diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..51f4ec2 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,1341 @@
 {
-  "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."
-      ]
-    },
+ "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."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42999f9-311f-481e-ae63-40a5577072c5",
+   "metadata": {
+    "id": "b42999f9-311f-481e-ae63-40a5577072c5"
+   },
+   "source": [
+    "## Bonus"
+   ]
+  },
+  {
+   "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."
+   ]
+  },
+  {
+   "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": "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": 11,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [
     {
-      "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."
+     "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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\")\n",
+    "\n",
+    "df.columns = df.columns.str.lower().str.replace(\" \", \"_\")\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "f8f41ff1-60bf-4b89-89b3-468bd4b53f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['effective_to_date'] = pd.to_datetime(\n",
+    "    df['effective_to_date'],\n",
+    "    format='%d/%m/%y',\n",
+    "    errors='coerce'\n",
+    ")\n",
+    "\n",
+    "df['month'] = df['effective_to_date'].dt.month_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0edb099f-d393-431e-a32d-124056b58d47",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "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_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>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>January</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FM55990</td>\n",
+       "      <td>California</td>\n",
+       "      <td>5989.773931</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-19</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>739.200000</td>\n",
+       "      <td>Sports Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>January</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>4626.801093</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>2011-01-16</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>Special L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>547.200000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>January</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>NJ54277</td>\n",
+       "      <td>California</td>\n",
+       "      <td>3746.751625</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-02-26</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>19.575683</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Large</td>\n",
+       "      <td>A</td>\n",
+       "      <td>February</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>27</td>\n",
+       "      <td>MQ68407</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>4376.363592</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-02-28</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>60.036683</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>February</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",
+       "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        2011-01-11         Employed      M  ...   \n",
+       "8    Premium   College        2011-01-19         Employed      M  ...   \n",
+       "15     Basic    Master        2011-01-16         Employed      F  ...   \n",
+       "19  Extended   College        2011-02-26         Employed      F  ...   \n",
+       "27   Premium  Bachelor        2011-02-28         Employed      F  ...   \n",
+       "\n",
+       "    number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "3                    2  Corporate Auto  Corporate L3            Offer2   \n",
+       "8                    1   Personal Auto   Personal L1            Offer2   \n",
+       "15                   1    Special Auto    Special L1            Offer2   \n",
+       "19                   1   Personal Auto   Personal L2            Offer2   \n",
+       "27                   1   Personal Auto   Personal L3            Offer2   \n",
+       "\n",
+       "    sales_channel  total_claim_amount  vehicle_class  vehicle_size  \\\n",
+       "3          Branch          484.013411  Four-Door Car       Medsize   \n",
+       "8          Branch          739.200000     Sports Car       Medsize   \n",
+       "15         Branch          547.200000            SUV       Medsize   \n",
+       "19    Call Center           19.575683   Two-Door Car         Large   \n",
+       "27          Agent           60.036683  Four-Door Car       Medsize   \n",
+       "\n",
+       "   vehicle_type     month  \n",
+       "3             A   January  \n",
+       "8           NaN   January  \n",
+       "15          NaN   January  \n",
+       "19            A  February  \n",
+       "27          NaN  February  \n",
+       "\n",
+       "[5 rows x 27 columns]"
       ]
-    },
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "low_claim_yes = df[(df['total_claim_amount'] < 1000) & (df['response'].str.lower() == 'yes')].copy()\n",
+    "low_claim_yes.shape\n",
+    "low_claim_yes.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "e2bad9dd-9780-4fc8-ba5a-f3ef923e39c3",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "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",
+       "      <th>monthly_premium_auto</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Corporate Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>7712.63</td>\n",
+       "      <td>94.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7944.47</td>\n",
+       "      <td>92.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>8339.79</td>\n",
+       "      <td>99.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7448.38</td>\n",
+       "      <td>91.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>7691.58</td>\n",
+       "      <td>92.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>8247.09</td>\n",
