diff --git a/lab-dw-data-structuring-and-combining.ipynb b/lab-dw-data-structuring-and-combining.ipynb
index ec4e3f9..c83bb68 100644
--- a/lab-dw-data-structuring-and-combining.ipynb
+++ b/lab-dw-data-structuring-and-combining.ipynb
@@ -36,14 +36,442 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "492d06e3-92c7-4105-ac72-536db98d3244",
    "metadata": {
     "id": "492d06e3-92c7-4105-ac72-536db98d3244"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c7a21e8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "print(pd.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6760a859",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Customer",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "ST",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "GENDER",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Education",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Customer Lifetime Value",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "Income",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Monthly Premium Auto",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Number of Open Complaints",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Policy Type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Vehicle Class",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Total Claim Amount",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "State",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Gender",
+         "rawType": "str",
+         "type": "string"
+        }
+       ],
+       "ref": "0d458bf9-8e6b-4690-83a3-3d0294210270",
+       "rows": [
+        [
+         "0",
+         "RB50392",
+         "Washington",
+         null,
+         "Master",
+         null,
+         "0.0",
+         "1000.0",
+         "1/0/00",
+         "Personal Auto",
+         "Four-Door Car",
+         "2.704934",
+         null,
+         null
+        ],
+        [
+         "1",
+         "QZ44356",
+         "Arizona",
+         "F",
+         "Bachelor",
+         "697953.59%",
+         "0.0",
+         "94.0",
+         "1/0/00",
+         "Personal Auto",
+         "Four-Door Car",
+         "1131.464935",
+         null,
+         null
+        ],
+        [
+         "2",
+         "AI49188",
+         "Nevada",
+         "F",
+         "Bachelor",
+         "1288743.17%",
+         "48767.0",
+         "108.0",
+         "1/0/00",
+         "Personal Auto",
+         "Two-Door Car",
+         "566.472247",
+         null,
+         null
+        ],
+        [
+         "3",
+         "WW63253",
+         "California",
+         "M",
+         "Bachelor",
+         "764586.18%",
+         "0.0",
+         "106.0",
+         "1/0/00",
+         "Corporate Auto",
+         "SUV",
+         "529.881344",
+         null,
+         null
+        ],
+        [
+         "4",
+         "GA49547",
+         "Washington",
+         "M",
+         "High School or Below",
+         "536307.65%",
+         "36357.0",
+         "68.0",
+         "1/0/00",
+         "Personal Auto",
+         "Four-Door Car",
+         "17.269323",
+         null,
+         null
+        ]
+       ],
+       "shape": {
+        "columns": 13,
+        "rows": 5
+       }
+      },
+      "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</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2.704934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953.59%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743.17%</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566.472247</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586.18%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529.881344</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307.65%</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17.269323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER             Education Customer Lifetime Value  \\\n",
+       "0  RB50392  Washington    NaN                Master                     NaN   \n",
+       "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+       "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+       "3  WW63253  California      M              Bachelor              764586.18%   \n",
+       "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+       "\n",
+       "    Income  Monthly Premium Auto Number of Open Complaints     Policy Type  \\\n",
+       "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+       "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+       "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+       "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+       "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+       "\n",
+       "   Vehicle Class  Total Claim Amount State Gender  \n",
+       "0  Four-Door Car            2.704934   NaN    NaN  \n",
+       "1  Four-Door Car         1131.464935   NaN    NaN  \n",
+       "2   Two-Door Car          566.472247   NaN    NaN  \n",
+       "3            SUV          529.881344   NaN    NaN  \n",
+       "4  Four-Door Car           17.269323   NaN    NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Load the three datasets\n",
+    "file1 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\")\n",
+    "file2 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\")\n",
+    "file3 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\")\n",
+    "\n",
+    "# Combine them into one DataFrame\n",
+    "df = pd.concat([file1, file2, file3], ignore_index=True)\n",
+    "\n",
+    "# Display the first rows\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "44146957",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 12074 entries, 0 to 12073\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                     Non-Null Count  Dtype  \n",
+      "---  ------                     --------------  -----  \n",
+      " 0   Customer                   9137 non-null   str    \n",
+      " 1   ST                         2067 non-null   str    \n",
+      " 2   GENDER                     1945 non-null   str    \n",
+      " 3   Education                  9137 non-null   str    \n",
+      " 4   Customer Lifetime Value    9130 non-null   object \n",
+      " 5   Income                     9137 non-null   float64\n",
+      " 6   Monthly Premium Auto       9137 non-null   float64\n",
+      " 7   Number of Open Complaints  9137 non-null   object \n",
+      " 8   Policy Type                9137 non-null   str    \n",
+      " 9   Vehicle Class              9137 non-null   str    \n",
+      " 10  Total Claim Amount         9137 non-null   float64\n",
+      " 11  State                      7070 non-null   str    \n",
+      " 12  Gender                     7070 non-null   str    \n",
+      "dtypes: float64(3), object(2), str(8)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the shape of the DataFrame\n",
+    "df.shape\n",
+    "\n",
+    "# Display the column names\n",
+    "df.columns\n",
+    "\n",
+    "# Display information about the DataFrame\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "09999c9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'st', 'gender', 'education', 'customer_lifetime_value',\n",
+       "       'income', 'monthly_premium_auto', 'number_of_open_complaints',\n",
+       "       'policy_type', 'vehicle_class', 'total_claim_amount', 'state',\n",
+       "       'gender'],\n",
+       "      dtype='str')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Standardize column names\n",
+    "df.columns = (\n",
+    "    df.columns\n",
+    "      .str.lower()\n",
+    "      .str.strip()\n",
+    "      .str.replace(\" \", \"_\")\n",
+    ")\n",
+    "\n",
+    "df.columns"
    ]
   },
   {
@@ -72,14 +500,574 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
    "metadata": {
     "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.DataFrame'>\n",
+      "RangeIndex: 12074 entries, 0 to 12073\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                     Non-Null Count  Dtype  \n",
+      "---  ------                     --------------  -----  \n",
+      " 0   customer                   9137 non-null   str    \n",
+      " 1   st                         2067 non-null   str    \n",
+      " 2   gender                     1945 non-null   str    \n",
+      " 3   education                  9137 non-null   str    \n",
+      " 4   customer_lifetime_value    9130 non-null   object \n",
+      " 5   income                     9137 non-null   float64\n",
+      " 6   monthly_premium_auto       9137 non-null   float64\n",
+      " 7   number_of_open_complaints  9137 non-null   object \n",
+      " 8   policy_type                9137 non-null   str    \n",
+      " 9   vehicle_class              9137 non-null   str    \n",
+      " 10  total_claim_amount         9137 non-null   float64\n",
+      " 11  state                      7070 non-null   str    \n",
+      " 12  gender                     7070 non-null   str    \n",
+      "dtypes: float64(3), object(2), str(8)\n",
+      "memory usage: 1.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.shape\n",
+    "df.columns\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "0e0bce20",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "unnamed:_0",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "customer",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "state",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "customer_lifetime_value",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "response",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "coverage",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "education",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "effective_to_date",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "employmentstatus",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "gender",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "income",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "location_code",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "marital_status",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "monthly_premium_auto",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "months_since_last_claim",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "months_since_policy_inception",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "number_of_open_complaints",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "number_of_policies",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "policy_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "policy",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "renew_offer_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "sales_channel",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "total_claim_amount",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vehicle_class",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "vehicle_size",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "vehicle_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "month",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "eaf71d69-e3cb-40ce-aa49-a034f5dc140a",
+       "rows": [
+        [
+         "0",
+         "0",
+         "DK49336",
+         "Arizona",
+         "4809.21696",
+         "No",
+         "Basic",
+         "College",
+         "2011-02-18",
+         "Employed",
+         "M",
+         "48029",
+         "Suburban",
+         "Married",
+         "61",
+         "7.0",
+         "52",
+         "0.0",
+         "9",
+         "Corporate Auto",
+         "Corporate L3",
+         "Offer3",
+         "Agent",
+         "292.8",
+         "Four-Door Car",
+         "Medsize",
+         "A",
+         "2"
+        ],
+        [
+         "1",
+         "1",
+         "KX64629",
+         "California",
+         "2228.525238",
+         "No",
+         "Basic",
+         "College",
+         "2011-01-18",
+         "Unemployed",
+         "F",
+         "0",
+         "Suburban",
+         "Single",
+         "64",
+         "3.0",
+         "26",
+         "0.0",
+         "1",
+         "Personal Auto",
+         "Personal L3",
+         "Offer4",
+         "Call Center",
+         "744.924331",
+         "Four-Door Car",
+         "Medsize",
+         "A",
+         "1"
+        ],
+        [
+         "2",
+         "2",
+         "LZ68649",
+         "Washington",
+         "14947.9173",
+         "No",
+         "Basic",
+         "Bachelor",
+         "2011-02-10",
+         "Employed",
+         "M",
+         "22139",
+         "Suburban",
+         "Single",
+         "100",
+         "34.0",
+         "31",
+         "0.0",
+         "2",
+         "Personal Auto",
+         "Personal L3",
+         "Offer3",
+         "Call Center",
+         "480.0",
+         "SUV",
+         "Medsize",
+         "A",
+         "2"
+        ],
+        [
+         "3",
+         "3",
+         "XL78013",
+         "Oregon",
+         "22332.43946",
+         "Yes",
+         "Extended",
+         "College",
+         "2011-01-11",
+         "Employed",
+         "M",
+         "49078",
+         "Suburban",
+         "Single",
+         "97",
+         "10.0",
+         "3",
+         "0.0",
+         "2",
+         "Corporate Auto",
+         "Corporate L3",
+         "Offer2",
+         "Branch",
+         "484.013411",
+         "Four-Door Car",
+         "Medsize",
+         "A",
+         "1"
+        ],
+        [
+         "4",
+         "4",
+         "QA50777",
+         "Oregon",
+         "9025.067525",
+         "No",
+         "Premium",
+         "Bachelor",
+         "2011-01-17",
+         "Medical Leave",
+         "F",
+         "23675",
+         "Suburban",
+         "Married",
+         "117",
+         "15.149070740488469",
+         "31",
+         "0.384256105867471",
+         "7",
+         "Personal Auto",
+         "Personal L2",
+         "Offer1",
+         "Branch",
+         "707.925645",
+         "Four-Door Car",
+         "Medsize",
+         "A",
+         "1"
+        ]
+       ],
+       "shape": {
+        "columns": 27,
+        "rows": 5
+       }
+      },
+      "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>unnamed:_0</th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "      <th>vehicle_type</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-02-18</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-18</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LZ68649</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>14947.917300</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-02-10</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>480.000000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-01-17</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unnamed:_0 customer       state  customer_lifetime_value response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "2           2  LZ68649  Washington             14947.917300       No   \n",
+       "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4           4  QA50777      Oregon              9025.067525       No   \n",
+       "\n",
+       "   coverage education effective_to_date employmentstatus gender  ...  \\\n",
+       "0     Basic   College        2011-02-18         Employed      M  ...   \n",
+       "1     Basic   College        2011-01-18       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor        2011-02-10         Employed      M  ...   \n",
+       "3  Extended   College        2011-01-11         Employed      M  ...   \n",
+       "4   Premium  Bachelor        2011-01-17    Medical Leave      F  ...   \n",
+       "\n",
+       "   number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                   9  Corporate Auto  Corporate L3            Offer3   \n",
+       "1                   1   Personal Auto   Personal L3            Offer4   \n",
+       "2                   2   Personal Auto   Personal L3            Offer3   \n",
+       "3                   2  Corporate Auto  Corporate L3            Offer2   \n",
+       "4                   7   Personal Auto   Personal L2            Offer1   \n",
+       "\n",
+       "   sales_channel  total_claim_amount  vehicle_class  vehicle_size  \\\n",
+       "0          Agent          292.800000  Four-Door Car       Medsize   \n",
+       "1    Call Center          744.924331  Four-Door Car       Medsize   \n",
+       "2    Call Center          480.000000            SUV       Medsize   \n",
+       "3         Branch          484.013411  Four-Door Car       Medsize   \n",
+       "4         Branch          707.925645  Four-Door Car       Medsize   \n",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "2            A     2  \n",
+       "3            A     1  \n",
+       "4            A     1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "import pandas as pd\n",
+    "\n",
+    "marketing = pd.read_csv(\n",
+    "    \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    ")\n",
+    "\n",
+    "marketing.head()"
    ]
   },
   {
@@ -93,6 +1081,200 @@
     "Round the total revenue to 2 decimal points.  Analyze the resulting table to draw insights."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ed82f6e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Total Claim Amount'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m sales_channel_revenue = marketing.pivot_table(\n\u001b[32m      2\u001b[39m     index=\u001b[33m\"Sales Channel\"\u001b[39m,\n\u001b[32m      3\u001b[39m     values=\u001b[33m\"Total Claim Amount\"\u001b[39m,\n\u001b[32m      4\u001b[39m     aggfunc=\u001b[33m\"sum\"\u001b[39m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\teres\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\pandas\\core\\frame.py:11159\u001b[39m, in \u001b[36mDataFrame.pivot_table\u001b[39m\u001b[34m(self, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort, **kwargs)\u001b[39m\n\u001b[32m  11155\u001b[39m         **kwargs,\n\u001b[32m  11156\u001b[39m     ) -> DataFrame:\n\u001b[32m  11157\u001b[39m         \u001b[38;5;28;01mfrom\u001b[39;00m pandas.core.reshape.pivot \u001b[38;5;28;01mimport\u001b[39;00m pivot_table\n\u001b[32m  11158\u001b[39m \n\u001b[32m> \u001b[39m\u001b[32m11159\u001b[39m         return pivot_table(\n\u001b[32m  11160\u001b[39m             self,\n\u001b[32m  11161\u001b[39m             values=values,\n\u001b[32m  11162\u001b[39m             index=index,\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\teres\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:267\u001b[39m, in \u001b[36mpivot_table\u001b[39m\u001b[34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort, **kwargs)\u001b[39m\n\u001b[32m    264\u001b[39m     table = concat(pieces, keys=keys, axis=\u001b[32m1\u001b[39m)\n\u001b[32m    265\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m table.__finalize__(data, method=\u001b[33m\"\u001b[39m\u001b[33mpivot_table\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m267\u001b[39m table = \u001b[30;43m__internal_pivot_table\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    268\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdata\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    269\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mvalues\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    270\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mindex\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    271\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mcolumns\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    272\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43maggfunc\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    273\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mfill_value\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    274\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmargins\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    275\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdropna\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    276\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mmargins_name\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    277\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mobserved\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    278\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43msort\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    279\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    280\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    281\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m table.__finalize__(data, method=\u001b[33m\"\u001b[39m\u001b[33mpivot_table\u001b[39m\u001b[33m\"\u001b[39m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\teres\\AppData\\Local\\Programs\\Python\\Python314\\Lib\\site-packages\\pandas\\core\\reshape\\pivot.py:315\u001b[39m, in \u001b[36m__internal_pivot_table\u001b[39m\u001b[34m(data, values, index, columns, aggfunc, fill_value, margins, dropna, margins_name, observed, sort, kwargs)\u001b[39m\n\u001b[32m    313\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m values:\n\u001b[32m    314\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m data:\n\u001b[32m--> \u001b[39m\u001b[32m315\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(i)\n\u001b[32m    317\u001b[39m to_filter = []\n\u001b[32m    318\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m keys + values:\n",
+      "\u001b[31mKeyError\u001b[39m: 'Total Claim Amount'"
+     ]
+    }
+   ],
+   "source": [
+    "sales_channel_revenue = marketing.pivot_table(\n",
+    "    index=\"Sales Channel\",\n",
+    "    values=\"Total Claim Amount\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "sales_channel_revenue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "5a57d6f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['unnamed:_0',\n",
+       " 'customer',\n",
+       " 'state',\n",
+       " 'customer_lifetime_value',\n",
+       " 'response',\n",
+       " 'coverage',\n",
+       " 'education',\n",
+       " 'effective_to_date',\n",
+       " 'employmentstatus',\n",
+       " 'gender',\n",
+       " 'income',\n",
+       " 'location_code',\n",
+       " 'marital_status',\n",
+       " 'monthly_premium_auto',\n",
+       " 'months_since_last_claim',\n",
+       " 'months_since_policy_inception',\n",
+       " 'number_of_open_complaints',\n",
+       " 'number_of_policies',\n",
+       " 'policy_type',\n",
+       " 'policy',\n",
+       " 'renew_offer_type',\n",
+       " 'sales_channel',\n",
+       " 'total_claim_amount',\n",
+       " 'vehicle_class',\n",
+       " 'vehicle_size',\n",
+       " 'vehicle_type',\n",
+       " 'month']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing.columns.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "95ba0c9c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "sales_channel",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "total_claim_amount",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "25a73d00-75d4-424e-b2a9-0f80a6cae80b",
+       "rows": [
+        [
+         "Agent",
+         "1810226.82"
+        ],
+        [
+         "Branch",
+         "1301204.0"
+        ],
+        [
+         "Call Center",
+         "926600.82"
+        ],
+        [
+         "Web",
+         "706600.04"
+        ]
+       ],
+       "shape": {
+        "columns": 1,
+        "rows": 4
+       }
+      },
+      "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>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>1810226.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>1301204.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>926600.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>706600.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               total_claim_amount\n",
+       "sales_channel                    \n",
+       "Agent                  1810226.82\n",
+       "Branch                 1301204.00\n",
+       "Call Center             926600.82\n",
+       "Web                     706600.04"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_channel_revenue = marketing.pivot_table(\n",
+    "    index=\"sales_channel\",\n",
+    "    values=\"total_claim_amount\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "sales_channel_revenue"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "640993b2-a291-436c-a34d-a551144f8196",
@@ -103,6 +1285,157 @@
     "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "e87d553a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "gender",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "Bachelor",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "College",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Doctor",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "High School or Below",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Master",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "dadd480e-a18c-487f-98e9-834650eb5254",
+       "rows": [
+        [
+         "F",
+         "7874.27",
+         "7748.82",
+         "7328.51",
+         "8675.22",
+         "8157.05"
+        ],
+        [
+         "M",
+         "7703.6",
+         "8052.46",
+         "7415.33",
+         "8149.69",
+         "8168.83"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 2
+       }
+      },
+      "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>education</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <th>College</th>\n",
+       "      <th>Doctor</th>\n",
+       "      <th>High School or Below</th>\n",
+       "      <th>Master</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>7874.27</td>\n",
+       "      <td>7748.82</td>\n",
+       "      <td>7328.51</td>\n",
+       "      <td>8675.22</td>\n",
+       "      <td>8157.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7703.60</td>\n",
+       "      <td>8052.46</td>\n",
+       "      <td>7415.33</td>\n",
+       "      <td>8149.69</td>\n",
+       "      <td>8168.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "education  Bachelor  College   Doctor  High School or Below   Master\n",
+       "gender                                                              \n",
+       "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+       "M           7703.60  8052.46  7415.33               8149.69  8168.83"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clv_gender_education = marketing.pivot_table(\n",
+    "    index=\"gender\",\n",
+    "    columns=\"education\",\n",
+    "    values=\"customer_lifetime_value\",\n",
+    "    aggfunc=\"mean\"\n",
+    ").round(2)\n",
+    "\n",
+    "clv_gender_education"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb98b2c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
@@ -130,14 +1463,169 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "3a069e0b-b400-470e-904d-d17582191be4",
    "metadata": {
     "id": "3a069e0b-b400-470e-904d-d17582191be4"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "policy_type",
+         "rawType": "str",
+         "type": "string"
+        },
+        {
+         "name": "month",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "number_of_open_complaints",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "6bd7ea31-cee7-48e7-b5f2-74ab8e828a10",
+       "rows": [
+        [
+         "0",
+         "Corporate Auto",
+         "1",
+         "443.4349518341929"
+        ],
+        [
+         "1",
+         "Corporate Auto",
+         "2",
+         "385.2081346696507"
+        ],
+        [
+         "2",
+         "Personal Auto",
+         "1",
+         "1727.6057215140604"
+        ],
+        [
+         "3",
+         "Personal Auto",
+         "2",
+         "1453.684440984723"
+        ],
+        [
+         "4",
+         "Special Auto",
+         "1",
+         "87.07404884693977"
+        ],
+        [
+         "5",
+         "Special Auto",
+         "2",
+         "95.22681716454218"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 6
+       }
+      },
+      "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>policy_type</th>\n",
+       "      <th>month</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>1</td>\n",
+       "      <td>443.434952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>2</td>\n",
+       "      <td>385.208135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1727.605722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1453.684441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>1</td>\n",
+       "      <td>87.074049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>2</td>\n",
+       "      <td>95.226817</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      policy_type  month  number_of_open_complaints\n",
+       "0  Corporate Auto      1                 443.434952\n",
+       "1  Corporate Auto      2                 385.208135\n",
+       "2   Personal Auto      1                1727.605722\n",
+       "3   Personal Auto      2                1453.684441\n",
+       "4    Special Auto      1                  87.074049\n",
+       "5    Special Auto      2                  95.226817"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "complaints_by_policy_month = marketing.pivot_table(\n",
+    "    index=[\"policy_type\", \"month\"],\n",
+    "    values=\"number_of_open_complaints\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").reset_index()\n",
+    "\n",
+    "complaints_by_policy_month"
    ]
   }
  ],
@@ -146,7 +1634,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -160,7 +1648,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.14.6"
   }
  },
  "nbformat": 4,