3.muesli_V.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tracking bottlenecks in the Muesli orders process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# hides warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "#style for the plots\n",
    "plt.style.use('fivethirtyeight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Origin Channel</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>CA-2019-121755</td>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>EH-13945</td>\n",
       "      <td>Eric Hoffmann</td>\n",
       "      <td>Email</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90049.0</td>\n",
       "      <td>West</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10003027</td>\n",
       "      <td>90.57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.7741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>CA-2019-118255</td>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>First Class</td>\n",
       "      <td>ON-18715</td>\n",
       "      <td>Odella Nelson</td>\n",
       "      <td>Sales</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eagan</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>55122.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10000171</td>\n",
       "      <td>45.98</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.7714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index        Order ID Order Date     Ship mode Customer ID  Customer Name  \\\n",
       "0   27.0  CA-2019-121755 2019-01-16  Second Class    EH-13945  Eric Hoffmann   \n",
       "1   45.0  CA-2019-118255 2019-03-11   First Class    ON-18715  Odella Nelson   \n",
       "\n",
       "  Origin Channel Country/Region         City       State  Postal Code  \\\n",
       "0          Email  United States  Los Angeles  California      90049.0   \n",
       "1          Sales  United States        Eagan   Minnesota      55122.0   \n",
       "\n",
       "    Region                 Category Sub-Category       Product ID  Sales  \\\n",
       "0     West  Special Projects Muesil  Gluten Free  TEC-AC-10003027  90.57   \n",
       "1  Central  Special Projects Muesil  Gluten Free  TEC-AC-10000171  45.98   \n",
       "\n",
       "   Quantity  Discount   Profit  \n",
       "0       3.0       0.0  11.7741  \n",
       "1       2.0       0.0  19.7714  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read data \n",
    "orders = pd.read_excel('data/muesli_raw_data.xlsx', header=1) #Please change your patch accordingly\n",
    "orders.head(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create dataframes for each excel sheet:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>origin_channel</th>\n",
       "      <th>country/region</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>region</th>\n",
       "      <th>category</th>\n",
       "      <th>sub-category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27.0</td>\n",
       "      <td>CA-2019-121755</td>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>EH-13945</td>\n",
       "      <td>Eric Hoffmann</td>\n",
       "      <td>Email</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90049.0</td>\n",
       "      <td>West</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10003027</td>\n",
       "      <td>90.57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.7741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45.0</td>\n",
       "      <td>CA-2019-118255</td>\n",
       "      <td>2019-03-11</td>\n",
       "      <td>First Class</td>\n",
       "      <td>ON-18715</td>\n",
       "      <td>Odella Nelson</td>\n",
       "      <td>Sales</td>\n",
       "      <td>United States</td>\n",
       "      <td>Eagan</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>55122.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10000171</td>\n",
       "      <td>45.98</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.7714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        order_id order_date     ship_mode customer_id  customer_name  \\\n",
       "0   27.0  CA-2019-121755 2019-01-16  Second Class    EH-13945  Eric Hoffmann   \n",
       "1   45.0  CA-2019-118255 2019-03-11   First Class    ON-18715  Odella Nelson   \n",
       "\n",
       "  origin_channel country/region         city       state  postal_code  \\\n",
       "0          Email  United States  Los Angeles  California      90049.0   \n",
       "1          Sales  United States        Eagan   Minnesota      55122.0   \n",
       "\n",
       "    region                 category sub-category       product_id  sales  \\\n",
       "0     West  Special Projects Muesil  Gluten Free  TEC-AC-10003027  90.57   \n",
       "1  Central  Special Projects Muesil  Gluten Free  TEC-AC-10000171  45.98   \n",
       "\n",
       "   quantity  discount   profit  \n",
       "0       3.0       0.0  11.7741  \n",
       "1       2.0       0.0  19.7714  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean orders dataframe\n",
    "orders.columns = orders.columns.str.lower()\n",
    "orders.columns = orders.columns.str.replace(\" \",\"_\")\n",
    "orders.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>arrival_scan_date</th>\n",
       "      <th>customer_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2019-109666</td>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>Kunst Miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2019-138933</td>\n",
       "      <td>2019-05-03</td>\n",
       "      <td>Jack Lebron</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         order_id arrival_scan_date customer_name\n",
       "0  CA-2019-109666        2019-05-03  Kunst Miller\n",
       "1  CA-2019-138933        2019-05-03   Jack Lebron"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#create arrival df\n",
    "arrival= pd.read_excel('data/muesli_raw_data.xlsx', sheet_name='Campaign Data')\n",
    "\n",
    "#clean arrival df\n",
    "arrival.columns = arrival.columns.str.lower()\n",
    "arrival.columns = arrival.columns.str.replace(\" \",\"_\")\n",
    "arrival.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>on_truck_scan_date</th>\n",
       "      <th>ship_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3074.0</td>\n",
       "      <td>CA-2019-125206</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4919.0</td>\n",
       "      <td>CA-2019-160304</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   row_id        order_id order_date on_truck_scan_date            ship_mode\n",
       "0  3074.0  CA-2019-125206 2019-01-03         2019-01-07              Express\n",
       "1  4919.0  CA-2019-160304 2019-01-02         2019-01-09  Standard Processing"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create truck df\n",
    "truck = pd.read_excel('data/muesli_raw_data.xlsx', sheet_name='Order Process Data')\n",
    "\n",
    "#clean truck df\n",
    "truck.columns = truck.columns.str.lower()\n",
    "truck.columns = truck.columns.str.replace(\" \",\"_\")\n",
    "truck.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>CA-2019-125206</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Express</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2019-160304</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2019-160304</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2019-116365</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2019-116365</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>5894</th>\n",
       "      <td>CA-2020-143259</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5895</th>\n",
       "      <td>CA-2020-143259</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5896</th>\n",
       "      <td>CA-2020-115427</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5897</th>\n",
       "      <td>CA-2020-115427</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5898</th>\n",
       "      <td>CA-2020-156720</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5899 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            order_id order_date on_truck_scan_date            ship_mode\n",
       "0     CA-2019-125206 2019-01-03         2019-01-07              Express\n",
       "1     CA-2019-160304 2019-01-02         2019-01-09  Standard Processing\n",
       "2     CA-2019-160304 2019-01-02         2019-01-09  Standard Processing\n",
       "3     US-2019-116365 2019-01-03         2019-01-09  Standard Processing\n",
       "4     US-2019-116365 2019-01-03         2019-01-09  Standard Processing\n",
       "...              ...        ...                ...                  ...\n",
       "5894  CA-2020-143259 2020-12-30         2021-01-06  Standard Processing\n",
       "5895  CA-2020-143259 2020-12-30         2021-01-06  Standard Processing\n",
       "5896  CA-2020-115427 2020-12-30         2021-01-06  Standard Processing\n",
       "5897  CA-2020-115427 2020-12-30         2021-01-06  Standard Processing\n",
       "5898  CA-2020-156720 2020-12-30         2021-01-06  Standard Processing\n",
       "\n",
       "[5899 rows x 4 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truck.drop('row_id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5899 entries, 0 to 5898\n",
      "Data columns (total 5 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   row_id              5899 non-null   float64       \n",
      " 1   order_id            5899 non-null   object        \n",
      " 2   order_date          5899 non-null   datetime64[ns]\n",
      " 3   on_truck_scan_date  5899 non-null   datetime64[ns]\n",
      " 4   ship_mode           5899 non-null   object        \n",
      "dtypes: datetime64[ns](2), float64(1), object(2)\n",
      "memory usage: 230.6+ KB\n"
     ]
    }
   ],
   "source": [
    "truck.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>ready_to_ship_date</th>\n",
       "      <th>pickup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id ready_to_ship_date pickup_date\n",
       "0  CA-2019-116540         2019-09-02  2019-09-03\n",
       "1  CA-2019-116540         2019-09-02  2019-09-03"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#create ready_pickup df\n",
    "ready_pickup = pd.read_excel('data/muesli_raw_data.xlsx', sheet_name='InternData Study')\n",
    "\n",
    "#clean ready_pickup df\n",
    "ready_pickup.columns = ready_pickup.columns.str.lower()\n",
    "ready_pickup.columns = ready_pickup.columns.str.replace(\" \",\"_\")\n",
    "ready_pickup.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicates for orders: 0\n",
      "duplicates for arrival: 0\n",
      "duplicates for truck: 0\n",
      "duplicates for ready_pickup: 147\n"
     ]
    }
   ],
   "source": [
    "duplicates_orders = orders[orders.duplicated(keep=False)].shape\n",
    "print(f'duplicates for orders: {duplicates_orders[0]}') \n",
    "\n",
    "duplicates_arrival = arrival[arrival.duplicated(keep=False)].shape\n",
    "print(f'duplicates for arrival: {duplicates_arrival[0]}')\n",
    "\n",
    "duplicates_truck = truck[truck.duplicated(keep=False)].shape\n",
    "print(f'duplicates for truck: {duplicates_truck[0]}')\n",
    "\n",
    "duplicates_ready_pickup = ready_pickup[ready_pickup.duplicated(keep=False)].shape\n",
    "print(f'duplicates for ready_pickup: {duplicates_ready_pickup[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duplicates for ready_pickup: 0\n"
     ]
    }
   ],
   "source": [
    "# dropping and corroborating duplicates in ready_pickup\n",
    "\n",
    "ready_pickup.drop_duplicates(subset='order_id', inplace=True)\n",
    "duplicates_ready_pickup = ready_pickup[ready_pickup.duplicated(keep=False)].shape\n",
    "print(f'duplicates for ready_pickup: {duplicates_ready_pickup[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining dataframes to measure the  overall lead time of the muesli orders (1° KPI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>index</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name_x</th>\n",
       "      <th>origin_channel</th>\n",
       "      <th>country/region</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>...</th>\n",
       "      <th>region</th>\n",
       "      <th>category</th>\n",
       "      <th>sub-category</th>\n",
       "      <th>product_id</th>\n",
       "      <th>sales</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>profit</th>\n",
       "      <th>arrival_scan_date</th>\n",
       "      <th>customer_name_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>252.0</td>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>KC-16540</td>\n",
       "      <td>Kelly Collister</td>\n",
       "      <td>Email</td>\n",
       "      <td>United States</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10003832</td>\n",
       "      <td>3347.37</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>636.0003</td>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>Kelly Collister</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>325.0</td>\n",
       "      <td>CA-2019-162138</td>\n",
       "      <td>2019-04-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GK-14620</td>\n",
       "      <td>Grace Kelly</td>\n",
       "      <td>Sales</td>\n",
       "      <td>United States</td>\n",
       "      <td>Hesperia</td>\n",
       "      <td>California</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>Special Projects Muesil</td>\n",
       "      <td>Gluten Free</td>\n",
       "      <td>TEC-AC-10001908</td>\n",
       "      <td>99.99</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.9965</td>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>Grace Kelly</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index        order_id order_date       ship_mode customer_id  \\\n",
       "0  252.0  CA-2019-145625 2019-09-11  Standard Class    KC-16540   \n",
       "2  325.0  CA-2019-162138 2019-04-23  Standard Class    GK-14620   \n",
       "\n",
       "   customer_name_x origin_channel country/region       city       state  ...  \\\n",
       "0  Kelly Collister          Email  United States  San Diego  California  ...   \n",
       "2      Grace Kelly          Sales  United States   Hesperia  California  ...   \n",
       "\n",
       "   region                 category sub-category       product_id    sales  \\\n",
       "0    West  Special Projects Muesil  Gluten Free  TEC-AC-10003832  3347.37   \n",
       "2    West  Special Projects Muesil  Gluten Free  TEC-AC-10001908    99.99   \n",
       "\n",
       "   quantity  discount    profit  arrival_scan_date  customer_name_y  \n",
       "0      13.0       0.0  636.0003         2019-09-23  Kelly Collister  \n",
       "2       1.0       0.0   34.9965         2019-05-06      Grace Kelly  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining orders and arrivals = df1\n",
    "df1= pd.merge(orders, arrival, on= \"order_id\", how= \"inner\")\n",
    "df1.drop_duplicates(subset='order_id', inplace=True)\n",
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 333 entries, 0 to 667\n",
      "Data columns (total 21 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   index              333 non-null    float64       \n",
      " 1   order_id           333 non-null    object        \n",
      " 2   order_date         333 non-null    datetime64[ns]\n",
      " 3   ship_mode          333 non-null    object        \n",
      " 4   customer_id        333 non-null    object        \n",
      " 5   customer_name_x    333 non-null    object        \n",
      " 6   origin_channel     333 non-null    object        \n",
      " 7   country/region     333 non-null    object        \n",
      " 8   city               333 non-null    object        \n",
      " 9   state              333 non-null    object        \n",
      " 10  postal_code        333 non-null    float64       \n",
      " 11  region             333 non-null    object        \n",
      " 12  category           333 non-null    object        \n",
      " 13  sub-category       333 non-null    object        \n",
      " 14  product_id         333 non-null    object        \n",
      " 15  sales              333 non-null    float64       \n",
      " 16  quantity           333 non-null    float64       \n",
      " 17  discount           333 non-null    float64       \n",
      " 18  profit             333 non-null    float64       \n",
      " 19  arrival_scan_date  333 non-null    datetime64[ns]\n",
      " 20  customer_name_y    333 non-null    object        \n",
      "dtypes: datetime64[ns](2), float64(6), object(13)\n",
      "memory usage: 57.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Time between the order date and the arrival to the customer**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>arrival_scan_date</th>\n",
       "      <th>day_dif_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2019-162138</td>\n",
       "      <td>2019-04-23</td>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id order_date arrival_scan_date  day_dif_order\n",
       "0  CA-2019-145625 2019-09-11        2019-09-23             12\n",
       "2  CA-2019-162138 2019-04-23        2019-05-06             13"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1 has order dates and arrivals for the gerneral lead time kpi\n",
    "df1 = df1[[\"order_id\", \"order_date\", \"arrival_scan_date\"]]\n",
    "\n",
    "#create columns for day difference\n",
    "df1[\"day_dif_order\"]= (df1[\"arrival_scan_date\"]-df1[\"order_date\"]).dt.days\n",
    "df1.head(2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting overall lead time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('default'):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        df1\n",
    "        .groupby('day_dif_order')\n",
    "        .count()['order_id']\n",
    "        .plot.bar(rot=360) \n",
    "    )\n",
    "    ax.set_title('Overall leadtime from order date to arrival date')\n",
    "\n",
    "    ax.set_xlabel('Lead time');\n",
    "    ax.set_ylabel('Count of orders')\n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**  \n",
    "There's not enough data here because the arrival data is only available for the 3.3% of the orders, so it is not representative of the whole case.\n",
    "The man of the overall time is 11 days and the range is 14 days.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    333.0\n",
       "mean      10.8\n",
       "std        2.9\n",
       "min        3.0\n",
       "25%        9.0\n",
       "50%       11.0\n",
       "75%       13.0\n",
       "max       17.0\n",
       "Name: day_dif_order, dtype: float64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary statistics for first kpi\n",
    "#according to company estimation it should take 13 day in the worth case.\n",
    "df1.describe()[\"day_dif_order\"].round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot to see the outliers in the delivary dates\n",
    "with plt.style.context('default'):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        df1\n",
    "        .groupby('day_dif_order')\n",
    "        .count()['order_id']\n",
    "        .plot.box() \n",
    "    )\n",
    "    ax.set_title('Overall leadtime from order date to arrival date')\n",
    "\n",
    "    ax.set_xlabel('Lead time');\n",
    "    ax.set_ylabel('Count of orders')\n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**  \n",
    "According to box plot more than half of orders had the delivery date more then 12 days (which is the median)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Making the 1° KPI more specific: Calculating lead time according to the shipping method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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>order_id</th>\n",
       "      <th>order_date_ord</th>\n",
       "      <th>arrival_scan_date</th>\n",
       "      <th>day_dif_order</th>\n",
       "      <th>row_id</th>\n",
       "      <th>order_datetruck_</th>\n",
       "      <th>on_truck_scan_date</th>\n",
       "      <th>ship_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>12</td>\n",
       "      <td>251.0</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2019-09-18</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2019-09-23</td>\n",
       "      <td>12</td>\n",
       "      <td>252.0</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>2019-09-18</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id order_date_ord arrival_scan_date  day_dif_order  row_id  \\\n",
       "0  CA-2019-145625     2019-09-11        2019-09-23             12   251.0   \n",
       "1  CA-2019-145625     2019-09-11        2019-09-23             12   252.0   \n",
       "\n",
       "  order_datetruck_ on_truck_scan_date            ship_mode  \n",
       "0       2019-09-11         2019-09-18  Standard Processing  \n",
       "1       2019-09-11         2019-09-18  Standard Processing  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#joining df1 and truck for ship method\n",
    "# lead_t_shipmode = pd.concat([df1, truck], axis=1, join='inner\n",
    "lead_t_shipmode = df1.merge(truck, how='inner', on='order_id', suffixes=('_ord', 'truck_'))\n",
    "lead_t_shipmode.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 668 entries, 0 to 667\n",
      "Data columns (total 8 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   order_id            668 non-null    object        \n",
      " 1   order_date_ord      668 non-null    datetime64[ns]\n",
      " 2   arrival_scan_date   668 non-null    datetime64[ns]\n",
      " 3   day_dif_order       668 non-null    int64         \n",
      " 4   row_id              668 non-null    float64       \n",
      " 5   order_datetruck_    668 non-null    datetime64[ns]\n",
      " 6   on_truck_scan_date  668 non-null    datetime64[ns]\n",
      " 7   ship_mode           668 non-null    object        \n",
      "dtypes: datetime64[ns](4), float64(1), int64(1), object(2)\n",
      "memory usage: 47.0+ KB\n"
     ]
    }
   ],
   "source": [
    "lead_t_shipmode.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>day_dif_order</th>\n",
       "      <th>row_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>668.000000</td>\n",
       "      <td>668.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.886228</td>\n",
       "      <td>4895.901198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.878174</td>\n",
       "      <td>2989.513912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2170.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>4949.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>7522.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>9987.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       day_dif_order       row_id\n",
       "count     668.000000   668.000000\n",
       "mean       10.886228  4895.901198\n",
       "std         2.878174  2989.513912\n",
       "min         3.000000     1.000000\n",
       "25%         9.000000  2170.750000\n",
       "50%        12.000000  4949.000000\n",
       "75%        13.000000  7522.250000\n",
       "max        17.000000  9987.000000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_t_shipmode.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>order_id</th>\n",
       "      <th>order_date_ord</th>\n",
       "      <th>day_dif_order</th>\n",
       "      <th>ship_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>12</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2019-145625</td>\n",
       "      <td>2019-09-11</td>\n",
       "      <td>12</td>\n",
       "      <td>Standard Processing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id order_date_ord  day_dif_order            ship_mode\n",
       "0  CA-2019-145625     2019-09-11             12  Standard Processing\n",
       "1  CA-2019-145625     2019-09-11             12  Standard Processing"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#choosing the columns we need and cleaning columns\n",
    "lead_t_shipmode = lead_t_shipmode[['order_id', 'order_date_ord', 'day_dif_order', 'ship_mode']]\n",
    "lead_t_shipmode.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>ship_mode</th>\n",
       "      <th>Express</th>\n",
       "      <th>Standard Processing</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_dif_order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "ship_mode      Express  Standard Processing\n",
       "day_dif_order                              \n",
       "3                    3                    0\n",
       "4                   14                    0\n",
       "5                   26                    0\n",
       "6                   22                    4\n",
       "7                   14                   11\n",
       "8                   30                   26\n",
       "9                   12                   22\n",
       "10                  15                   44\n",
       "11                   5                   81\n",
       "12                   4                  111\n",
       "13                   3                  116\n",
       "14                   0                   55\n",
       "15                   0                   43\n",
       "16                   0                    5\n",
       "17                   0                    2"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shipment aggregated by overall lead time\n",
    "kpi1 = pd.crosstab(lead_t_shipmode['day_dif_order'], columns=lead_t_shipmode['ship_mode'])\n",
    "kpi1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using seaborn to plot\n",
    "with plt.style.context('default'): #evoke style temporarily\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,5))\n",
    "    fig = sns.countplot( x=lead_t_shipmode['day_dif_order'], hue=lead_t_shipmode['ship_mode'], palette=['#008080', '#da70d6']) #hue serves as group by\n",
    "    plt.legend(loc='upper left')\n",
    "    fig.set_xlabel('Lead time')\n",
    "    fig.set_ylabel('Amount of orders')\n",
    "    # plt.box(on=None) #this line takes all the box off\n",
    "    fig.spines['right'].set_color('none') # turn off the right spine\n",
    "    fig.spines['top'].set_color('none') #turn off the top spine\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Avg. Lead Time</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Express</th>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Standard Processing</th>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Avg. Lead Time\n",
       "Shipping Mode                      \n",
       "Express                         7.2\n",
       "Standard Processing            11.9"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lead_t_shipmode.rename(columns={'day_dif_order': 'Avg. Lead Time', 'truck_day_dif': 'Days for order to be ready', 'ship_mode': 'Shipping Mode'}, inplace=True)\n",
    "lead_t_shipmode.groupby('Shipping Mode').mean().round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**  \n",
    "- In average, the lead time has 5 days difference between shipment methods \n",
    "- Mean for lead time is 11 days\n",
    "- Express is faster as expected\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time between order date and loading Muesli in the truck (2° KPI)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>on_truck_scan_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>truck_day_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3074.0</td>\n",
       "      <td>CA-2019-125206</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>Express</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4919.0</td>\n",
       "      <td>CA-2019-160304</td>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>Standard Processing</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date on_truck_scan_date            ship_mode  \\\n",
       "0  3074.0  CA-2019-125206 2019-01-03         2019-01-07              Express   \n",
       "1  4919.0  CA-2019-160304 2019-01-02         2019-01-09  Standard Processing   \n",
       "\n",
       "   truck_day_dif  \n",
       "0              4  \n",
       "1              7  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we don't need to join orders with truck because the 5899 entries already have both the order_date and the truck_date\n",
    "\n",
    "#create columns for day difference\n",
    "truck['truck_day_dif'] = (truck['on_truck_scan_date'] - truck['order_date']).dt.days\n",
    "truck.head(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting leadtime_muesli\n",
    "with plt.style.context('default'):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        truck\n",
    "        .groupby('truck_day_dif')\n",
    "        .count()['row_id']\n",
    "        .plot.bar(rot=360) \n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('days', fontsize=10)\n",
    "    ax.set_ylabel('orders', fontsize=10)\n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary statistics for the second kpi (order-date and on truck delivery date)\n",
    "\n",
    "It should take 6 days at the worse case.\n",
    "we have complete data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5899.0\n",
       "mean        6.1\n",
       "std         2.5\n",
       "min         0.0\n",
       "25%         5.0\n",
       "50%         7.0\n",
       "75%         8.0\n",
       "max        12.0\n",
       "Name: truck_day_dif, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truck.describe()[\"truck_day_dif\"].round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Box plot for 2° KPI**  \n",
    "- It takes 6 days in avegerage to load the order in the truck\n",
    "- Most of the orders take 7 or more days to be loaded\n",
    "In next step we want to see what happens from ready to ship date and on truck scan date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('default'):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        truck\n",
    "        .groupby('truck_day_dif')\n",
    "        .count()['row_id']\n",
    "        .plot.box() \n",
    "    )\n",
    "    ax.set_title('leadtime from order date to on truck scan date')\n",
    "\n",
    "    ax.set_xlabel('Lead time')\n",
    "    ax.set_ylabel('count of orders')\n",
    "    \n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time between ready to ship date and being loaded in the truck (3° KPI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>on_truck_scan_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>truck_day_dif</th>\n",
       "      <th>ready_to_ship_date</th>\n",
       "      <th>pickup_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3416.0</td>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>Express</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3417.0</td>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>Express</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date on_truck_scan_date ship_mode  \\\n",
       "0  3416.0  CA-2019-116540 2019-09-02         2019-09-03   Express   \n",
       "1  3417.0  CA-2019-116540 2019-09-02         2019-09-03   Express   \n",
       "\n",
       "   truck_day_dif ready_to_ship_date pickup_date  \n",
       "0              1         2019-09-02  2019-09-03  \n",
       "1              1         2019-09-02  2019-09-03  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#join truck and ready_pickup\n",
    "df3= pd.merge(truck, ready_pickup, on= \"order_id\", how= \"inner\")\n",
    "df3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>row_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_date</th>\n",
       "      <th>on_truck_scan_date</th>\n",
       "      <th>ship_mode</th>\n",
       "      <th>truck_day_dif</th>\n",
       "      <th>ready_to_ship_date</th>\n",
       "      <th>pickup_date</th>\n",
       "      <th>ready_to_ship_on_truck_dif</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3416.0</td>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>Express</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3417.0</td>\n",
       "      <td>CA-2019-116540</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>Express</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-09-02</td>\n",
       "      <td>2019-09-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   row_id        order_id order_date on_truck_scan_date ship_mode  \\\n",
       "0  3416.0  CA-2019-116540 2019-09-02         2019-09-03   Express   \n",
       "1  3417.0  CA-2019-116540 2019-09-02         2019-09-03   Express   \n",
       "\n",
       "   truck_day_dif ready_to_ship_date pickup_date  ready_to_ship_on_truck_dif  \n",
       "0              1         2019-09-02  2019-09-03                           1  \n",
       "1              1         2019-09-02  2019-09-03                           1  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lead time from on truck scan date and ready to ship date\n",
    "df3['ready_to_ship_on_truck_dif'] = (df3['on_truck_scan_date'] - df3['ready_to_ship_date']).dt.days\n",
    "df3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    164\n",
       "1    107\n",
       "3     84\n",
       "0     63\n",
       "Name: ready_to_ship_on_truck_dif, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['ready_to_ship_on_truck_dif'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting leadtime from ready to ship date to on truck scan date\n",
    "with plt.style.context('default'):\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        df3\n",
    "        .groupby('ready_to_ship_on_truck_dif')\n",
    "        .count()['order_id']\n",
    "        .plot.bar(rot=360)\n",
    "        \n",
    "    )\n",
    "    ax.set_xlabel('days', fontsize=10);\n",
    "    ax.set_ylabel('orders', fontsize=10)\n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**  \n",
    "- Data here is insufficient to be representative of the whole case.\n",
    "- According to company estimation it should take 3 days at the worth case\n",
    "- They are complying to the times, based on the data that we have.\n",
    "- The mode of is 2 days, showing a good performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    418.0\n",
       "mean       1.6\n",
       "std        1.0\n",
       "min        0.0\n",
       "25%        1.0\n",
       "50%        2.0\n",
       "75%        2.0\n",
       "max        3.0\n",
       "Name: ready_to_ship_on_truck_dif, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()['ready_to_ship_on_truck_dif'].round(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lead time between on truck scan date and arrival scan date "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>row_id</th>\n",
       "      <th>truck_day_dif</th>\n",
       "      <th>on_truck_arrival_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>668.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4896.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2990.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2171.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4949.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7522.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9987.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       row_id  truck_day_dif  on_truck_arrival_diff\n",
       "count   668.0          668.0                  668.0\n",
       "mean   4896.0            6.0                    5.0\n",
       "std    2990.0            3.0                    1.0\n",
       "min       1.0            0.0                    1.0\n",
       "25%    2171.0            5.0                    4.0\n",
       "50%    4949.0            7.0                    5.0\n",
       "75%    7522.0            8.0                    5.0\n",
       "max    9987.0           12.0                    7.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merging truck and arrival info\n",
    "shipment = pd.merge(truck, arrival, on=\"order_id\", how= \"right\")\n",
    "#created a column for weekday\n",
    "shipment[\"on_truck_arrival_diff\"]= (shipment[\"arrival_scan_date\"] - shipment[\"on_truck_scan_date\"]).dt.days\n",
    "#count the values\n",
    "shipment[\"on_truck_arrival_diff\"].value_counts()\n",
    "shipment.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ploting lead time in the delivery company\n",
    "with plt.style.context('default'):\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(10,5))\n",
    "    (\n",
    "        shipment\n",
    "        .groupby(\"on_truck_arrival_diff\")\n",
    "        .count()['order_id']\n",
    "        .plot.bar(rot=360) \n",
    "    )\n",
    "\n",
    "    ax.set_xlabel('days', fontsize = 10)\n",
    "    ax.set_ylabel('orders', fontsize = 10)\n",
    "    ax.set_title('KPI 3: Lead time in delivery company')\n",
    "    ax.spines['right'].set_color('none') # turn off the right spine\n",
    "    ax.spines['top'].set_color('none') #turn off the top spine;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**\n",
    "- According to company estimations, orders should take about 5 days since they leave the muesli company and get to the customer\n",
    "- Orders are arriving to costumers on average in less than 5 days, showing a good performance in the delivery company process\n",
    "- Most of the orders take 5 days or less. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra information: when are the customers ordering?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orders_weekday\n",
      "0    1408\n",
      "1    1783\n",
      "2    1706\n",
      "3    1736\n",
      "4    1648\n",
      "5     876\n",
      "6     837\n",
      "Name: order_id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#create columns for orders date\n",
    "orders[\"orders_weekday\"]= (orders['order_date']).dt.dayofweek\n",
    "when_orders_made = orders.groupby('orders_weekday').order_id.count()\n",
    "print(when_orders_made)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the orders in days\n",
    "with plt.style.context('default'):\n",
    "    plt.hist(x=orders['orders_weekday'], bins=7, alpha=0.8, edgecolor='k', color='purple');\n",
    "    plt.xticks([0, 1, 2, 3, 4, 5, 6], ['Mon', 'Tues', 'Wed', 'Thur', 'Fri', 'Sa', 'So']);\n",
    "    plt.xlabel('Order day', fontsize = 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Findings**  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9994\n",
      "83% of the orders are made during the week\n",
      "17% of the orders are made during the weekend\n"
     ]
    }
   ],
   "source": [
    "total_orders = orders['orders_weekday'].count()\n",
    "print(total_orders) # 9994\n",
    "week = 1408 + 1783 + 1706 + 1736 + 1648\n",
    "weekend = total_orders - week\n",
    "print(f'{round(week*100/total_orders)}% of the orders are made during the week')\n",
    "print(f'{round(weekend*100/total_orders)}% of the orders are made during the weekend')"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "ab3d85a18739f6fff6a9c8c504adc2ff9340867b576dede986e2ee74c099e4e5"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('nf_base')",
   "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": "3.9.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}