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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e",
+   "metadata": {
+    "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e"
+   },
+   "source": [
+    "# Lab | Data Structuring and Combining Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986",
+   "metadata": {
+    "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986"
+   },
+   "source": [
+    "## Challenge 1: Combining & Cleaning Data\n",
+    "\n",
+    "In this challenge, we will be working with the customer data from an insurance company, as we did in the two previous labs. The data can be found here:\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\n",
+    "\n",
+    "But this time, we got new data, which can be found in the following 2 CSV files located at the links below.\n",
+    "\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\n",
+    "\n",
+    "Note that you'll need to clean and format the new data.\n",
+    "\n",
+    "Observation:\n",
+    "- One option is to first combine the three datasets and then apply the cleaning function to the new combined dataset\n",
+    "- Another option would be to read the clean file you saved in the previous lab, and just clean the two new files and concatenate the three clean datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "492d06e3-92c7-4105-ac72-536db98d3244",
+   "metadata": {
+    "id": "492d06e3-92c7-4105-ac72-536db98d3244"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Load all three CSV files\n",
+    "file1 = pd.read_csv(\"filetwo.csv\")     # file1 is mislabeled, it's actually file2\n",
+    "file2 = pd.read_csv(\"filethree.csv\")   # file2 is mislabeled, it's actually file3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e0674f28-82bd-4711-898a-e3425ed7b487",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Standardize column names\n",
+    "def standardize_columns(df):\n",
+    "    df.columns = df.columns.str.lower().str.replace(\" \", \"_\")\n",
+    "    return df\n",
+    "\n",
+    "file1 = standardize_columns(file1)\n",
+    "file2 = standardize_columns(file2)\n",
+    "\n",
+    "# Clean file1 (remove '%' and extract complaint numbers)\n",
+    "file1['customer_lifetime_value'] = file1['customer_lifetime_value'].str.replace('%', '').astype(float) / 100\n",
+    "file1['number_of_open_complaints'] = file1['number_of_open_complaints'].str.extract('(\\d+)').astype(float)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "92da7f4c-cd97-475a-b893-9f0e9dc6c9c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>customer</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>total_claim_amount</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>state</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>3239.1247</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>633.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>4626.8011</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>547.2</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>8997.0402</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>537.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UH35128</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>25807.0630</td>\n",
+       "      <td>71210</td>\n",
+       "      <td>214</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1027.2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>WH52799</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>3808.1221</td>\n",
+       "      <td>94903</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>451.2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer          st gender education  customer_lifetime_value  income  \\\n",
+       "0  GS98873     Arizona      F  Bachelor                3239.1247   16061   \n",
+       "1  CW49887  California      F    Master                4626.8011   79487   \n",
+       "2  MY31220  California      F   College                8997.0402   54230   \n",
+       "3  UH35128      Oregon      F   College               25807.0630   71210   \n",
+       "4  WH52799     Arizona      F   College                3808.1221   94903   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints  total_claim_amount  \\\n",
+       "0                    88                        1.0               633.6   \n",
+       "1                   114                        1.0               547.2   \n",
+       "2                   112                        1.0               537.6   \n",
+       "3                   214                        1.0              1027.2   \n",
+       "4                    94                        1.0               451.2   \n",
+       "\n",
+       "      policy_type  vehicle_class state  \n",
+       "0   Personal Auto  Four-Door Car   NaN  \n",
+       "1    Special Auto            SUV   NaN  \n",
+       "2   Personal Auto   Two-Door Car   NaN  \n",
+       "3   Personal Auto     Luxury Car   NaN  \n",
+       "4  Corporate Auto   Two-Door Car   NaN  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Combine both cleaned dataframes\n",
+    "combined_data = pd.concat([file1, file2], ignore_index=True)\n",
+    "combined_data.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31b8a9e7-7db9-4604-991b-ef6771603e57",
+   "metadata": {
+    "id": "31b8a9e7-7db9-4604-991b-ef6771603e57"
+   },
+   "source": [
+    "# Challenge 2: Structuring Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b",
+   "metadata": {
+    "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company, but we will use a dataset with more columns, called marketing_customer_analysis.csv, which can be found at the following link:\n",
+    "\n",
+    "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\n",
+    "\n",
+    "This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by performing data cleaning, formatting, and structuring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
+   "metadata": {
+    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
+   },
+   "outputs": [],
+   "source": [
+    "# Load marketing dataset\n",
+    "marketing_df = pd.read_csv(\"marketing_customer_analysis_clean.csv\")\n",
+    "\n",
+    "# Standardize column names\n",
+    "marketing_df.columns = marketing_df.columns.str.lower().str.replace(\" \", \"_\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "d4eb8e1e-790b-4eff-85fb-41a859c8c0fd",
+   "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>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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total revenue by sales channel\n",
+    "revenue_by_channel = marketing_df.pivot_table(\n",
+    "    index='sales_channel',\n",
+    "    values='total_claim_amount',\n",
+    "    aggfunc='sum'\n",
+    ").round(2)\n",
+    "\n",
+    "revenue_by_channel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "639d7348-ee0c-402f-b3fc-db4ad664e8de",
+   "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>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": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total revenue by sales channel\n",
+    "revenue_by_channel = marketing_df.pivot_table(\n",
+    "    index='sales_channel',\n",
+    "    values='total_claim_amount',\n",
+    "    aggfunc='sum'\n",
+    ").round(2)\n",
+    "\n",
+    "revenue_by_channel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "cdfb2193-bf11-4e17-a501-96ce3586bc4e",
+   "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></th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <td>7874.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>College</th>\n",
+       "      <td>7748.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Doctor</th>\n",
+       "      <td>7328.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High School or Below</th>\n",
+       "      <td>8675.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>8157.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <td>7703.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>College</th>\n",
+       "      <td>8052.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Doctor</th>\n",
+       "      <td>7415.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High School or Below</th>\n",
+       "      <td>8149.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>8168.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             customer_lifetime_value\n",
+       "gender education                                    \n",
+       "F      Bachelor                              7874.27\n",
+       "       College                               7748.82\n",
+       "       Doctor                                7328.51\n",
+       "       High School or Below                  8675.22\n",
+       "       Master                                8157.05\n",
+       "M      Bachelor                              7703.60\n",
+       "       College                               8052.46\n",
+       "       Doctor                                7415.33\n",
+       "       High School or Below                  8149.69\n",
+       "       Master                                8168.83"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Average CLV by gender and education\n",
+    "clv_by_group = marketing_df.pivot_table(\n",
+    "    index=['gender', 'education'],\n",
+    "    values='customer_lifetime_value',\n",
+    "    aggfunc='mean'\n",
+    ").round(2)\n",
+    "\n",
+    "clv_by_group\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df35fd0d-513e-4e77-867e-429da10a9cc7",
+   "metadata": {
+    "id": "df35fd0d-513e-4e77-867e-429da10a9cc7"
+   },
+   "source": [
+    "1. You work at the marketing department and you want to know which sales channel brought the most sales in terms of total revenue. Using pivot, create a summary table showing the total revenue for each sales channel (branch, call center, web, and mail).\n",
+    "Round the total revenue to 2 decimal points.  Analyze the resulting table to draw insights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "640993b2-a291-436c-a34d-a551144f8196",
+   "metadata": {
+    "id": "640993b2-a291-436c-a34d-a551144f8196"
+   },
+   "source": [
+    "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": "markdown",
+   "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
+   "metadata": {
+    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198"
+   },
+   "source": [
+    "## Bonus\n",
+    "\n",
+    "You work at the customer service department and you want to know which months had the highest number of complaints by policy type category. Create a summary table showing the number of complaints by policy type and month.\n",
+    "Show it in a long format table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291",
+   "metadata": {
+    "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291"
+   },
+   "source": [
+    "*In data analysis, a long format table is a way of structuring data in which each observation or measurement is stored in a separate row of the table. The key characteristic of a long format table is that each column represents a single variable, and each row represents a single observation of that variable.*\n",
+    "\n",
+    "*More information about long and wide format tables here: https://www.statology.org/long-vs-wide-data/*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a069e0b-b400-470e-904d-d17582191be4",
+   "metadata": {
+    "id": "3a069e0b-b400-470e-904d-d17582191be4"
+   },
+   "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>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>February</td>\n",
+       "      <td>385.208135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>January</td>\n",
+       "      <td>443.434952</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>February</td>\n",
+       "      <td>1453.684441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>January</td>\n",
+       "      <td>1727.605722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>February</td>\n",
+       "      <td>95.226817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>January</td>\n",
+       "      <td>87.074049</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      policy_type     month  number_of_open_complaints\n",
+       "0  Corporate Auto  February                 385.208135\n",
+       "1  Corporate Auto   January                 443.434952\n",
+       "2   Personal Auto  February                1453.684441\n",
+       "3   Personal Auto   January                1727.605722\n",
+       "4    Special Auto  February                  95.226817\n",
+       "5    Special Auto   January                  87.074049"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Convert to datetime and extract month\n",
+    "marketing_df['effective_to_date'] = pd.to_datetime(marketing_df['effective_to_date'])\n",
+    "marketing_df['month'] = marketing_df['effective_to_date'].dt.month_name()\n",
+    "\n",
+    "# Complaints by policy type and month\n",
+    "complaints_table = marketing_df.groupby(['policy_type', 'month'])['number_of_open_complaints'].sum().reset_index()\n",
+    "complaints_table\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab01dff8-1e38-41c0-825a-78268c3bcf8f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}