0 5000000010  GBI-085\n",
       "1 5000000011  GBI-085\n",
       "2 5000000012  GBI-085\n",
       "3 5000000013  GBI-085\n",
       "4 5000000014  GBI-085"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkpf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The MSEG table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "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.\n",
    "* `MATNR` - Contains the material id.\n",
    "* `LIFNR` - Contains the vendor id.\n",
    "* `MENGE` - Contains the volume of the shipment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T22:54:20.318069Z",
     "start_time": "2018-04-26T22:54:20.299872Z"
    }
   },
   "source": [
    "**Your task:** Reduce the MSEG table on the columns of interest. Store the result in a variable called `mseg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:19.937650Z",
     "start_time": "2018-04-26T23:53:19.924452Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg = mseg_table.loc[:, ['MBLNR','BWART', 'MATNR','LIFNR','MENGE']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Show the unique movement types in the `MSEG`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:21.513168Z",
     "start_time": "2018-04-26T23:53:21.500900Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([101, 122, 161, 261])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg['BWART'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following list shows important movement types:\n",
    "* 101 - Goods receipt for a purchase order\n",
    "* 102 - Goods receipt for a purchase order - reversal\n",
    "* 122 - Return delivery to vendor\n",
    "* 161 - Return delivery to vendor for a purchase order\n",
    "* 261 - Consumption for production order from warehouse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Filter the MSEG table on the movement type of interest. Store the result in a variable called `mseg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:22.984176Z",
     "start_time": "2018-04-26T23:53:22.977203Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg = mseg[mseg['BWART']==101]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we are only interested in the goods movements of blue paint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task:** Filter the `MSEG` table on the material id of blue paint. Store the result in a variable called `mseg`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:26.127936Z",
     "start_time": "2018-04-26T23:53:26.113275Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg = mseg[mseg['MATNR']=='BLUEPAINT']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:47:34.944258Z",
     "start_time": "2018-04-26T23:47:34.928199Z"
    }
   },
   "source": [
    "**Your task:** Assign the variable type `int` to the column that contains the shipment volume."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:28.118341Z",
     "start_time": "2018-04-26T23:53:28.058930Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg['MENGE'] = mseg.MENGE.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:29.816737Z",
     "start_time": "2018-04-26T23:53:29.802196Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:17:50.387687Z",
     "start_time": "2018-04-26T23:17:50.370377Z"
    }
   },
   "source": [
    "**Your task:** Show a sample of the filtered `MSEG` table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:31.971257Z",
     "start_time": "2018-04-26T23:53:31.918544Z"
    }
   },
   "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>MBLNR</th>\n",
       "      <th>BWART</th>\n",
       "      <th>MATNR</th>\n",
       "      <th>LIFNR</th>\n",
       "      <th>MENGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>5000000450</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125000</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5000000519</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125134</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>5000000374</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125109</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5000000490</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125098</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5000000197</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125134</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5000000375</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125109</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>5000000400</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125000</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>5000000432</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125000</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5000000452</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125109</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>5000000504</td>\n",
       "      <td>101</td>\n",
       "      <td>BLUEPAINT</td>\n",
       "      <td>125109</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MBLNR  BWART      MATNR   LIFNR  MENGE\n",
       "81   5000000450    101  BLUEPAINT  125000    103\n",
       "150  5000000519    101  BLUEPAINT  125134     95\n",
       "36   5000000374    101  BLUEPAINT  125109    100\n",
       "121  5000000490    101  BLUEPAINT  125098    100\n",
       "8    5000000197    101  BLUEPAINT  125134    103\n",
       "37   5000000375    101  BLUEPAINT  125109     95\n",
       "62   5000000400    101  BLUEPAINT  125000    104\n",
       "69   5000000432    101  BLUEPAINT  125000    100\n",
       "83   5000000452    101  BLUEPAINT  125109    104\n",
       "135  5000000504    101  BLUEPAINT  125109    103"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:33:11.584587Z",
     "start_time": "2018-04-26T23:33:11.574337Z"
    }
   },
   "source": [
    "## Analysis of blue paint shipments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:33:15.543301Z",
     "start_time": "2018-04-26T23:33:15.530214Z"
    }
   },
   "source": [
    "**Your task**: Calculate the mean, the minimum value, and the maximum value of the received shipments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-27T00:43:32.336592Z",
     "start_time": "2018-04-27T00:43:32.301986Z"
    }
   },
   "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>MENGE</th>\n",
       "      <th>LIFNR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>100</td>\n",
       "      <td>125091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>95</td>\n",
       "      <td>125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>98</td>\n",
       "      <td>125098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100</td>\n",
       "      <td>125109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>103</td>\n",
       "      <td>125110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105</td>\n",
