notebooks/section06_midcourse_project.ipynb

{
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   "metadata": {},
   "source": [
    "# Mid-Course Project\n",
    "\n",
    "Hi There, and thanks for your help. If you're reading this you've been selected to help on a secret initiative.\n",
    "\n",
    "You will be helping us analyze a portion of data from a company we want to acquire, which could greatly improve the fortunes of Maven Mega Mart.\n",
    "\n",
    "We'll be working with `project_transactions.csv` and briefly take a look at `product.csv`.\n",
    "\n",
    "First, read in the transactions data and explore it.\n",
    "\n",
    "* Take a look at the raw data, the datatypes, and cast `DAY`, `QUANTITY`, `STORE_ID`, and `WEEK_NO` columns to the smallest appropriate datatype. Check the memory reduction by doing so.\n",
    "* Is there any missing data?\n",
    "* How many unique households and products are there in the data? The fields household_key and Product_ID will help here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "048c011c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "89cc0170",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2146311 entries, 0 to 2146310\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Dtype  \n",
      "---  ------             -----  \n",
      " 0   household_key      int64  \n",
      " 1   BASKET_ID          int64  \n",
      " 2   DAY                int64  \n",
      " 3   PRODUCT_ID         int64  \n",
      " 4   QUANTITY           int64  \n",
      " 5   SALES_VALUE        float64\n",
      " 6   STORE_ID           int64  \n",
      " 7   RETAIL_DISC        float64\n",
      " 8   WEEK_NO            int64  \n",
      " 9   COUPON_DISC        float64\n",
      " 10  COUPON_MATCH_DISC  float64\n",
      "dtypes: float64(4), int64(7)\n",
      "memory usage: 180.1 MB\n"
     ]
    }
   ],
   "source": [
    "transactions = pd.read_csv(\"../project_data/project_transactions.csv\")\n",
    "\n",
    "transactions.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d9a39bf",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>household_key</th>\n",
       "      <th>BASKET_ID</th>\n",
       "      <th>DAY</th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>QUANTITY</th>\n",
       "      <th>SALES_VALUE</th>\n",
       "      <th>STORE_ID</th>\n",
       "      <th>RETAIL_DISC</th>\n",
       "      <th>WEEK_NO</th>\n",
       "      <th>COUPON_DISC</th>\n",
       "      <th>COUPON_MATCH_DISC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1364</td>\n",
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       "      <td>1</td>\n",
       "      <td>842930</td>\n",
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       "      <td>31742</td>\n",
       "      <td>-0.40</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>920955</td>\n",
       "      <td>1</td>\n",
       "      <td>3.09</td>\n",
       "      <td>31742</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>937406</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>31742</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1364</td>\n",
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       "      <td>1</td>\n",
       "      <td>981760</td>\n",
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       "      <td>0.60</td>\n",
       "      <td>31742</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   household_key    BASKET_ID  DAY  PRODUCT_ID  QUANTITY  SALES_VALUE  \\\n",
       "0           1364  26984896261    1      842930         1         2.19   \n",
       "1           1364  26984896261    1      897044         1         2.99   \n",
       "2           1364  26984896261    1      920955         1         3.09   \n",
       "3           1364  26984896261    1      937406         1         2.50   \n",
       "4           1364  26984896261    1      981760         1         0.60   \n",
       "\n",
       "   STORE_ID  RETAIL_DISC  WEEK_NO  COUPON_DISC  COUPON_MATCH_DISC  \n",
       "0     31742         0.00        1          0.0                0.0  \n",
       "1     31742        -0.40        1          0.0                0.0  \n",
       "2     31742         0.00        1          0.0                0.0  \n",
       "3     31742        -0.99        1          0.0                0.0  \n",
       "4     31742        -0.79        1          0.0                0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22fb8381",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>household_key</th>\n",
       "      <th>BASKET_ID</th>\n",
       "      <th>DAY</th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>QUANTITY</th>\n",
       "      <th>SALES_VALUE</th>\n",
       "      <th>STORE_ID</th>\n",
       "      <th>RETAIL_DISC</th>\n",
       "      <th>WEEK_NO</th>\n",
       "      <th>COUPON_DISC</th>\n",
       "      <th>COUPON_MATCH_DISC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2146311.0</td>\n",
       "      <td>2.146311e+06</td>\n",
       "      <td>2146311.0</td>\n",
       "      <td>2146311.0</td>\n",
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       "      <td>2146311.0</td>\n",
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       "      <td>2146311.0</td>\n",
       "      <td>2146311.0</td>\n",
       "      <td>2146311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1056.0</td>\n",
       "      <td>3.404897e+10</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2884715.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3268.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>605.0</td>\n",
       "      <td>4.723748e+09</td>\n",
       "      <td>190.0</td>\n",
       "      <td>3831949.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9122.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.698490e+10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25671.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-130.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-56.0</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>548.0</td>\n",
       "      <td>3.040798e+10</td>\n",
       "      <td>229.0</td>\n",
       "      <td>917231.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1042.0</td>\n",
       "      <td>3.281176e+10</td>\n",
       "      <td>392.0</td>\n",
       "      <td>1027960.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1581.0</td>\n",
       "      <td>4.012804e+10</td>\n",
       "      <td>555.0</td>\n",
       "      <td>1132771.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2099.0</td>\n",
       "      <td>4.230536e+10</td>\n",
       "      <td>711.0</td>\n",
       "      <td>18316298.0</td>\n",
       "      <td>89638.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>34280.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "       household_key     BASKET_ID        DAY  PRODUCT_ID   QUANTITY  \\\n",
       "count      2146311.0  2.146311e+06  2146311.0   2146311.0  2146311.0   \n",
       "mean          1056.0  3.404897e+10      390.0   2884715.0      101.0   \n",
       "std            605.0  4.723748e+09      190.0   3831949.0     1152.0   \n",
       "min              1.0  2.698490e+10        1.0     25671.0        0.0   \n",
       "25%            548.0  3.040798e+10      229.0    917231.0        1.0   \n",
       "50%           1042.0  3.281176e+10      392.0   1027960.0        1.0   \n",
       "75%           1581.0  4.012804e+10      555.0   1132771.0        1.0   \n",
       "max           2099.0  4.230536e+10      711.0  18316298.0    89638.0   \n",
       "\n",
       "       SALES_VALUE   STORE_ID  RETAIL_DISC    WEEK_NO  COUPON_DISC  \\\n",
       "count    2146311.0  2146311.0    2146311.0  2146311.0    2146311.0   \n",
       "mean           3.0     3268.0         -1.0       56.0         -0.0   \n",
       "std            4.0     9122.0          1.0       27.0          0.0   \n",
       "min            0.0        1.0       -130.0        1.0        -56.0   \n",
       "25%            1.0      330.0         -1.0       33.0          0.0   \n",
       "50%            2.0      372.0          0.0       57.0          0.0   \n",
       "75%            3.0      422.0          0.0       80.0          0.0   \n",
       "max          840.0    34280.0          4.0      102.0          0.0   \n",
       "\n",
       "       COUPON_MATCH_DISC  \n",
       "count          2146311.0  \n",
       "mean                -0.0  \n",
       "std                  0.0  \n",
       "min                 -8.0  \n",
       "25%                  0.0  \n",
       "50%                  0.0  \n",
       "75%                  0.0  \n",
       "max                  0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use to identify which values can be downcast\n",
    "\n",
    "transactions.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c019ac8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "transactions = pd.read_csv(\"../project_data/project_transactions.csv\",\n",
    "                          dtype={\"DAY\": \"Int16\",\n",
    "                                 \"QUANTITY\": \"Int32\",\n",
    "                                 \"STORE_ID\": \"Int32\",\n",
    "                                 \"WEEK_NO\": \"Int8\",}\n",
    "                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c10e5f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2146311 entries, 0 to 2146310\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Dtype  \n",
      "---  ------             -----  \n",
      " 0   household_key      int64  \n",
      " 1   BASKET_ID          int64  \n",
      " 2   DAY                Int16  \n",
      " 3   PRODUCT_ID         int64  \n",
      " 4   QUANTITY           Int32  \n",
      " 5   SALES_VALUE        float64\n",
      " 6   STORE_ID           Int32  \n",
      " 7   RETAIL_DISC        float64\n",
      " 8   WEEK_NO            Int8   \n",
      " 9   COUPON_DISC        float64\n",
      " 10  COUPON_MATCH_DISC  float64\n",
      "dtypes: Int16(1), Int32(2), Int8(1), float64(4), int64(3)\n",
      "memory usage: 145.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Reduced memory usage by ~35MB after converting to correct dtypes\n",
    "\n",
    "transactions.info(memory_usage=\"deep\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6fdf78f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "household_key        0\n",
