diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..408ed74 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,941 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
-      "metadata": {
-        "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
-      },
-      "source": [
-        "# Lab | Data Aggregation and Filtering"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
-      "metadata": {
-        "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
-      },
-      "source": [
-        "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset 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.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 first performing data cleaning, formatting, and structuring."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
-      "metadata": {
-        "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50"
-      },
-      "source": [
-        "1. Create a new DataFrame that only includes customers who:\n",
-        "   - have a **low total_claim_amount** (e.g., below $1,000),\n",
-        "   - have a response \"Yes\" to the last marketing campaign."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
-      "metadata": {
-        "id": "b9be383e-5165-436e-80c8-57d4c757c8c3"
-      },
-      "source": [
-        "2. Using the original Dataframe, analyze:\n",
-        "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
-        "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
-      "metadata": {
-        "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0"
-      },
-      "source": [
-        "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
-      "metadata": {
-        "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
-      },
-      "source": [
-        "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
+   "metadata": {
+    "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
+   },
+   "source": [
+    "# Lab | Data Aggregation and Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
+   "metadata": {
+    "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset 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.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 first performing data cleaning, formatting, and structuring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "72ce5a09-0ccc-4b68-bca0-49ace641d434",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b42999f9-311f-481e-ae63-40a5577072c5",
-      "metadata": {
-        "id": "b42999f9-311f-481e-ae63-40a5577072c5"
-      },
-      "source": [
-        "## Bonus"
+     "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>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 Open Complaints</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",
+       "    </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>2/18/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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>NaN</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>1/18/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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>NaN</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>2/10/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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",
+       "    </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>1/11/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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",
+       "    </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>1/17/11</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 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           2/18/11         Employed      M  ...   \n",
+       "1     Basic   College           1/18/11       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor           2/10/11         Employed      M  ...   \n",
+       "3  Extended   College           1/11/11         Employed      M  ...   \n",
+       "4   Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n",
+       "\n",
+       "   Number of Open Complaints Number of Policies     Policy Type        Policy  \\\n",
+       "0                        0.0                  9  Corporate Auto  Corporate L3   \n",
+       "1                        0.0                  1   Personal Auto   Personal L3   \n",
+       "2                        0.0                  2   Personal Auto   Personal L3   \n",
+       "3                        0.0                  2  Corporate Auto  Corporate L3   \n",
+       "4                        NaN                  7   Personal Auto   Personal L2   \n",
+       "\n",
+       "   Renew Offer Type  Sales Channel  Total Claim Amount  Vehicle Class  \\\n",
+       "0            Offer3          Agent          292.800000  Four-Door Car   \n",
+       "1            Offer4    Call Center          744.924331  Four-Door Car   \n",
+       "2            Offer3    Call Center          480.000000            SUV   \n",
+       "3            Offer2         Branch          484.013411  Four-Door Car   \n",
+       "4            Offer1         Branch          707.925645  Four-Door Car   \n",
+       "\n",
+       "  Vehicle Size Vehicle Type  \n",
+       "0      Medsize          NaN  \n",
+       "1      Medsize          NaN  \n",
+       "2      Medsize            A  \n",
+       "3      Medsize            A  \n",
+       "4      Medsize          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
       ]
-    },
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd \n",
+    "url = 'https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv'\n",
+    "df = pd.read_csv(url)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
+   "metadata": {
+    "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50"
+   },
+   "source": [
+    "1. Create a new DataFrame that only includes customers who:\n",
+    "   - have a **low total_claim_amount** (e.g., below $1,000),\n",
+    "   - have a response \"Yes\" to the last marketing campaign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1024dca0-b90a-42ae-9f01-c9d506919d4f",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "81ff02c5-6584-4f21-a358-b918697c6432",