       "      <td>125134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MENGE  LIFNR\n",
       "count    162    162\n",
       "mean     100 125091\n",
       "std        3     45\n",
       "min       95 125000\n",
       "25%       98 125098\n",
       "50%      100 125109\n",
       "75%      103 125110\n",
       "max      105 125134"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg[['MENGE', 'LIFNR']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:36.001452Z",
     "start_time": "2018-04-26T23:53:35.953393Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.24074074074075"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "95"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "105"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg.MENGE.mean()\n",
    "mseg.MENGE.min()\n",
    "mseg.MENGE.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task**: Plot the volume of the shipments in relationship to the index (`mseg.index`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:38.123698Z",
     "start_time": "2018-04-26T23:53:37.932800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f035e404e48>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mseg.index, mseg.MENGE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task**: Confirm that five vendors deliver blue paint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:39.794760Z",
     "start_time": "2018-04-26T23:53:39.782547Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([125000, 125134, 125109, 125098, 125110])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mseg.LIFNR.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:28:39.480201Z",
     "start_time": "2018-04-26T23:28:39.468289Z"
    }
   },
   "source": [
    "We normalize the number of shipments per vendor. **Make sure to run the following cell before proceeding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:41.858496Z",
     "start_time": "2018-04-26T23:53:41.841896Z"
    }
   },
   "outputs": [],
   "source": [
    "mseg['order'] = mseg.groupby('LIFNR').cumcount()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:29:34.434691Z",
     "start_time": "2018-04-26T23:29:34.426340Z"
    }
   },
   "source": [
    "**Your task**: Develop a chart that shows the shipment volumes across time. To do this, replace the `<PLACEHOLDER>` in the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:51:16.713332Z",
     "start_time": "2018-04-26T23:51:16.391885Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'<VENDOR>'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '<VENDOR>'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-e20fab713fc2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmseg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'<VENDOR>'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_wrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'order'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'<VOLUME OF SHIPMENT>'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxhline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'<CONTRACTED VOLUME>'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'grey'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, row, col, hue, col_wrap, sharex, sharey, size, aspect, palette, row_order, col_order, hue_order, hue_kws, dropna, legend_out, despine, margin_titles, xlim, ylim, subplot_kws, gridspec_kws)\u001b[0m\n\u001b[1;32m    248\u001b[0m             \u001b[0mcol_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mcol_names\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcategorical_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;31m# Additional dict of kwarg -> list of values for mapping the hue var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2137\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2141\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1840\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1841\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1843\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1844\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3842\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3844\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3845\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2525\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2529\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: '<VENDOR>'"
     ]
    }
   ],
   "source": [
    "g = sns.FacetGrid(mseg, col='<VENDOR>', col_wrap=3);\n",
    "g.map(plt.plot, 'order', '<VOLUME OF SHIPMENT>');\n",
    "\n",
    "for a in g.axes:\n",
    "    a.axhline('<CONTRACTED VOLUME>', alpha=0.5, color='grey');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:46.768745Z",
     "start_time": "2018-04-26T23:53:46.008748Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f035c326908>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f035c3b7cc0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f035c24bb00>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f035c23f1d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f035c23f898>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f035c21a8d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(mseg, col='LIFNR', col_wrap=3)\n",
    "g.map(plt.plot, \"order\", \"MENGE\")\n",
    "\n",
    "for a in g.axes:\n",
    "    a.axhline(100, alpha=0.5, color='grey')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:35:25.454137Z",
     "start_time": "2018-04-26T23:35:25.448470Z"
    }
   },
   "source": [
    "## Identify the person of interest in the inventory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task**: Left-Join the `MSEG` and `MKPF` tables. Store the result in a variable called `inventory`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:48.878437Z",
     "start_time": "2018-04-26T23:53:48.862223Z"
    }
   },
   "outputs": [],
   "source": [
    "inventory = pd.merge(mseg, mkpf, on='MBLNR', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your task**: Group the `inventory` by the vendor id and show the unique id's of the inventory employees that managed shipments from each vendor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-26T23:53:50.566731Z",
     "start_time": "2018-04-26T23:53:50.546810Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LIFNR\n",
       "125000    [GBIFAC-01, GBI-018, GBI-034, GBI-053, GBI-054...\n",
       "125098    [GBI-013, GBI-018, GBI-012, GBI-008, GBI-044, ...\n",
       "125109    [GBIFAC-01, GBI-018, GBI-054, GBI-034, GBI-008...\n",
       "125110    [GBI-018, GBI-008, GBI-038, GBI-012, GBI-045, ...\n",
       "125134                                          [GBIFAC-01]\n",
       "Name: USNAM, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inventory.groupby('LIFNR')['USNAM'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Who is the person of interest?**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}