       "BASKET_ID            0\n",
       "DAY                  0\n",
       "PRODUCT_ID           0\n",
       "QUANTITY             0\n",
       "SALES_VALUE          0\n",
       "STORE_ID             0\n",
       "RETAIL_DISC          0\n",
       "WEEK_NO              0\n",
       "COUPON_DISC          0\n",
       "COUPON_MATCH_DISC    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing value counts\n",
    "\n",
    "transactions.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3995fbe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2099"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate unique households in dataset with nunique (describe could also be used)\n",
    "\n",
    "transactions[\"household_key\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e363ef5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84138"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate unique product_ids in dataset with nunique\n",
    "\n",
    "transactions[\"PRODUCT_ID\"].nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "42983a48",
   "metadata": {},
   "source": [
    "## Column Creation\n",
    "\n",
    "Create two columns:\n",
    "\n",
    "* A column that captures the `total_discount` by row (sum of `RETAIL_DISC`, `COUPON_DISC`)\n",
    "* The percentage disount (`total_discount` / `SALES_VALUE`). Make sure this is positive (try `.abs()`).\n",
    "* If the percentage discount is greater than 1, set it equal to 1. If it is less than 0, set it to 0. \n",
    "* Drop the individual discount columns (`RETAIL_DISC`, `COUPON_DISC`, `COUPON_MATCH_DISC`).\n",
    "\n",
    "Feel free to overwrite the existing transaction DataFrame after making the modifications above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e0af83c4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>household_key</th>\n",
       "      <th>BASKET_ID</th>\n",
       "      <th>DAY</th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>QUANTITY</th>\n",
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       "      <th>STORE_ID</th>\n",
       "      <th>WEEK_NO</th>\n",
       "      <th>total_discount</th>\n",
       "      <th>percentage_discount</th>\n",
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       "      <th>0</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>842930</td>\n",
       "      <td>1</td>\n",
       "      <td>2.19</td>\n",
       "      <td>31742</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>897044</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>31742</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>0.133779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>920955</td>\n",
       "      <td>1</td>\n",
       "      <td>3.09</td>\n",
       "      <td>31742</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>937406</td>\n",
       "      <td>1</td>\n",
       "      <td>2.50</td>\n",
       "      <td>31742</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>0.396000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1364</td>\n",
       "      <td>26984896261</td>\n",
       "      <td>1</td>\n",
       "      <td>981760</td>\n",
       "      <td>1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>31742</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   household_key    BASKET_ID  DAY  PRODUCT_ID  QUANTITY  SALES_VALUE  \\\n",
       "0           1364  26984896261    1      842930         1         2.19   \n",
       "1           1364  26984896261    1      897044         1         2.99   \n",
       "2           1364  26984896261    1      920955         1         3.09   \n",
       "3           1364  26984896261    1      937406         1         2.50   \n",
       "4           1364  26984896261    1      981760         1         0.60   \n",
       "\n",
       "   STORE_ID  WEEK_NO  total_discount  percentage_discount  \n",
       "0     31742        1            0.00             0.000000  \n",
       "1     31742        1           -0.40             0.133779  \n",
       "2     31742        1            0.00             0.000000  \n",
       "3     31742        1           -0.99             0.396000  \n",
       "4     31742        1           -0.79             1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a discount sum column and a percentage discount column\n",
    "transactions = (\n",
    "    transactions\n",
    "    .assign(total_discount = transactions[\"RETAIL_DISC\"] + transactions[\"COUPON_DISC\"],\n",
    "            percentage_discount = (lambda x: (x[\"total_discount\"] / x[\"SALES_VALUE\"]).abs()))\n",
    "    .drop([\"RETAIL_DISC\", \"COUPON_DISC\", \"COUPON_MATCH_DISC\"], axis=1))\n",
    "\n",
    "\n",
    "# Use where to cap values above 1 at 1 and below 0 at 0. Other methods could be used here as well.\n",
    "transactions[\"percentage_discount\"] = (transactions[\"percentage_discount\"]\n",
    "                                       .where(transactions[\"percentage_discount\"] < 1, 1.0)\n",
    "                                       .where(transactions[\"percentage_discount\"] > 0, 0)\n",
    "                                      )\n",
    "\n",
    "transactions.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7194e351",
   "metadata": {},
   "source": [
    "## Overall Statistics\n",
    "\n",
    "Calculate:\n",
    "\n",
    "* The total sales (sum of `SALES_VALUE`), \n",
    "* Total discount (sum of `total_discount`)\n",
    "* Overall percentage discount (sum of total_discount / sum of sales value)\n",
    "* Total quantity sold (sum of `QUANTITY`).\n",
    "* Max quantity sold in a single row. Inspect the row as well. Does this have a high discount percentage?\n",
    "* Total sales value per basket (sum of sales value / nunique basket_id).\n",
    "* Total sales value per household (sum of sales value / nunique household_key). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b828816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6666243.499999999"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Sales Value\n",
    "\n",
    "transactions['SALES_VALUE'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e041e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1178658.0799999998"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Discount\n",
    "\n",
    "transactions['total_discount'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2250c8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1768099350106248"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall Percent Discount\n",
    "\n",
    "transactions['total_discount'].sum() / transactions['SALES_VALUE'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bba26a36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2073244407398103"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average of pct_discount column\n",
    "\n",
    "transactions[\"percentage_discount\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "301729f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216713611"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Quantity Sold\n",
    "\n",
    "transactions['QUANTITY'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "082c31a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89638"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Max quantity in single row\n",
    "\n",
    "transactions[\"QUANTITY\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a65cf90a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "household_key                  630.0\n",
       "BASKET_ID              34749153595.0\n",
       "DAY                            503.0\n",
       "PRODUCT_ID                 6534178.0\n",
       "QUANTITY                     89638.0\n",
       "SALES_VALUE                    250.0\n",
       "STORE_ID                       384.0\n",
       "WEEK_NO                         73.0\n",
       "total_discount                -13.45\n",
       "percentage_discount           0.0538\n",
       "Name: 1442095, dtype: Float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use to grab row with max value - discount rate is lower than average\n",
    "\n",
    "transactions.loc[transactions[\"QUANTITY\"].argmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "032377b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.61797938516092"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sales value per transaction/basket\n",
    "\n",
    "transactions['SALES_VALUE'].sum() / transactions['BASKET_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6e35594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3175.9140066698424"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sales value per household\n",
    "\n",
    "transactions['SALES_VALUE'].sum() / transactions['household_key'].nunique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "89016a20",
   "metadata": {},
   "source": [
    "## Household Analysis\n",
    "\n",
    "* Plot the distribution of total sales value purchased at the household level. \n",
    "* What were the top 10 households by quantity purchased?\n",
    "* What were the top 10 households by sales value?\n",
    "* Plot the total sales value for our top 10 households by value, ordered from highest to lowest.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c3e70a6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='Frequency'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of households by total sales value\n",
    "# First groupby household and calculate sum of sales\n",
    "# then plot with a histogram\n",
    "\n",
    "(transactions\n",
    " .groupby(\"household_key\")\n",
    " .agg({'SALES_VALUE': 'sum'})\n",
    " .plot.hist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92654f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store top 10 households by total value and quantity\n",
    "# groupby household_key, calculate sum of relevant columns by household\n",
    "# sort both by relevant metric in descending order, and grab top 10 rows\n",
    "\n",
    "top10_value = (transactions\n",
    " .groupby(\"household_key\")\n",
    " .agg({'SALES_VALUE': 'sum'})\n",
    " .sort_values(\"SALES_VALUE\", ascending=False)\n",
    " .iloc[:10])\n",
    "\n",
    "top10_quant = (transactions\n",
    " .groupby(\"household_key\")\n",
    " .agg({\"QUANTITY\": \"sum\"})\n",
    " .sort_values(\"QUANTITY\", ascending=False)\n",
    " .iloc[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3a8e4d39",
   "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>SALES_VALUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>38319.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>27859.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1453</th>\n",
       "      <td>21661.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>20352.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>19299.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>19194.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1653</th>\n",
       "      <td>19153.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>18894.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>18790.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>18494.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SALES_VALUE\n",
       "household_key             \n",
       "1023              38319.79\n",
       "1609              27859.68\n",
       "1453              21661.29\n",
       "1430              20352.99\n",
       "718               19299.86\n",
       "707               19194.42\n",
       "1653              19153.75\n",
       "1111              18894.72\n",
       "982               18790.34\n",
       "400               18494.14"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "27541797",
   "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>QUANTITY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household_key</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>4479917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>755</th>\n",
       "      <td>3141769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>2146715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1863829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>1741892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1527</th>\n",
       "      <td>1734632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>1669880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>707</th>\n",
       "      <td>1640193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>1496204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>1492863</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               QUANTITY\n",
       "household_key          \n",
       "1023            4479917\n",
       "755             3141769\n",
       "1609            2146715\n",
       "13              1863829\n",
       "1430            1741892\n",
       "1527            1734632\n",
       "1762            1669880\n",
       "707             1640193\n",
       "1029            1496204\n",
       "1314            1492863"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top10_quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d6c574c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2099.000000\n",
       "mean      3175.914007\n",
       "std       3287.043772\n",
       "min          8.170000\n",
       "25%        971.035000\n",
       "50%       2145.710000\n",
       "75%       4295.395000\n",
       "max      38319.790000\n",
       "Name: SALES_VALUE, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use multiple aggregation to create both in a single table an option\n",
    "# this here is just to use to compare to chart\n",
    "\n",
    "(transactions\n",
    " .groupby(\"household_key\")\n",
    " .agg({'SALES_VALUE': 'sum','QUANTITY': 'sum'})\n",
    " .sort_values(\"SALES_VALUE\", ascending=False)\n",
    " .loc[:, \"SALES_VALUE\"]\n",
    " .describe()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4d1f0de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='household_key'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 households by sales value plotted with a bar plot\n",
    "\n",
    "top10_value[\"SALES_VALUE\"].plot.bar()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "24204c05",
   "metadata": {},
   "source": [
    "## Product Analysis\n",
    "\n",
    "* Which products had the most sales by sales_value? Plot  a horizontal bar chart.\n",
    "* Did the top 10 selling items have a higher than average discount rate?\n",
    "* What was the most common `PRODUCT_ID` among rows with the households in our top 10 households by sales value?\n",
    "* Look up the names of the  top 10 products by sales in the `products.csv` dataset.\n",
    "* Look up the product name of the item that had the highest quantity sold in a single row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b78a084a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create top 10 products by sales df\n",
    "# group by PRODUCT_ID and sum sales value by product\n",
    "# Sort in descending order and grab top 10 rows\n",
    "\n",
    "top10_products = (transactions\n",
    "                 .groupby([\"PRODUCT_ID\"])\n",
    "                 .agg({\"SALES_VALUE\": \"sum\"})\n",
    "                 .sort_values(\"SALES_VALUE\", ascending=False)\n",
    "                 .iloc[:10]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d09ad840",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='PRODUCT_ID'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot top 10 products by sale value\n",
    "\n",
    "top10_products[\"SALES_VALUE\"].sort_values().plot.barh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "abe7ad27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.10331267387397927"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total discount for top 10 products\n",
    "# Divide that by sales value for top 10 products\n",
    "\n",
    "((transactions\n",
    " .query(\"PRODUCT_ID in @top10_products.index\")\n",
    " .loc[: ,\"total_discount\"]\n",
    " .sum()) \n",
    "/(transactions\n",
    " .query(\"PRODUCT_ID in @top10_products.index\")\n",
    " .loc[: ,\"SALES_VALUE\"]\n",
    "  .sum())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9b7286ae",
   "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>PRODUCT_ID</th>\n",
       "      <th>MANUFACTURER</th>\n",
       "      <th>DEPARTMENT</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>COMMODITY_DESC</th>\n",
       "      <th>SUB_COMMODITY_DESC</th>\n",
       "      <th>CURR_SIZE_OF_PRODUCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25671</td>\n",
       "      <td>2</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>National</td>\n",
       "      <td>FRZN ICE</td>\n",
       "      <td>ICE - CRUSHED/CUBED</td>\n",
       "      <td>22 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26081</td>\n",
       "      <td>2</td>\n",
       "      <td>MISC. TRANS.</td>\n",
       "      <td>National</td>\n",
       "      <td>NO COMMODITY DESCRIPTION</td>\n",
       "      <td>NO SUBCOMMODITY DESCRIPTION</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26093</td>\n",
       "      <td>69</td>\n",
       "      <td>PASTRY</td>\n",
       "      <td>Private</td>\n",
       "      <td>BREAD</td>\n",
       "      <td>BREAD:ITALIAN/FRENCH</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26190</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FRUIT - SHELF STABLE</td>\n",
       "      <td>APPLE SAUCE</td>\n",
       "      <td>50 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26355</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>COOKIES/CONES</td>\n",
       "      <td>SPECIALTY COOKIES</td>\n",
       "      <td>14 OZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PRODUCT_ID  MANUFACTURER    DEPARTMENT     BRAND            COMMODITY_DESC  \\\n",
       "0       25671             2       GROCERY  National                  FRZN ICE   \n",
       "1       26081             2  MISC. TRANS.  National  NO COMMODITY DESCRIPTION   \n",
       "2       26093            69        PASTRY   Private                     BREAD   \n",
       "3       26190            69       GROCERY   Private      FRUIT - SHELF STABLE   \n",
       "4       26355            69       GROCERY   Private             COOKIES/CONES   \n",
       "\n",
       "            SUB_COMMODITY_DESC CURR_SIZE_OF_PRODUCT  \n",
       "0          ICE - CRUSHED/CUBED                22 LB  \n",
       "1  NO SUBCOMMODITY DESCRIPTION                       \n",
       "2         BREAD:ITALIAN/FRENCH                       \n",
       "3                  APPLE SAUCE                50 OZ  \n",
       "4            SPECIALTY COOKIES                14 OZ  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read in products data\n",
    "\n",
    "products = pd.read_csv(\"../project_data/product.csv\")\n",
    "\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "284ae4c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([1082185, 1029743, 6534178, 6533889, 1127831,  951590,  860776, 1106523,\n",
       "        981760, 9677202],\n",
       "      dtype='int64', name='PRODUCT_ID')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look up top 10 products for households in top10_value table\n",
    "# Use query to reference index of top10_value to filter to relevant households\n",
    "# Use value counts to get counts by product_id (this will be order in descending order)\n",
    "# Then grab the top 10 products with iloc and extract the index to get product numbers\n",
    "\n",
    "top_hh_products = (transactions\n",
    "                   .query(\"household_key in @top10_value.index\")\n",
    "                   .loc[:, \"PRODUCT_ID\"]\n",
    "                   .value_counts()\n",
    "                   .iloc[:10]\n",
    "                   .index\n",
    "                  )\n",
    "\n",
    "top_hh_products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9047ee04",
   "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>PRODUCT_ID</th>\n",
       "      <th>MANUFACTURER</th>\n",
       "      <th>DEPARTMENT</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>COMMODITY_DESC</th>\n",
       "      <th>SUB_COMMODITY_DESC</th>\n",
       "      <th>CURR_SIZE_OF_PRODUCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10630</th>\n",
       "      <td>860776</td>\n",
       "      <td>2</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>National</td>\n",
       "      <td>VEGETABLES - ALL OTHERS</td>\n",
       "      <td>CUCUMBERS</td>\n",
       "      <td>36 CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20973</th>\n",
       "      <td>951590</td>\n",
       "      <td>910</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>National</td>\n",
       "      <td>BAKED BREAD/BUNS/ROLLS</td>\n",
       "      <td>MAINSTREAM WHITE BREAD</td>\n",
       "      <td>20 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24250</th>\n",
       "      <td>981760</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>EGGS</td>\n",
       "      <td>EGGS - X-LARGE</td>\n",
       "      <td>1 DZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29657</th>\n",
       "      <td>1029743</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FLUID MILK PRODUCTS</td>\n",
       "      <td>FLUID MILK WHITE ONLY</td>\n",
       "      <td>1 GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35576</th>\n",
       "      <td>1082185</td>\n",
       "      <td>2</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>National</td>\n",
       "      <td>TROPICAL FRUIT</td>\n",
       "      <td>BANANAS</td>\n",
       "      <td>40 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38262</th>\n",
       "      <td>1106523</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FLUID MILK PRODUCTS</td>\n",
       "      <td>FLUID MILK WHITE ONLY</td>\n",
       "      <td>1 GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40600</th>\n",
       "      <td>1127831</td>\n",
       "      <td>5937</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>National</td>\n",
       "      <td>BERRIES</td>\n",
       "      <td>STRAWBERRIES</td>\n",
       "      <td>16 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57181</th>\n",
       "      <td>6533889</td>\n",
       "      <td>69</td>\n",
       "      <td>MISC SALES TRAN</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57221</th>\n",
       "      <td>6534178</td>\n",
       "      <td>69</td>\n",
       "      <td>KIOSK-GAS</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68952</th>\n",
       "      <td>9677202</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>PAPER TOWELS</td>\n",
       "      <td>PAPER TOWELS &amp; HOLDERS</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PRODUCT_ID  MANUFACTURER       DEPARTMENT     BRAND  \\\n",
       "10630      860776             2          PRODUCE  National   \n",
       "20973      951590           910          GROCERY  National   \n",
       "24250      981760            69          GROCERY   Private   \n",
       "29657     1029743            69          GROCERY   Private   \n",
       "35576     1082185             2          PRODUCE  National   \n",
       "38262     1106523            69          GROCERY   Private   \n",
       "40600     1127831          5937          PRODUCE  National   \n",
       "57181     6533889            69  MISC SALES TRAN   Private   \n",
       "57221     6534178            69        KIOSK-GAS   Private   \n",
       "68952     9677202            69          GROCERY   Private   \n",
       "\n",
       "                COMMODITY_DESC      SUB_COMMODITY_DESC CURR_SIZE_OF_PRODUCT  \n",
       "10630  VEGETABLES - ALL OTHERS               CUCUMBERS                36 CT  \n",
       "20973   BAKED BREAD/BUNS/ROLLS  MAINSTREAM WHITE BREAD                20 OZ  \n",
       "24250                     EGGS          EGGS - X-LARGE                 1 DZ  \n",
       "29657      FLUID MILK PRODUCTS   FLUID MILK WHITE ONLY                 1 GA  \n",
       "35576           TROPICAL FRUIT                 BANANAS                40 LB  \n",
       "38262      FLUID MILK PRODUCTS   FLUID MILK WHITE ONLY                 1 GA  \n",
       "40600                  BERRIES            STRAWBERRIES                16 OZ  \n",
       "57181        COUPON/MISC ITEMS   GASOLINE-REG UNLEADED                       \n",
       "57221        COUPON/MISC ITEMS   GASOLINE-REG UNLEADED                       \n",
       "68952             PAPER TOWELS  PAPER TOWELS & HOLDERS                       "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter product table to products from prior cell\n",
    "\n",
    "products.query(\"PRODUCT_ID in @top_hh_products\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b267852",
   "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>PRODUCT_ID</th>\n",
       "      <th>MANUFACTURER</th>\n",
       "      <th>DEPARTMENT</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>COMMODITY_DESC</th>\n",
       "      <th>SUB_COMMODITY_DESC</th>\n",
       "      <th>CURR_SIZE_OF_PRODUCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57221</th>\n",
       "      <td>6534178</td>\n",
       "      <td>69</td>\n",
       "      <td>KIOSK-GAS</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PRODUCT_ID  MANUFACTURER DEPARTMENT    BRAND     COMMODITY_DESC  \\\n",
       "57221     6534178            69  KIOSK-GAS  Private  COUPON/MISC ITEMS   \n",
       "\n",
       "          SUB_COMMODITY_DESC CURR_SIZE_OF_PRODUCT  \n",
       "57221  GASOLINE-REG UNLEADED                       "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Product with highest quantity in a single row\n",
    "\n",
    "products.query(\"PRODUCT_ID == 6534178\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0c1fb0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRODUCT_ID</th>\n",
       "      <th>MANUFACTURER</th>\n",
       "      <th>DEPARTMENT</th>\n",
       "      <th>BRAND</th>\n",
       "      <th>COMMODITY_DESC</th>\n",
       "      <th>SUB_COMMODITY_DESC</th>\n",
       "      <th>CURR_SIZE_OF_PRODUCT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16863</th>\n",
       "      <td>916122</td>\n",
       "      <td>4314</td>\n",
       "      <td>MEAT</td>\n",
       "      <td>National</td>\n",
       "      <td>CHICKEN</td>\n",
       "      <td>CHICKEN BREAST BONELESS</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25754</th>\n",
       "      <td>995242</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FLUID MILK PRODUCTS</td>\n",
       "      <td>FLUID MILK WHITE ONLY</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29657</th>\n",
       "      <td>1029743</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FLUID MILK PRODUCTS</td>\n",
       "      <td>FLUID MILK WHITE ONLY</td>\n",
       "      <td>1 GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35576</th>\n",
       "      <td>1082185</td>\n",
       "      <td>2</td>\n",
       "      <td>PRODUCE</td>\n",
       "      <td>National</td>\n",
       "      <td>TROPICAL FRUIT</td>\n",
       "      <td>BANANAS</td>\n",
       "      <td>40 LB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38262</th>\n",
       "      <td>1106523</td>\n",
       "      <td>69</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>Private</td>\n",
       "      <td>FLUID MILK PRODUCTS</td>\n",
       "      <td>FLUID MILK WHITE ONLY</td>\n",
       "      <td>1 GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53097</th>\n",
       "      <td>5569230</td>\n",
       "      <td>1208</td>\n",
       "      <td>GROCERY</td>\n",
       "      <td>National</td>\n",
       "      <td>SOFT DRINKS</td>\n",
       "      <td>SOFT DRINKS 12/18&amp;15PK CAN CAR</td>\n",
       "      <td>12 OZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57171</th>\n",
       "      <td>6533765</td>\n",
       "      <td>69</td>\n",
       "      <td>KIOSK-GAS</td>\n",
       "      <td>Private</td>\n",
       "      <td>FUEL</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57181</th>\n",
       "      <td>6533889</td>\n",
       "      <td>69</td>\n",
       "      <td>MISC SALES TRAN</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57216</th>\n",
       "      <td>6534166</td>\n",
       "      <td>69</td>\n",
       "      <td>MISC SALES TRAN</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57221</th>\n",
       "      <td>6534178</td>\n",
       "      <td>69</td>\n",
       "      <td>KIOSK-GAS</td>\n",
       "      <td>Private</td>\n",
       "      <td>COUPON/MISC ITEMS</td>\n",
       "      <td>GASOLINE-REG UNLEADED</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PRODUCT_ID  MANUFACTURER       DEPARTMENT     BRAND  \\\n",
       "16863      916122          4314             MEAT  National   \n",
       "25754      995242            69          GROCERY   Private   \n",
       "29657     1029743            69          GROCERY   Private   \n",
       "35576     1082185             2          PRODUCE  National   \n",
       "38262     1106523            69          GROCERY   Private   \n",
       "53097     5569230          1208          GROCERY  National   \n",
       "57171     6533765            69        KIOSK-GAS   Private   \n",
       "57181     6533889            69  MISC SALES TRAN   Private   \n",
       "57216     6534166            69  MISC SALES TRAN   Private   \n",
       "57221     6534178            69        KIOSK-GAS   Private   \n",
       "\n",
       "            COMMODITY_DESC              SUB_COMMODITY_DESC  \\\n",
       "16863              CHICKEN         CHICKEN BREAST BONELESS   \n",
       "25754  FLUID MILK PRODUCTS           FLUID MILK WHITE ONLY   \n",
       "29657  FLUID MILK PRODUCTS           FLUID MILK WHITE ONLY   \n",
       "35576       TROPICAL FRUIT                         BANANAS   \n",
       "38262  FLUID MILK PRODUCTS           FLUID MILK WHITE ONLY   \n",
       "53097          SOFT DRINKS  SOFT DRINKS 12/18&15PK CAN CAR   \n",
       "57171                 FUEL           GASOLINE-REG UNLEADED   \n",
       "57181    COUPON/MISC ITEMS           GASOLINE-REG UNLEADED   \n",
       "57216    COUPON/MISC ITEMS           GASOLINE-REG UNLEADED   \n",
       "57221    COUPON/MISC ITEMS           GASOLINE-REG UNLEADED   \n",
       "\n",
       "      CURR_SIZE_OF_PRODUCT  \n",
       "16863                       \n",
       "25754                       \n",
       "29657                 1 GA  \n",
       "35576                40 LB  \n",
       "38262                 1 GA  \n",
       "53097                12 OZ  \n",
       "57171                       \n",
       "57181                       \n",
       "57216                       \n",
       "57221                       "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look up 10 product names for all customers (from first cell)\n",
    "\n",
    "products.query(\"PRODUCT_ID in @top10_products.index\")"
   ]
  }
 ],
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