+       "      <td>86.34</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       customer_lifetime_value  monthly_premium_auto\n",
+       "policy_type    gender                                               \n",
+       "Corporate Auto F                       7712.63                 94.30\n",
+       "               M                       7944.47                 92.19\n",
+       "Personal Auto  F                       8339.79                 99.00\n",
+       "               M                       7448.38                 91.09\n",
+       "Special Auto   F                       7691.58                 92.31\n",
+       "               M                       8247.09                 86.34"
       ]
-    },
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "responders = df[df['response'].str.lower() == 'yes'].copy()\n",
+    "\n",
+    "pivot_avg = pd.pivot_table(\n",
+    "    responders,\n",
+    "    values=['monthly_premium_auto', 'customer_lifetime_value'],\n",
+    "    index=['policy_type', 'gender'],\n",
+    "    aggfunc='mean'\n",
+    ").round(2)\n",
+    "\n",
+    "pivot_avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "39e37279-226f-46c0-a051-7fe53f3bd4ad",
+   "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>policy_type</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>avg_monthly_premium</th>\n",
+       "      <th>avg_clv</th>\n",
+       "      <th>avg_total_claim</th>\n",
+       "      <th>median_total_claim</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>94.30</td>\n",
+       "      <td>7712.63</td>\n",
+       "      <td>433.74</td>\n",
+       "      <td>420.04</td>\n",
+       "      <td>169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>92.19</td>\n",
+       "      <td>7944.47</td>\n",
+       "      <td>408.58</td>\n",
+       "      <td>364.80</td>\n",
+       "      <td>154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>99.00</td>\n",
+       "      <td>8339.79</td>\n",
+       "      <td>452.97</td>\n",
+       "      <td>424.33</td>\n",
+       "      <td>540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>91.09</td>\n",
+       "      <td>7448.38</td>\n",
+       "      <td>457.01</td>\n",
+       "      <td>412.80</td>\n",
+       "      <td>536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>92.31</td>\n",
+       "      <td>7691.58</td>\n",
+       "      <td>453.28</td>\n",
+       "      <td>420.04</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>86.34</td>\n",
+       "      <td>8247.09</td>\n",
+       "      <td>429.53</td>\n",
+       "      <td>345.60</td>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      policy_type gender  avg_monthly_premium  avg_clv  avg_total_claim  \\\n",
+       "0  Corporate Auto      F                94.30  7712.63           433.74   \n",
+       "1  Corporate Auto      M                92.19  7944.47           408.58   \n",
+       "2   Personal Auto      F                99.00  8339.79           452.97   \n",
+       "3   Personal Auto      M                91.09  7448.38           457.01   \n",
+       "4    Special Auto      F                92.31  7691.58           453.28   \n",
+       "5    Special Auto      M                86.34  8247.09           429.53   \n",
+       "\n",
+       "   median_total_claim  count  \n",
+       "0              420.04    169  \n",
+       "1              364.80    154  \n",
+       "2              424.33    540  \n",
+       "3              412.80    536  \n",
+       "4              420.04     35  \n",
+       "5              345.60     32  "
       ]
-    },
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "comparison = (\n",
+    "    responders\n",
+    "    .groupby(['policy_type', 'gender'])\n",
+    "    .agg(\n",
+    "        avg_monthly_premium = ('monthly_premium_auto', 'mean'),\n",
+    "        avg_clv = ('customer_lifetime_value', 'mean'),\n",
+    "        avg_total_claim = ('total_claim_amount', 'mean'),\n",
+    "        median_total_claim = ('total_claim_amount', 'median'),\n",
+    "        count = ('customer', 'count')\n",
+    "    )\n",
+    "    .round(2)\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "eeef8e0b-30ac-45d8-8f86-9d1809eee4d3",
+   "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."
+     "data": {
+      "text/plain": [
+       "(        state  n_customers\n",
+       " 1  California         3150\n",
+       " 3      Oregon         2601\n",
+       " 0     Arizona         1703\n",
+       " 2      Nevada          882\n",
+       " 4  Washington          798,\n",
+       "         state  n_customers\n",
+       " 1  California         3150\n",
+       " 3      Oregon         2601\n",
+       " 0     Arizona         1703\n",
+       " 2      Nevada          882\n",
+       " 4  Washington          798)"
       ]
-    },
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers_by_state = (\n",
+    "    df.groupby('state')['customer'].nunique()\n",
+    "    .reset_index(name='n_customers')\n",
+    "    .sort_values('n_customers', ascending=False)\n",
+    ")\n",
+    "\n",
+    "customers_by_state_top = customers_by_state[customers_by_state['n_customers'] > 500]\n",
+    "\n",
+    "customers_by_state, customers_by_state_top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "052f3fee-7614-4ec7-bf4e-6f748d1b2b5c",
+   "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>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</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>73225.96</td>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>5640.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>5548.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>F</td>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>5623.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>College</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>6005.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>5332.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>5577.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>6039.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>6286.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>F</td>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>5729.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>50568.26</td>\n",
+       "      <td>2272.31</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              education gender       max      min   median\n",
+       "0              Bachelor      F  73225.96  1904.00  5640.51\n",
+       "1              Bachelor      M  67907.27  1898.01  5548.03\n",
+       "2               College      F  61850.19  1898.68  5623.61\n",
+       "3               College      M  61134.68  1918.12  6005.85\n",
+       "4                Doctor      F  44856.11  2395.57  5332.46\n",
+       "5                Doctor      M  32677.34  2267.60  5577.67\n",
+       "6  High School or Below      F  55277.45  2144.92  6039.55\n",
+       "7  High School or Below      M  83325.38  1940.98  6286.73\n",
+       "8                Master      F  51016.07  2417.78  5729.86\n",
+       "9                Master      M  50568.26  2272.31  5579.10"
       ]
-    },
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clv_stats = (\n",
+    "    df.groupby(['education', 'gender'])['customer_lifetime_value']\n",
+    "    .agg(['max', 'min', 'median'])\n",
+    "    .round(2)\n",
+    "    .reset_index()\n",
+    ")\n",
+    "\n",
+    "clv_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "a143d941-c908-4151-be82-2b5f671c2ec0",
+   "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>month</th>\n",
+       "      <th>January</th>\n",
+       "      <th>February</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>1008.0</td>\n",
+       "      <td>929.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>1918.0</td>\n",
+       "      <td>1634.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>551.0</td>\n",
+       "      <td>442.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>1565.0</td>\n",
+       "      <td>1344.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>463.0</td>\n",
+       "      <td>425.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "month       January  February\n",
+       "state                        \n",
+       "Arizona      1008.0     929.0\n",
+       "California   1918.0    1634.0\n",
+       "Nevada        551.0     442.0\n",
+       "Oregon       1565.0    1344.0\n",
+       "Washington    463.0     425.0"
       ]
-    },
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "policies_state_month = (\n",
+    "    df.groupby(['state', 'month'])['policy']\n",
+    "    .count()\n",
+    "    .reset_index(name='n_policies')\n",
+    ")\n",
+    "\n",
+    "policies_pivot = policies_state_month.pivot_table(\n",
+    "    index='state', columns='month', values='n_policies', fill_value=0\n",
+    ")\n",
+    "\n",
+    "month_order = [\n",
+    "    'January', 'February', 'March', 'April', 'May', 'June',\n",
+    "    'July', 'August', 'September', 'October', 'November', 'December'\n",
+    "]\n",
+    "policies_pivot = policies_pivot.reindex(columns=[m for m in month_order if m in policies_pivot.columns])\n",
+    "\n",
+    "policies_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a712e6a0-b0e3-450d-8220-f758d4da3b9b",
+   "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>month</th>\n",
+       "      <th>January</th>\n",
+       "      <th>February</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>1008.0</td>\n",
+       "      <td>929.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>1918.0</td>\n",
+       "      <td>1634.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>1565.0</td>\n",
+       "      <td>1344.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "month       January  February\n",
+       "state                        \n",
+       "Arizona      1008.0     929.0\n",
+       "California   1918.0    1634.0\n",
+       "Oregon       1565.0    1344.0"
       ]
-    },
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state_totals = (\n",
+    "    policies_state_month.groupby('state')['n_policies']\n",
+    "    .sum()\n",
+    "    .reset_index()\n",
+    "    .sort_values('n_policies', ascending=False)\n",
+    ")\n",
+    "\n",
+    "top3_states = state_totals.head(3)['state'].tolist()\n",
+    "\n",
+    "top3_pivot = policies_state_month[policies_state_month['state'].isin(top3_states)].pivot_table(\n",
+    "    index='state', columns='month', values='n_policies', fill_value=0\n",
+    ")\n",
+    "\n",
+    "top3_pivot = top3_pivot.reindex(columns=[m for m in month_order if m in top3_pivot.columns])\n",
+    "\n",
+    "top3_pivot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4df122a6-d195-4177-9843-5905786b889c",
+   "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"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>response_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>11.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>11.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>10.98</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  sales_channel  response_rate\n",
+       "0         Agent          19.07\n",
+       "3           Web          11.71\n",
+       "1        Branch          11.38\n",
+       "2   Call Center          10.98"
       ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "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": [
+    "channel_response = (\n",
+    "    df.groupby('sales_channel')['response']\n",
+    "    .apply(lambda s: (s.str.lower() == 'yes').sum() / s.count())\n",
+    "    .reset_index(name='response_rate')\n",
+    ")\n",
+    "\n",
+    "channel_response['response_rate'] = (channel_response['response_rate']*100).round(2)\n",
+    "channel_response.sort_values('response_rate', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbd3029e-60e4-49f0-9140-fe935de8a153",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
-  "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": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }