{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <strong> The Goal </strong>\n",
    "Provide a specific set of recommendations on how to improve The Client's business based on the provided data. Provide the recommendations and any supporting analyses and assumptions.\n",
    "# <strong> Approach </strong>\n",
    "## 1. QA Data \n",
    "- confirm all data is present and entered correctly. If null values are present, determine whether to \n",
    "    1) drop row values\n",
    "    2) impute null values using other row indicators, if applicable\n",
    "    \n",
    "## 2. Exploratory Data Analysis\n",
    "- look for trends in the data and see if any obvious patterns emerge. Make sure data trends make sense - if they do not, reevaluate data quality and determine reason for discrepancy\n",
    "- look for patterns that emerge (or don't)\n",
    "- identify target metrics for improvement\n",
    "    \n",
    "## 3. (Time Permitting) Competitive Analysis / ML Analysis \n",
    "- compare The Client data to competitors (e.g. DoorDash, UberEats, GrubHub, and proprietary food delivery services like Domino's) and identify gaps / competitive advantages\n",
    "- using the sklearn library, utilize the appropriate models to look for most impactful indicators to either further exploit advantages or close gaps\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PHASE 1: Import & QA the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1a) Import the dataset and instantiate the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import the necessary modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#the dts list denotes the columns that represent datetimes. Because the datetime format provided is not standard, \n",
    "#we will not adjust them when we instatiate the dataframe; we will adjust each column with pd.to_datetime. \n",
    "dts = [\"Customer placed order datetime\", \n",
    "       \"Placed order with restaurant datetime\", \n",
    "       \"Driver at restaurant datetime\", \n",
    "       \"Delivered to consumer datetime\"]\n",
    "#instatiate the dataframe from the provided excel\n",
    "df = pd.read_excel(\"Sample deliveries data - 1 month (1) (2).xlsx\")\n",
    "#Convert datetime columns to appropriate data types, and convert from base UTC to the Client's operational time zone (PT)  \n",
    "for col in dts:\n",
    "    df[col] = pd.to_datetime(df[col], format = \"%d %H:%M:%S\", utc=True).dt.tz_convert('US/Pacific')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1b) check for missing values in dataframe "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique delivery regions: ['Mountain View' 'Palo Alto' 'San Jose' 'None']\n",
      "26 entries do not have region data\n",
      "\n",
      "# Missing values for each column:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Customer placed order datetime              0\n",
       "Placed order with restaurant datetime      40\n",
       "Driver at restaurant datetime            4531\n",
       "Delivered to consumer datetime              0\n",
       "Driver ID                                   0\n",
       "Restaurant ID                               0\n",
       "Consumer ID                                 0\n",
       "Delivery Region                            26\n",
       "Is ASAP                                     0\n",
       "Order total                                 0\n",
       "Amount of discount                          0\n",
       "Amount of tip                               0\n",
       "Refunded amount                             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f'Unique delivery regions: {df[\"Delivery Region\"].unique()}')\n",
    "print(f'{len(df[df[\"Delivery Region\"]==\"None\"])} entries do not have region data')\n",
    "df[\"Delivery Region\"].replace({\"None\": np.nan}, inplace=True)\n",
    "print(\"\\n# Missing values for each column:\")\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problems: \n",
    "- 3 columns (\"Placed order with restaurant datetime,\" \"Delivery Region,\" and \"Driver at restaurant datetime\") are missing some data. This may impact analysis.\n",
    "\n",
    "#### Mitigation Options: Two solutions present themselves for the datetime data: \n",
    "1. Impute the data where appropriate using the average delivery time.\n",
    "    Deliver time can be inferred by comparing existing \"driver at restaurant\" and \"delivered to consumer\" times. This solution raises the below concerns.\n",
    "        a) We do not have geolocation data; some customers may be placing orders at restaurants far away. \n",
    "        b) We do not know what method the driver is using the deliver the food (e.g. bike, motorcycle, car)\n",
    "        c) We do not know the whether conditions on the given day, or other one-off events that cause atypical fluctuations in delivery times. \n",
    "2. Remove the columns. \n",
    "    When data quality is in question, it is oftentimes advisable to remove the questionable entries when it will not cause a material change to the dataset. In this case, 4500 rows represents almost 25% of all rows - removing this much data would cause a material change to data interpretation. \n",
    "        \n",
    "\n",
    "#### Solutions\n",
    "Per the above, I consider the uncertainty inherent in imputing values less of a concern than removing up 25% of data. \n",
    "1. For missing delivery region, I will impute using the most common delivery region (mode)\n",
    "2. For missing driver at restaurant values, I will infer the missing data using the average (median) delivery time. The median is resistent to outliers. As we are missing significant context when it comes to deliveries (restaurant delays, distance, etc), the mode is a safer reflection of the average delivery time than the mean.\n",
    "3. For order placed w/restaurant values, I will use a two pronged approach based on ASAP status:\n",
    "    1. If the \"IS ASAP\" flag is True: \n",
    "        a) If the number missing data is material: I will impute order using the \"order placed w/customer\" value\n",
    "        b) If the number missing data is not material: I will drop the rows from the relevant analysis\n",
    "    2. If the order is not ASAP: order-time would be delayed from order placed. As we do not know the time the customer has specified for delivery given current data, we will leave these blank and not analyze non-asap orders\n",
    "\n",
    "\n",
    "#### Assumptions\n",
    "##### 1. Customers' distance from restaurants are all similar\n",
    "##### 2. All drivers are using the same method of transportation (given the \"Driver\" denotation, likely a car)\n",
    "##### 3. Weather conditions are immaterial to delivery time. As the delivery regions specified typically have driving-favorable weather, this is a very reasonable assumption\n",
    "##### 4. We will not analyze non-asap orders per Solution 3.2 above\n",
    "\n",
    "Note: For a more detailed analysis, we could additionally factor in delivery region and time-of-day to impute average delivery time. As I am limited on time, I will go with the simple region approach denoted in "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ASAP status of orders missing order placement data: \n",
      "False    38\n",
      "True      2\n",
      "Name: Is ASAP, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Impute Delivery Region\n",
    "df[\"Delivery Region\"].fillna(df[\"Delivery Region\"].mode()[0], inplace=True)\n",
    "# Impute Driver at Restaurant\n",
    "#      Step 1: Create delivery Driver_time_elapsed column\n",
    "df[\"Driver_time_elapsed\"] = df[\"Delivered to consumer datetime\"] - df[\"Driver at restaurant datetime\"]\n",
    "#      Step 2: Create an integer column for feature engineering\n",
    "df[\"Driver at restaurant datetime\"] = df[\"Delivered to consumer datetime\"] - df[\"Driver_time_elapsed\"].median()\n",
    "# Impute order placed w/restaurants for ASAP deliveries\n",
    "print(f'ASAP status of orders missing order placement data: \\n{df[df[\"Placed order with restaurant datetime\"].isnull()][\"Is ASAP\"].value_counts()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only two values missing order placement time are ASAP orders; this number is immaterial to any analysis. I will drop them. Let's confirm all the null values are gone now. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer placed order datetime              0\n",
       "Placed order with restaurant datetime      40\n",
       "Driver at restaurant datetime               0\n",
       "Delivered to consumer datetime              0\n",
       "Driver ID                                   0\n",
       "Restaurant ID                               0\n",
       "Consumer ID                                 0\n",
       "Delivery Region                             0\n",
       "Is ASAP                                     0\n",
       "Order total                                 0\n",
       "Amount of discount                          0\n",
       "Amount of tip                               0\n",
       "Refunded amount                             0\n",
       "Driver_time_elapsed                      4531\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! All the null values are gone. I'll do a quick sanity check on the dataframe and make sure all the info still looks right after our transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Customer placed order datetime</th>\n",
       "      <th>Placed order with restaurant datetime</th>\n",
       "      <th>Driver at restaurant datetime</th>\n",
       "      <th>Delivered to consumer datetime</th>\n",
       "      <th>Driver ID</th>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th>Consumer ID</th>\n",
       "      <th>Delivery Region</th>\n",
       "      <th>Is ASAP</th>\n",
       "      <th>Order total</th>\n",
       "      <th>Amount of discount</th>\n",
       "      <th>Amount of tip</th>\n",
       "      <th>Refunded amount</th>\n",
       "      <th>Driver_time_elapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1899-12-31 18:59:12-07:53</td>\n",
       "      <td>1899-12-31 19:07:25-07:53</td>\n",
       "      <td>1899-12-31 19:19:58-07:53</td>\n",
       "      <td>1899-12-31 19:42:20-07:53</td>\n",
       "      <td>279</td>\n",
       "      <td>50</td>\n",
       "      <td>6738</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>True</td>\n",
       "      <td>16.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.0</td>\n",
       "      <td>00:27:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1900-01-13 08:05:57-07:53</td>\n",
       "      <td>1900-01-13 10:03:45-07:53</td>\n",
       "      <td>1900-01-13 10:41:39-07:53</td>\n",
       "      <td>1900-01-13 11:04:01-07:53</td>\n",
       "      <td>303</td>\n",
       "      <td>96</td>\n",
       "      <td>64746</td>\n",
       "      <td>Palo Alto</td>\n",
       "      <td>True</td>\n",
       "      <td>76.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>00:32:22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer placed order datetime Placed order with restaurant datetime  \\\n",
       "0      1899-12-31 18:59:12-07:53             1899-12-31 19:07:25-07:53   \n",
       "1      1900-01-13 08:05:57-07:53             1900-01-13 10:03:45-07:53   \n",
       "\n",
       "  Driver at restaurant datetime Delivered to consumer datetime  Driver ID  \\\n",
       "0     1899-12-31 19:19:58-07:53      1899-12-31 19:42:20-07:53        279   \n",
       "1     1900-01-13 10:41:39-07:53      1900-01-13 11:04:01-07:53        303   \n",
       "\n",
       "   Restaurant ID  Consumer ID Delivery Region  Is ASAP  Order total  \\\n",
       "0             50         6738   Mountain View     True        16.33   \n",
       "1             96        64746       Palo Alto     True        76.14   \n",
       "\n",
       "   Amount of discount  Amount of tip  Refunded amount Driver_time_elapsed  \n",
       "0                 0.0           0.82              0.0            00:27:11  \n",
       "1                 0.0           6.45              0.0            00:32:22  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure all the data looks correct. \n",
    "# QAing with the provided excel shows that the datetime conversion is a difference of 8 hours.\n",
    "# This which is correct if Daylight savings time is not active. \n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Everything's looking good. A quick sanity check on the timezones shows that the UTC to PT conversion clocks in at -8 hours, which fits if daylight savings time is not active (Pacific time is currently PST). \n",
    "\n",
    "#### Assumption: Pacific time is currently on PST (not PDT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration 1: Demand trends\n",
    "1. Can we get a better idea of what month this data is representing? \n",
    "2. Does food-delivery demand have any common trends?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1: Restrict analysis to operational month (PST, not UTC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def order_check(num, df):\n",
    "    return(f\"Total # of orders check: {num}/{len(df)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since orders can be placed in advance, monthly operational data can be best inferred by the date an order is delivered to a consumer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The largest day # in the dataset in PT is: 30\n",
      "of the 18078 entries provided, 17698 are in the operational month\n"
     ]
    }
   ],
   "source": [
    "#Dataframe contains month values given in UTC; some values \n",
    "#(e.g. 1st day of the month, 2AM UTC) would actually belong to the previous operational month in Pacific Time \n",
    "pst_df = df[df[\"Delivered to consumer datetime\"].dt.year != 1899]\n",
    "print (f'The largest day # in the dataset in PT is: {pst_df[\"Delivered to consumer datetime\"].dt.day.max()}')\n",
    "print (f\"of the {len(df)} entries provided, {len(pst_df)} are in the operational month\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total # of orders check: 17698/17698\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create pivot table with count of data entries per day ID\n",
    "day_quant_table = pst_df.pivot_table(index = pst_df[\"Delivered to consumer datetime\"].dt.day, aggfunc = \"count\")\n",
    "weekday_quant_table = pst_df.pivot_table(index = pst_df[\"Delivered to consumer datetime\"].dt.weekday, aggfunc = \"count\")\n",
    "\n",
    "#Confirm that sum of orders equals the total number of orders in the dataframe (18078)\n",
    "print(order_check(day_quant_table[\"Restaurant ID\"].sum(), pst_df))\n",
    "#Graph by day\n",
    "fig, axes = plt.subplots(1,2, figsize=(14,4))\n",
    "for table, ax in zip([day_quant_table, weekday_quant_table], axes):\n",
    "    ax.bar(table.index, table[\"Restaurant ID\"])\n",
    "axes[0].set(title=\"Orders by day of month\", xlabel=\"Day of Month\", ylabel=\"# Orders per day\");\n",
    "axes[1].set(title=\"Orders by day of week (relative weekday number)\", xlabel=\"Day of Week\", ylabel=\"Total monthly orders\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Day 30 in this example may be underrepresented by design, as our extract is pulled on UTC data. Thus, in the same way that this month's extract included some of last month's data, at least someof the Day 30 operational data for this month would be included in the next month's data, as UTC is ahead of PST. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Takeaways / Trends\n",
    "Using the knowledge that the largest day contained in the date columns is 30, we know that we are operating in a month with 31 days. Thus, we know this data comes from either September, April, June, and November. \n",
    "\n",
    "The data also seems to follow two patterns a 7-day basis:\n",
    "    1. Starting with Day #4, every 7th day appears to see a relative spike in orders\n",
    "    2. Starting with Day #7, every 7th day represents a relative trough in order demand\n",
    "    \n",
    "\n",
    "# Demand trends require further exploration\n",
    "We have a clear relative trend of order volume by relative weekday, but what month and year is this data exerpt taken from? Knowing the time will provide clarity as to which weekdays are most valuable. \n",
    "- We know that this data comes from either Sept, April, June, or November\n",
    "    \n",
    "- The data is exposing a clear demand trend that seems to hold for almost the entire duration of the data provided. Knowing which month would allow us to determine which weekdays the Client sees peak demand and likewise, which weekdays demand suffers. Knowing what month this data is from would also allow us to cross-reference our demand data with likely confounding variables, such as holidays. For example, the massive decline in demand seen in Days 20 and 30 could be explained by national holidays  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration 2: Delivery Trends\n",
    "1. Are there any significant lead times when communicicating Client orders to partner restaurants?\n",
    "    - Are certain restaurants suffering from disproportionately long lead-times? \n",
    "2. Do monetary KPIs differ by delivery region?\n",
    "    - Create the same time-series as #2, grouped by Delivery Region\n",
    "3. Do certain consumers out-refund others? Why?\n",
    "    - Group monetary kpis by consumer ID and look for outliers \n",
    "4. (Time-permitting) Do certain drivers outperform others?\n",
    "    - Group monetary kpis by driver ID and look for outliers\n",
    "5. (Time-permitting) Do certain restaurants stand out (for better or for worse?)\n",
    "    - Group monetary KPIs by restaurant.\n",
    "        - Do discounts correspond to increased order volume?\n",
    "        - Do some restaurants account for a larger share of order totals?\n",
    "        - Are some restaurants more likely to be associated with refunded orders?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Restaurant Order lead-times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     14104\n",
       "mean     0 days 00:04:28.898468\n",
       "std      0 days 06:03:04.639094\n",
       "min          -30 days +04:56:24\n",
       "25%             0 days 00:01:17\n",
       "50%             0 days 00:03:14\n",
       "75%             0 days 00:09:09\n",
       "max             2 days 19:59:58\n",
       "Name: order_lt, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pst_df[\"order_lt\"] = pst_df[\"Placed order with restaurant datetime\"] - pst_df[\"Customer placed order datetime\"]\n",
    "pst_df[\"order_lt_min\"] = pst_df[\"order_lt\"].dt.seconds/60\n",
    "#order lead times are only relevant for orders placed for immediate delivery; \n",
    "# the current dataset does not have data to analyze orders placed for delayed delivery\n",
    "pst_df[pst_df[\"Is ASAP\"]][\"order_lt\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there may be some data integrity issues - some orders seem to have placed an order with the restaurant significantly before the customer even placed the order. I'll drop all the values that have orders placed with restaurants before the customer places their order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     14103\n",
       "mean     0 days 00:07:31.447209\n",
       "std      0 days 00:35:37.410277\n",
       "min             0 days 00:00:06\n",
       "25%             0 days 00:01:17\n",
       "50%             0 days 00:03:14\n",
       "75%             0 days 00:09:09\n",
       "max             2 days 19:59:58\n",
       "Name: order_lt, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_df = pst_df.drop(pst_df[pst_df[\"Placed order with restaurant datetime\"] < pst_df[\"Customer placed order datetime\"]].index)\n",
    "time_df[time_df[\"Is ASAP\"]][\"order_lt\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks better! Immediate takeaways are that some orders are placed almost immediately (6 seconds!), though over 75% of all orders take over a minute to place with the restaurant. If we can partner more closely with our partner restaurants and integrate their order queue directly to the Client's App, we can reduce order delays to almost 0! \n",
    "\n",
    "Let's see if there are any other specific insights we can glean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# orders placed in each region:\n",
      "Palo Alto        11218\n",
      "Mountain View     3668\n",
      "San Jose          2801\n",
      "Name: Delivery Region, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,2, figsize = (14,4))\n",
    "\n",
    "chart_regions = time_df[time_df[\"Is ASAP\"]].groupby(\"Delivery Region\").mean()[\"order_lt_min\"]\n",
    "chart_restaurants = time_df[time_df[\"Is ASAP\"]].groupby(\"Restaurant ID\").mean()[\"order_lt_min\"]\n",
    "titles = [\"Average delay placing order at restaurant, by region\", \"Average delay receiving Client order, by partner restaurant\"]\n",
    "print(f\"# orders placed in each region:\\n{time_df['Delivery Region'].value_counts()}\")\n",
    "\n",
    "for chart, ax, title in zip([chart_regions, chart_restaurants], axes, titles):\n",
    "    ax.bar(chart.index, chart)\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regions: Palo Alto seem to have the lowest average delivery time, but it's still over 6 minutes. As the Client already has significant market penetration in Palo Alto, it should prioritize developing relationships in Mountain View and San Jose to continue to improve customer and partner experience and further penetrate these markets\n",
    "\n",
    "Restaurants: Most restaurants have around a 10 minute delay, but a handful have scary high delays in receiving Client orders. Let's identify what restaurants those are, and work with them to fix whatever issue is causing these massive delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 78 restaurants with average order lead times > 10 min.\n",
      "They received a total of 2866 orders\n",
      "\n",
      "These 10 retaurants account for 1701 of the 2866 orders above:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># orders received</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Restaurant ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>194</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>224</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>264</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>239</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               # orders received\n",
       "Restaurant ID                   \n",
       "9                            710\n",
       "5                            187\n",
       "194                          184\n",
       "30                           156\n",
       "221                           96\n",
       "90                            89\n",
       "224                           75\n",
       "264                           71\n",
       "239                           68\n",
       "109                           65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print (f\"There are {len(chart_restaurants[chart_restaurants > 10])} restaurants with average order lead times > 10 min.\")\n",
    "print(f\"They received a total of {pst_df['Restaurant ID'].isin(chart_restaurants[chart_restaurants > 10].index).sum()} orders\")\n",
    "\n",
    "#Identify high-lead time restaurants with the highest volume of orders       \n",
    "big_delays = pst_df[pst_df[\"Restaurant ID\"].isin(chart_restaurants[chart_restaurants > 10].index)].\\\n",
    "pivot_table(index=\"Restaurant ID\", values = \"Consumer ID\", aggfunc=\"count\").\\\n",
    "sort_values(\"Consumer ID\", ascending=False).rename({\"Consumer ID\": \"# orders received\"},axis=1).head(10)\n",
    "      \n",
    "print(f\"\\nThese 10 retaurants account for {big_delays['# orders received'].sum()} of the 2866 orders above:\")\n",
    "display(big_delays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Action Item: \n",
    "Work with the above 10 restaurants and determine ways to best reduce delays from the time a customer places an order with  the Client to the time the partner restaurant receives it. I hypothesize the best way to do this is to integrate the partner restaurant's order queue with the Client's order API, as this would cause orders to be placed with the partner restaurant instantaneously. However, more research is required. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Monetary KPIs - by Delivery Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Palo Alto        11226\n",
       "Mountain View     3670\n",
       "San Jose          2802\n",
       "Name: Delivery Region, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Review how many orders were placed in each region\n",
    "pst_df[\"Delivery Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pst_df[\"tip % of order\"] = pst_df[\"Amount of tip\"] / pst_df[\"Order total\"] * 100\n",
    "money_kpis = [\"Order total\", \"tip % of order\", \"Refunded amount\", \"Amount of discount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,4, figsize=(14,4))\n",
    "legend_order = []\n",
    "for region in pst_df[\"Delivery Region\"].unique():\n",
    "    for kpi, ax in zip(money_kpis, axes):\n",
    "        temp = pst_df[pst_df[\"Delivery Region\"]==region]\n",
    "        ax.plot(temp.pivot_table(index=temp[\"Delivered to consumer datetime\"].dt.day, values=kpi, aggfunc=\"mean\"), alpha=.5)\n",
    "        ax.set(ylabel=\"Average $USD (mean)\")\n",
    "        ax.set(xlabel=\"Day of Month\")\n",
    "        ax.set(title=f\"Month - {kpi}\")\n",
    "        plt.tight_layout()\n",
    "    legend_order.append(region)\n",
    "ax.legend(legend_order, loc=\"upper right\");\n",
    "\n",
    "fig, axes = plt.subplots(1,4, figsize=(14,4))\n",
    "legend_order = []\n",
    "for region in pst_df[\"Delivery Region\"].unique():\n",
    "    for kpi, ax in zip(money_kpis, axes):\n",
    "        temp = pst_df[pst_df[\"Delivery Region\"]==region]\n",
    "        ax.plot(temp.pivot_table(index=temp[\"Delivered to consumer datetime\"].dt.weekday, values=kpi, aggfunc=\"mean\"), alpha=.5)\n",
    "        ax.set(ylabel=\"Average $USD (mean)\")\n",
    "        ax.set(xlabel=\"Weekday (relative number)\")\n",
    "        ax.set(title=f\"Weekday - {kpi}\")\n",
    "        plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Action Items\n",
    "- Focus growth into San Jose - the community seems to tip drivers with a a much higher % of the order total than consumers in other communities\n",
    "\n",
    "- Reevaluate discount frequency in Palo Alto and Mountain View areas. Palo Alto has almost 300% of Mountain View's order volume, but has higher average discounts almost every day. Either increase discount frequency in Mountain View to increase product update, or decrease discount frequency in Palo Alto if it is determined that market has reached maturity\n",
    "\n",
    "- Explore alternative delivery fee structures for different areas based on average order size. Assuming current delivery fees are a constant factor of order price and delivery time, the Client may want to consider region-specific policy to normalize income across different cost-of-living zones. Orders placed in San Jose are $10+ cheaper than those placed in Mountain View and Palo Alto. \n",
    "\n",
    "Note: Tips in San Jose may be a higher % of cost due to having extra money due to the cheaper meals. Before changing delivery fee structures, an elasticity exercise should be conducted. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Refunds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find all entries where customers asked for a refund\n",
    "refund_df = pst_df[pst_df[\"Refunded amount\"] > 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "486 refunds were issued to 436 unique customers\n",
      "The customers who refunded more than two orders this month are as follows: \n"
     ]
    },
    {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total Orders  Refunded Orders   Refund %\n",
       "Consumer ID                                          \n",
       "91848                   9                3  33.333333\n",
       "12420                  10                3  30.000000\n",
       "959                    11                3  27.272727\n",
       "2999                   14                3  21.428571\n",
       "96518                  14                3  21.428571\n",
       "5673                   21                4  19.047619\n",
       "14296                  20                3  15.000000\n",
       "10294                  21                3  14.285714\n",
       "929                    50                5  10.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"{len(refund_df)} refunds were issued to {refund_df['Consumer ID'].nunique()} unique customers\")\n",
    "print(\"The customers who refunded more than two orders this month are as follows: \")\n",
    "all_refunds = refund_df[\"Consumer ID\"].value_counts()\n",
    "#identify people who may be abusing the system by logging more than 2 refunds in a month\n",
    "potential_abusers = all_refunds[all_refunds > 2]\n",
    "#cross reference potential abusers with the total number of orders placed\n",
    "abuse_check = pst_df[pst_df[\"Consumer ID\"].isin(potential_abusers.index)].pivot_table(index=\"Consumer ID\", \n",
    "                                                                                      values=\"Refunded amount\", \n",
    "                                                                                      aggfunc=\"count\")\n",
    "abuse_check = abuse_check.join(potential_abusers)\n",
    "abuse_check.rename({\"Refunded amount\": \"Total Orders\", \"Consumer ID\": \"Refunded Orders\"}, axis=1, inplace=True)\n",
    "abuse_check[\"Refund %\"] = abuse_check[\"Refunded Orders\"] / abuse_check[\"Total Orders\"] * 100\n",
    "abuse_check.sort_values(by=\"Refund %\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of those who made more than 2 refunds in the month, the numbers are within the realm of reason - although the Client may to investigate whether customers with high refund %s are legitimate. Speaking of, let's take a quick gander at all refunds and see what the deal is with those who placed 2 or less. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refunders with highest average delivery time (of refunded items)\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "             Total Orders  Refunded Orders  \\\n",
       "Consumer ID                                  \n",
       "11055                  19                1   \n",
       "27808                   1                1   \n",
       "80030                  12                1   \n",
       "63544                   2                1   \n",
       "3293                    4                1   \n",
       "83931                   1                1   \n",
       "27329                   3                1   \n",
       "12891                   8                1   \n",
       "1103                   22                1   \n",
       "10990                   8                2   \n",
       "\n",
       "             Avg Delivery time of refunds (min)  Refund frequency  \\\n",
       "Consumer ID                                                         \n",
       "11055                                 91.416667          5.263158   \n",
       "27808                                 70.966667        100.000000   \n",
       "80030                                 69.050000          8.333333   \n",
       "63544                                 68.783333         50.000000   \n",
       "3293                                  67.483333         25.000000   \n",
       "83931                                 65.983333        100.000000   \n",
       "27329                                 62.916667         33.333333   \n",
       "12891                                 62.533333         12.500000   \n",
       "1103                                  60.950000          4.545455   \n",
       "10990                                 59.950000         25.000000   \n",
       "\n",
       "             Refund % of cost  \n",
       "Consumer ID                    \n",
       "11055                1.241308  \n",
       "27808               17.917936  \n",
       "80030               10.479329  \n",
       "63544               82.104679  \n",
       "3293                38.199823  \n",
       "83931               93.872191  \n",
       "27329               33.677616  \n",
       "12891               13.962405  \n",
       "1103                 5.995870  \n",
       "10990                7.116392  "
      ]
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refunders with lowest average delivery time (of refunded items)\n"
     ]
    },
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      "text/plain": [
       "             Total Orders  Refunded Orders  \\\n",
       "Consumer ID                                  \n",
       "14241                   3                1   \n",
       "111695                  1                1   \n",
       "13592                   1                1   \n",
       "459                    17                2   \n",
       "2186                    6                1   \n",
       "111584                  2                1   \n",
       "14188                   3                1   \n",
       "9938                    3                1   \n",
       "9267                    2                1   \n",
       "4087                   38                2   \n",
       "\n",
       "             Avg Delivery time of refunds (min)  Refund frequency  \\\n",
       "Consumer ID                                                         \n",
       "14241                                  5.533333         33.333333   \n",
       "111695                                 6.833333        100.000000   \n",
       "13592                                  7.216667        100.000000   \n",
       "459                                    7.666667         11.764706   \n",
       "2186                                   8.800000         16.666667   \n",
       "111584                                 8.866667         50.000000   \n",
       "14188                                  9.500000         33.333333   \n",
       "9938                                   9.666667         33.333333   \n",
       "9267                                   9.950000         50.000000   \n",
       "4087                                  10.116667          5.263158   \n",
       "\n",
       "             Refund % of cost  \n",
       "Consumer ID                    \n",
       "14241                6.253155  \n",
       "111695              88.310929  \n",
       "13592               17.637828  \n",
       "459                  2.586442  \n",
       "2186                 4.124251  \n",
       "111584              11.977292  \n",
       "14188               12.055160  \n",
       "9938                 5.880594  \n",
       "9267                12.549571  \n",
       "4087                 4.060977  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "refund_df[\"Delivery_Minutes\"] = refund_df[\"Driver_time_elapsed\"].dt.seconds/60\n",
    "abuse_check2 = pst_df[pst_df[\"Consumer ID\"].isin(all_refunds.index)].pivot_table(index=\"Consumer ID\", \n",
    "                                                                                      values=\"Refunded amount\", \n",
    "                                                                                      aggfunc=\"count\")\n",
    "abuse_check2 = abuse_check2.join(all_refunds)\n",
    "# Rename columns to approrpriate names\n",
    "abuse_check2.rename({\"Refunded amount\": \"Total Orders\", \"Consumer ID\": \"Refunded Orders\"}, axis=1, inplace=True)\n",
    "\n",
    "# Check how many accounts are refunding 100% of their orders\n",
    "abuse_check2 = abuse_check2.join(pst_df.groupby(\"Consumer ID\")[\"Order total\", \"Refunded amount\"].sum())\n",
    "abuse_check2 = abuse_check2.join(refund_df.groupby(\"Consumer ID\")[\"Delivery_Minutes\"].mean())\n",
    "abuse_check2.rename({\"Refunded amount\": \"Sum of refunds\", \"Order total\": \"Sum of order totals\", \"Delivery_Minutes\":\"Avg Delivery time of refunds (min)\"}, axis=1, inplace=True)\n",
    "\n",
    "abuse_check2[\"Refund frequency\"] = abuse_check2[\"Refunded Orders\"] / abuse_check2[\"Total Orders\"] * 100\n",
    "abuse_check2[\"Refund % of cost\"] = abuse_check2[\"Sum of refunds\"] / abuse_check2[\"Sum of order totals\"] * 100\n",
    "# Sort accounts/refunds by longest Driver_time_elapsed\n",
    "\n",
    "\n",
    "print(\"Refunders with highest average delivery time (of refunded items)\")\n",
    "display(abuse_check2.drop([\"Sum of order totals\", \"Sum of refunds\"], axis=1)\\\n",
    "        .sort_values(\"Avg Delivery time of refunds (min)\", ascending=False).head(10))\n",
    "print(\"Refunders with lowest average delivery time (of refunded items)\")\n",
    "display(abuse_check2.drop([\"Sum of order totals\", \"Sum of refunds\"], axis=1)\\\n",
    "        .sort_values([\"Avg Delivery time of refunds (min)\"], ascending=True).head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The customers at the top end of delivery times are all refunding orders that took over 50 minutes to arrive - likely legit. But it does look like at least one of these accounts has a refund frequency of 100. Let's see how common that is"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Total Orders</th>\n",
       "      <th>Avg Delivery time of refunds (min)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>86.0</td>\n",
       "      <td>62.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.814516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.861363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.691667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.0</td>\n",
       "      <td>34.441667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Orders  Avg Delivery time of refunds (min)\n",
       "count          86.0                           62.000000\n",
       "mean            1.0                           28.814516\n",
       "std             0.0                           13.861363\n",
       "min             1.0                            6.833333\n",
       "25%             1.0                           19.083333\n",
       "50%             1.0                           25.691667\n",
       "75%             1.0                           34.441667\n",
       "max             1.0                           70.966667"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abuse_check2[abuse_check2[\"Refund frequency\"] == 100].drop(\"Refund % of cost\", axis=1).describe()[[\"Total Orders\", \"Avg Delivery time of refunds (min)\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further Analysis Needed\n",
    "Looking at 100% refunded accounts, it isn't surprising that all of these accounts only placed 1 order. Nor is it surprising that some of these refunds were issued for long delivery times - 70 minutes is a lot! \n",
    "\n",
    "What is surprising is that 75% of these refunds were issued for food deliveries that took less than 34 minutes. Given the traffic in the Bay area, 35 minutes is not altogether unreasonable. Did these consumers receive the wrong order? Did they have an unpleasant experience? Were they just abusing the app? We can't know with this dataset, but it deserves further exploration in the future. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "1. Work with the 10 identified restaurants and reduce the delay in order placement leadtime\n",
    "2. Reevaluate discount frequency in Palo Alto / Mountain View\n",
    "3. Focus growth in San Jose to increase driver tips (and therefore, employee satisfaction)\n",
    "4. Investigate alternative fee structures based on region characteristics\n",
    "5. Explore why customers are refunding quick deliveries (qualitative analysis, surveys)\n",
    "6. Conduct competitive analysis and evaluate how metrics stack compared to competitors such as DoorDash/Uber Eats/Grubhub\n",
    "7. Capture more data per order (e.g. reason for refund, intended delivery time for non-ASAP orders) and create machine learning models to determine which features result in larger tips "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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