-      "metadata": {
-        "id": "81ff02c5-6584-4f21-a358-b918697c6432"
-      },
-      "source": [
-        "5. The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    unnamed:_0 customer       state  customer_lifetime_value response  \\\n",
+      "3            3  XL78013      Oregon             22332.439460      Yes   \n",
+      "8            8  FM55990  California              5989.773931      Yes   \n",
+      "15          15  CW49887  California              4626.801093      Yes   \n",
+      "19          19  NJ54277  California              3746.751625      Yes   \n",
+      "27          27  MQ68407      Oregon              4376.363592      Yes   \n",
+      "\n",
+      "    coverage education effective_to_date employmentstatus gender  ...  \\\n",
+      "3   Extended   College           1/11/11         Employed      M  ...   \n",
+      "8    Premium   College           1/19/11         Employed      M  ...   \n",
+      "15     Basic    Master           1/16/11         Employed      F  ...   \n",
+      "19  Extended   College           2/26/11         Employed      F  ...   \n",
+      "27   Premium  Bachelor           2/28/11         Employed      F  ...   \n",
+      "\n",
+      "    number_of_open_complaints number_of_policies     policy_type  \\\n",
+      "3                         0.0                  2  Corporate Auto   \n",
+      "8                         0.0                  1   Personal Auto   \n",
+      "15                        0.0                  1    Special Auto   \n",
+      "19                        1.0                  1   Personal Auto   \n",
+      "27                        0.0                  1   Personal Auto   \n",
+      "\n",
+      "          policy  renew_offer_type  sales_channel  total_claim_amount  \\\n",
+      "3   Corporate L3            Offer2         Branch          484.013411   \n",
+      "8    Personal L1            Offer2         Branch          739.200000   \n",
+      "15    Special L1            Offer2         Branch          547.200000   \n",
+      "19   Personal L2            Offer2    Call Center           19.575683   \n",
+      "27   Personal L3            Offer2          Agent           60.036683   \n",
+      "\n",
+      "    vehicle_class vehicle_size vehicle_type  \n",
+      "3   Four-Door Car      Medsize            A  \n",
+      "8      Sports Car      Medsize          NaN  \n",
+      "15            SUV      Medsize          NaN  \n",
+      "19   Two-Door Car        Large            A  \n",
+      "27  Four-Door Car      Medsize          NaN  \n",
+      "\n",
+      "[5 rows x 26 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "df.columns = df.columns.str.strip().str.lower().str.replace(\" \", \"_\")\n",
+    "# Step 2: Filter customers\n",
+    "low_claim_responders = df[\n",
+    "    (df['total_claim_amount'] < 1000) &\n",
+    "    (df['response'].str.lower() == 'yes')\n",
+    "].copy()\n",
+    "\n",
+    "# Step 3: View the result\n",
+    "print(low_claim_responders.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
+   "metadata": {
+    "id": "b9be383e-5165-436e-80c8-57d4c757c8c3"
+   },
+   "source": [
+    "2. Using the original Dataframe, analyze:\n",
+    "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
+    "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "ce49099f-2ff7-496b-a19f-8bdfb579b494",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b6aec097-c633-4017-a125-e77a97259cda",
-      "metadata": {
-        "id": "b6aec097-c633-4017-a125-e77a97259cda"
-      },
-      "source": [
-        "6.  Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n",
-        "\n",
-        "*Hint:*\n",
-        "- *To accomplish this, you will first need to group the data by state and month, then count the number of policies sold for each group. Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n",
-        "- *Next, you will select the top 3 states with the highest number of policies sold.*\n",
-        "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
+     "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>Gender</th>\n",
+       "      <th>Avg Monthly Premium</th>\n",
+       "      <th>Avg Customer Lifetime Value</th>\n",
+       "      <th>Avg Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>433.738499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>408.582459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>452.965929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>457.010178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>453.280164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>86.343750</td>\n",
+       "      <td>8247.088702</td>\n",
+       "      <td>429.527942</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Policy Type Gender  Avg Monthly Premium  Avg Customer Lifetime Value  \\\n",
+       "0  Corporate Auto      F            94.301775                  7712.628736   \n",
+       "1  Corporate Auto      M            92.188312                  7944.465414   \n",
+       "2   Personal Auto      F            98.998148                  8339.791842   \n",
+       "3   Personal Auto      M            91.085821                  7448.383281   \n",
+       "4    Special Auto      F            92.314286                  7691.584111   \n",
+       "5    Special Auto      M            86.343750                  8247.088702   \n",
+       "\n",
+       "   Avg Total Claim Amount  \n",
+       "0              433.738499  \n",
+       "1              408.582459  \n",
+       "2              452.965929  \n",
+       "3              457.010178  \n",
+       "4              453.280164  \n",
+       "5              429.527942  "
       ]
-    },
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 1: Filter for customers who responded \"Yes\"\n",
+    "responders = df[df['response'] == 'Yes']\n",
+    "\n",
+    "# Step 2: Group by policy_type and gender, and calculate relevant averages\n",
+    "summary = responders.groupby(['policy_type', 'gender']).agg({\n",
+    "    'monthly_premium_auto': 'mean',\n",
+    "    'customer_lifetime_value': 'mean',\n",
+    "    'total_claim_amount': 'mean'\n",
+    "}).reset_index()\n",
+    "\n",
+    "# Step 3: Rename columns for clarity\n",
+    "summary.columns = [\n",
+    "    'Policy Type', 'Gender',\n",
+    "    'Avg Monthly Premium',\n",
+    "    'Avg Customer Lifetime Value',\n",
+    "    'Avg Total Claim Amount'\n",
+    "]\n",
+    "\n",
+    "summary\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
+   "metadata": {
+    "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0"
+   },
+   "source": [
+    "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "16853f63-001d-4e8e-8a38-b2ef7769b7d2",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
-      "metadata": {
-        "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009"
-      },
-      "source": [
-        "7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
-        "\n",
-        "Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
+     "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>state</th>\n",
+       "      <th>customer_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        state  customer_count\n",
+       "0  California            3552\n",
+       "1      Oregon            2909\n",
+       "2     Arizona            1937\n",
+       "3      Nevada             993\n",
+       "4  Washington             888"
       ]
-    },
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Step 1: Count customers per state\n",
+    "state_counts = df['state'].value_counts().reset_index()\n",
+    "state_counts.columns = ['state', 'customer_count']\n",
+    "\n",
+    "# Step 2: Filter states with more than 500 customers\n",
+    "popular_states = state_counts[state_counts['customer_count'] > 500]\n",
+    "popular_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
+   "metadata": {
+    "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
+   },
+   "source": [
+    "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4a64fc05-fcb4-4734-a190-d02417e68b99",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
-      "metadata": {
-        "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d"
-      },
-      "source": [
-        "External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"
+     "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>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "      <th>median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1904.000852</td>\n",
+       "      <td>73225.95652</td>\n",
+       "      <td>5640.505303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1898.007675</td>\n",
+       "      <td>67907.27050</td>\n",
+       "      <td>5548.031892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>F</td>\n",
+       "      <td>1898.683686</td>\n",
+       "      <td>61850.18803</td>\n",
+       "      <td>5623.611187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>College</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1918.119700</td>\n",
+       "      <td>61134.68307</td>\n",
+       "      <td>6005.847375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2395.570000</td>\n",
+       "      <td>44856.11397</td>\n",
+       "      <td>5332.462694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>2267.604038</td>\n",
+       "      <td>32677.34284</td>\n",
+       "      <td>5577.669457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2144.921535</td>\n",
+       "      <td>55277.44589</td>\n",
+       "      <td>6039.553187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>1940.981221</td>\n",
+       "      <td>83325.38119</td>\n",
+       "      <td>6286.731006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>F</td>\n",
+       "      <td>2417.777032</td>\n",
+       "      <td>51016.06704</td>\n",
+       "      <td>5729.855012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>2272.307310</td>\n",
+       "      <td>50568.25912</td>\n",
+       "      <td>5579.099207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              education gender          min          max       median\n",
+       "0              Bachelor      F  1904.000852  73225.95652  5640.505303\n",
+       "1              Bachelor      M  1898.007675  67907.27050  5548.031892\n",
+       "2               College      F  1898.683686  61850.18803  5623.611187\n",
+       "3               College      M  1918.119700  61134.68307  6005.847375\n",
+       "4                Doctor      F  2395.570000  44856.11397  5332.462694\n",
+       "5                Doctor      M  2267.604038  32677.34284  5577.669457\n",
+       "6  High School or Below      F  2144.921535  55277.44589  6039.553187\n",
+       "7  High School or Below      M  1940.981221  83325.38119  6286.731006\n",
+       "8                Master      F  2417.777032  51016.06704  5729.855012\n",
+       "9                Master      M  2272.307310  50568.25912  5579.099207"
       ]
-    },
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Remove % and convert to float\n",
+    "df['customer_lifetime_value'] = df['customer_lifetime_value'].replace('%', '', regex=True).astype(float)\n",
+    "findings = df.groupby(['education', 'gender'])['customer_lifetime_value'].agg(['min', 'max', 'median']).reset_index()\n",
+    "findings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42999f9-311f-481e-ae63-40a5577072c5",
+   "metadata": {
+    "id": "b42999f9-311f-481e-ae63-40a5577072c5"
+   },
+   "source": [
+    "## Bonus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ff67ec8-617a-4502-ad41-3690ba8e4e6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "5. The marketing team wants to analyze the number of policies sold by state and month. \n",
+    "Present the data in a table where the months are arranged as columns and the states are arranged as rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "ba9a224c-5047-4804-8e01-79a209ad8d4e",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
-      "metadata": {
-        "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
-      },
-      "outputs": [],
-      "source": [
-        "# your code goes here"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "month       January  February  March  April  May  June  July  August  \\\n",
+      "state                                                                  \n",
+      "Arizona        1008       929      0      0    0     0     0       0   \n",
+      "California     1918      1634      0      0    0     0     0       0   \n",
+      "Nevada          551       442      0      0    0     0     0       0   \n",
+      "Oregon         1565      1344      0      0    0     0     0       0   \n",
+      "Washington      463       425      0      0    0     0     0       0   \n",
+      "\n",
+      "month       September  October  November  December  \n",
+      "state                                               \n",
+      "Arizona             0        0         0         0  \n",
+      "California          0        0         0         0  \n",
+      "Nevada              0        0         0         0  \n",
+      "Oregon              0        0         0         0  \n",
+      "Washington          0        0         0         0  \n"
+     ]
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+   ],
+   "source": [
+    "# Parse the date with the correct format to avoid the warning\n",
+    "df['effective_to_date'] = pd.to_datetime(df['effective_to_date'], format='%Y-%m-%d', errors='coerce')\n",
+    "\n",
+    "# Extract the month name\n",
+    "df['month'] = df['effective_to_date'].dt.month_name()\n",
+    "\n",
+    "# Create the pivot table: rows = states, columns = months, values = policy count\n",
+    "policies_by_state_month = pd.pivot_table(\n",
+    "    df,\n",
+    "    index='state',\n",
+    "    columns='month',\n",
+    "    values='policy',  # Replace with a unique customer ID if more accurate\n",
+    "    aggfunc='count',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "# Optional: Reorder columns to follow calendar months\n",
+    "month_order = [\n",
+    "    'January', 'February', 'March', 'April', 'May', 'June',\n",
+    "    'July', 'August', 'September', 'October', 'November', 'December'\n",
+    "]\n",
+    "policies_by_state_month = policies_by_state_month.reindex(columns=month_order, fill_value=0)\n",
+    "\n",
+    "# Display the result\n",
+    "print(policies_by_state_month)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6aec097-c633-4017-a125-e77a97259cda",
+   "metadata": {
+    "id": "b6aec097-c633-4017-a125-e77a97259cda"
+   },
+   "source": [
+    "6.  Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n",
+    "\n",
+    "*Hint:*\n",
+    "- *To accomplish this, you will first need to group the data by state and month, then count the number of policies sold for each group. Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n",
+    "- *Next, you will select the top 3 states with the highest number of policies sold.*\n",
+    "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9759642a-9dc5-4291-b307-caef8e70281b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "state_month_counts = df.groupby(['state', 'month']).size().reset_index(name='policies_sold')\n",
+    "top_states = total_by_state.sort_values(by='policies_sold', ascending=False).head(3)['state']\n",
+    "top_states_data = state_month_counts[state_month_counts['state'].isin(top_states)]\n",
+    "pivot_top_states = top_states_data.pivot_table(index='state', columns='month', values='policies_sold', aggfunc='sum', fill_value=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8db4997b-a2a1-4d1f-83c4-4e3c0da1d5b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "month       January  February  March  April  May  June  July  August  \\\n",
+      "state                                                                  \n",
+      "Arizona        1008       929      0      0    0     0     0       0   \n",
+      "California     1918      1634      0      0    0     0     0       0   \n",
+      "Oregon         1565      1344      0      0    0     0     0       0   \n",
+      "\n",
+      "month       September  October  November  December  \n",
+      "state                                               \n",
+      "Arizona             0        0         0         0  \n",
+      "California          0        0         0         0  \n",
+      "Oregon              0        0         0         0  \n"
+     ]
     }
+   ],
+   "source": [
+    "# 2. Extract the month name\n",
+    "df['month'] = df['effective_to_date'].dt.month_name()\n",
+    "\n",
+    "# 3. Count number of policies sold by state and month\n",
+    "state_month_counts = df.groupby(['state', 'month']).size().reset_index(name='policies_sold')\n",
+    "\n",
+    "# 4. Total policies sold by state\n",
+    "total_by_state = state_month_counts.groupby('state')['policies_sold'].sum().reset_index()\n",
+    "\n",
+    "# 5. Get top 3 states by total policies sold\n",
+    "top_states = total_by_state.sort_values(by='policies_sold', ascending=False).head(3)['state']\n",
+    "\n",
+    "# 6. Filter original grouped data to include only top 3 states\n",
+    "top_states_data = state_month_counts[state_month_counts['state'].isin(top_states)]\n",
+    "\n",
+    "# 7. Pivot for a cleaner format (rows = states, columns = months)\n",
+    "pivot_top_states = top_states_data.pivot_table(\n",
+    "    index='state',\n",
+    "    columns='month',\n",
+    "    values='policies_sold',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "pivot_top_states = pivot_top_states.reindex(columns=month_order, fill_value=0)\n",
+    "\n",
+    "# 9. Display result\n",
+    "print(pivot_top_states)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
+   "metadata": {
+    "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009"
+   },
+   "source": [
+    "7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
+    "\n",
+    "Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
+   "metadata": {
+    "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d"
+   },
+   "source": [
+    "External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [],
+   "source": [
+    "# your code goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }