diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fff3ae5..aeb1b10 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,161 +1,1587 @@
 {
-  "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 have a total_claim_amount greater than $1,000 and have a response of \"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 the average total_claim_amount by each policy type and gender for customers who have responded \"Yes\" to the last marketing campaign. 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": "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 have a total_claim_amount greater than $1,000 and have a response of \"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 the average total_claim_amount by each policy type and gender for customers who have responded \"Yes\" to the last marketing campaign. Write your conclusions."
+   ]
+  },
+  {
+   "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": 47,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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]"
       ]
-    },
-    {
-      "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."
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "\n",
+    "df_1 = pd.read_csv(url)\n",
+    "\n",
+    "df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "2ebb0ee8-cf2d-4afa-abbb-81600375cc20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "       Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+      "189           189  OK31456  California             11009.130490      Yes   \n",
+      "236           236  YJ16163      Oregon             11009.130490      Yes   \n",
+      "419           419  GW43195      Oregon             25807.063000      Yes   \n",
+      "442           442  IP94270     Arizona             13736.132500      Yes   \n",
+      "587           587  FJ28407  California              5619.689084      Yes   \n",
+      "...           ...      ...         ...                      ...      ...   \n",
+      "10351       10351  FN44127      Oregon              3508.569533      Yes   \n",
+      "10373       10373  XZ64172      Oregon             10963.957230      Yes   \n",
+      "10487       10487  IX60941      Oregon              3508.569533      Yes   \n",
+      "10565       10565  QO62792      Oregon              7840.165778      Yes   \n",
+      "10708       10708  CK39096      Oregon              5619.689084      Yes   \n",
+      "\n",
+      "       Coverage             Education Effective To Date EmploymentStatus  \\\n",
+      "189     Premium              Bachelor           1/24/11         Employed   \n",
+      "236     Premium              Bachelor           1/24/11         Employed   \n",
+      "419    Extended               College           2/13/11         Employed   \n",
+      "442     Premium                Master           2/13/11         Disabled   \n",
+      "587     Premium  High School or Below           1/26/11       Unemployed   \n",
+      "...         ...                   ...               ...              ...   \n",
+      "10351  Extended               College            1/5/11    Medical Leave   \n",
+      "10373   Premium  High School or Below            2/8/11         Employed   \n",
+      "10487  Extended               College            1/5/11    Medical Leave   \n",
+      "10565  Extended               College           1/14/11         Employed   \n",
+      "10708   Premium  High School or Below           1/26/11       Unemployed   \n",
+      "\n",
+      "      Gender  ...  Number of Open Complaints Number of Policies  \\\n",
+      "189        F  ...                        0.0                  1   \n",
+      "236        F  ...                        0.0                  1   \n",
+      "419        F  ...                        1.0                  2   \n",
+      "442        F  ...                        0.0                  8   \n",
+      "587        M  ...                        0.0                  1   \n",
+      "...      ...  ...                        ...                ...   \n",
+      "10351      M  ...                        1.0                  1   \n",
+      "10373      M  ...                        0.0                  1   \n",
+      "10487      M  ...                        1.0                  1   \n",
+      "10565      M  ...                        2.0                  1   \n",
+      "10708      M  ...                        0.0                  1   \n",
+      "\n",
+      "          Policy Type        Policy  Renew Offer Type  Sales Channel  \\\n",
+      "189    Corporate Auto  Corporate L3            Offer2          Agent   \n",
+      "236      Special Auto    Special L3            Offer2          Agent   \n",
+      "419     Personal Auto   Personal L2            Offer1         Branch   \n",
+      "442     Personal Auto   Personal L2            Offer1            Web   \n",
+      "587     Personal Auto   Personal L1            Offer2            Web   \n",
+      "...               ...           ...               ...            ...   \n",
+      "10351   Personal Auto   Personal L2            Offer2         Branch   \n",
+      "10373  Corporate Auto  Corporate L2            Offer1          Agent   \n",
+      "10487   Personal Auto   Personal L3            Offer2         Branch   \n",
+      "10565   Personal Auto   Personal L3            Offer2          Agent   \n",
+      "10708   Personal Auto   Personal L3            Offer2            Web   \n",
+      "\n",
+      "       Total Claim Amount  Vehicle Class Vehicle Size Vehicle Type  \n",
+      "189           1358.400000     Luxury Car      Medsize          NaN  \n",
+      "236           1358.400000     Luxury Car      Medsize            A  \n",
+      "419           1027.200000     Luxury Car        Small            A  \n",
+      "442           1261.319869            SUV      Medsize            A  \n",
+      "587           1027.000029            SUV      Medsize            A  \n",
+      "...                   ...            ...          ...          ...  \n",
+      "10351         1176.278800  Four-Door Car        Small          NaN  \n",
+      "10373         1324.800000     Luxury SUV      Medsize          NaN  \n",
+      "10487         1176.278800  Four-Door Car        Small          NaN  \n",
+      "10565         1008.000000            NaN          NaN          NaN  \n",
+      "10708         1027.000029            SUV      Medsize            A  \n",
+      "\n",
+      "[67 rows x 26 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "filtered_df = df_1[(df_1['Total Claim Amount'] > 1000) & (df_1['Response'] == 'Yes')]\n",
+    "print(filtered_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9e843c75-7350-4b40-b52e-3b96c444c391",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Aggregated Data:\n",
+      "     Response  Total Claim Amount  Unnamed: 0  \\\n",
+      "0          No            0.099007        3663   \n",
+      "1          No            0.382107        6869   \n",
+      "2          No            0.423310       13169   \n",
+      "3          No            0.517753        7567   \n",
+      "4          No            0.769185       10529   \n",
+      "...       ...                 ...         ...   \n",
+      "5161      Yes         1261.319869       31890   \n",
+      "5162      Yes         1294.700423       44458   \n",
+      "5163      Yes         1300.800000       34595   \n",
+      "5164      Yes         1324.800000       46968   \n",
+      "5165      Yes         1358.400000       28239   \n",
+      "\n",
+      "                                               Customer  \\\n",
+      "0                                               EY74093   \n",
+      "1                                        PN21042PN21042   \n",
+      "2                                        IS30186IS30186   \n",
+      "3                                               HG65722   \n",
+      "4                                               WW74234   \n",
+      "...                                                 ...   \n",
+      "5161         IP94270ZA45956TC99043MO21402MC30750FD73388   \n",
+      "5162         ZW93288CG15505UP29839YU86361IV54766GD55093   \n",
+      "5163         MA15172UY18770QW47320ZR79373DT77901YP53257   \n",
+      "5164  JC11405WP58340TQ84956NZ60700XT93203WP58340XZ64172   \n",
+      "5165  OK31456YJ16163TA66375FH77504TY31217AJ89108TY31217   \n",
+      "\n",
+      "                                                  State  \\\n",
+      "0                                                Nevada   \n",
+      "1                                        ArizonaArizona   \n",
+      "2                                  CaliforniaCalifornia   \n",
+      "3                                                Oregon   \n",
+      "4                                               Arizona   \n",
+      "...                                                 ...   \n",
+      "5161     ArizonaCaliforniaCaliforniaOregonOregonArizona   \n",
+      "5162  CaliforniaCaliforniaNevadaWashingtonOregonCali...   \n",
+      "5163  CaliforniaCaliforniaCaliforniaArizonaOregonOregon   \n",
+      "5164  OregonArizonaCaliforniaWashingtonCaliforniaAri...   \n",
+      "5165  CaliforniaOregonOregonCaliforniaWashingtonCali...   \n",
+      "\n",
+      "      Customer Lifetime Value  \\\n",
+      "0                 5004.135361   \n",
+      "1                 9077.695636   \n",
+      "2                12372.995662   \n",
+      "3                12819.102890   \n",
+      "4                 3969.433177   \n",
+      "...                       ...   \n",
+      "5161             82416.795000   \n",
+      "5162            114965.939640   \n",
+      "5163             61078.302240   \n",
+      "5164             76747.700610   \n",
+      "5165             77063.913430   \n",
+      "\n",
+      "                                               Coverage  \\\n",
+      "0                                                 Basic   \n",
+      "1                                            BasicBasic   \n",
+      "2                                      ExtendedExtended   \n",
+      "3                                               Premium   \n",
+      "4                                                 Basic   \n",
+      "...                                                 ...   \n",
+      "5161         PremiumPremiumPremiumPremiumPremiumPremium   \n",
+      "5162   ExtendedExtendedExtendedExtendedExtendedExtended   \n",
+      "5163         PremiumPremiumPremiumPremiumPremiumPremium   \n",
+      "5164  PremiumPremiumPremiumPremiumPremiumPremiumPremium   \n",
+      "5165  PremiumPremiumPremiumPremiumPremiumPremiumPremium   \n",
+      "\n",
+      "                                              Education  \\\n",
+      "0                                  High School or Below   \n",
+      "1                                      BachelorBachelor   \n",
+      "2                                        CollegeCollege   \n",
+      "3                                                Doctor   \n",
+      "4                                               College   \n",
+      "...                                                 ...   \n",
+      "5161               MasterMasterMasterMasterMasterMaster   \n",
+      "5162               MasterMasterMasterMasterMasterMaster   \n",
+      "5163   BachelorBachelorBachelorBachelorBachelorBachelor   \n",
+      "5164  High School or BelowHigh School or BelowHigh S...   \n",
+      "5165  BachelorBachelorBachelorBachelorBachelorBachel...   \n",
+      "\n",
+      "                                      Effective To Date  \\\n",
+      "0                                               2/21/11   \n",
+      "1                                        2/17/112/17/11   \n",
+      "2                                        1/31/111/31/11   \n",
+      "3                                               1/25/11   \n",
+      "4                                                2/5/11   \n",
+      "...                                                 ...   \n",
+      "5161         2/13/112/13/112/13/112/13/112/13/112/13/11   \n",
+      "5162         2/20/112/20/112/20/112/20/112/20/112/20/11   \n",
+      "5163               1/5/111/5/111/5/111/5/111/5/111/5/11   \n",
+      "5164         2/8/112/8/112/8/112/8/112/8/112/8/112/8/11   \n",
+      "5165  1/24/111/24/111/24/111/24/111/24/111/24/111/24/11   \n",
+      "\n",
+      "                                       EmploymentStatus  ...  \\\n",
+      "0                                              Employed  ...   \n",
+      "1                                      EmployedEmployed  ...   \n",
+      "2                                      EmployedEmployed  ...   \n",
+      "3                                              Disabled  ...   \n",
+      "4                                              Employed  ...   \n",
+      "...                                                 ...  ...   \n",
+      "5161   DisabledDisabledDisabledDisabledDisabledDisabled  ...   \n",
+      "5162   EmployedEmployedEmployedEmployedEmployedEmployed  ...   \n",
+      "5163         RetiredRetiredRetiredRetiredRetiredRetired  ...   \n",
+      "5164  EmployedEmployedEmployedEmployedEmployedEmploy...  ...   \n",
+      "5165  EmployedEmployedEmployedEmployedEmployedEmploy...  ...   \n",
+      "\n",
+      "     Months Since Policy Inception  Number of Open Complaints  \\\n",
+      "0                               93                        0.0   \n",
+      "1                              124                        0.0   \n",
+      "2                               22                        0.0   \n",
+      "3                                4                        3.0   \n",
+      "4                               29                        0.0   \n",
+      "...                            ...                        ...   \n",
+      "5161                           474                        0.0   \n",
+      "5162                           354                        0.0   \n",
+      "5163                           240                        0.0   \n",
+      "5164                           637                        0.0   \n",
+      "5165                           301                        0.0   \n",
+      "\n",
+      "     Number of Policies                                        Policy Type  \\\n",
+      "0                     8                                     Corporate Auto   \n",
+      "1                     2                         Personal AutoPersonal Auto   \n",
+      "2                     6                         Personal AutoPersonal Auto   \n",
+      "3                     3                                      Personal Auto   \n",
+      "4                     1                                      Personal Auto   \n",
+      "...                 ...                                                ...   \n",
+      "5161                 48  Personal AutoPersonal AutoPersonal AutoPersona...   \n",
+      "5162                 42  Personal AutoPersonal AutoPersonal AutoPersona...   \n",
+      "5163                  6  Personal AutoPersonal AutoPersonal AutoPersona...   \n",
+      "5164                  7  Personal AutoPersonal AutoCorporate AutoPerson...   \n",
+      "5165                  7  Corporate AutoSpecial AutoCorporate AutoPerson...   \n",
+      "\n",
+      "                                                 Policy  \\\n",
+      "0                                          Corporate L1   \n",
+      "1                                Personal L3Personal L3   \n",
+      "2                                Personal L2Personal L2   \n",
+      "3                                           Personal L2   \n",
+      "4                                           Personal L3   \n",
+      "...                                                 ...   \n",
+      "5161  Personal L2Personal L2Personal L2Personal L3Pe...   \n",
+      "5162  Personal L1Personal L3Personal L1Personal L1Pe...   \n",
+      "5163  Personal L2Personal L3Personal L1Personal L3Pe...   \n",
+      "5164  Personal L3Personal L2Corporate L3Personal L2P...   \n",
+      "5165  Corporate L3Special L3Corporate L2Personal L3P...   \n",
+      "\n",
+      "                                Renew Offer Type  \\\n",
+      "0                                         Offer4   \n",
+      "1                                   Offer1Offer1   \n",
+      "2                                   Offer2Offer2   \n",
+      "3                                         Offer1   \n",
+      "4                                         Offer1   \n",
+      "...                                          ...   \n",
+      "5161        Offer1Offer1Offer1Offer1Offer1Offer1   \n",
+      "5162        Offer2Offer2Offer2Offer2Offer2Offer2   \n",
+      "5163        Offer1Offer1Offer1Offer1Offer1Offer1   \n",
+      "5164  Offer1Offer1Offer1Offer1Offer1Offer1Offer1   \n",
+      "5165  Offer2Offer2Offer2Offer2Offer2Offer2Offer2   \n",
+      "\n",
+      "                            Sales Channel  \\\n",
+      "0                                   Agent   \n",
+      "1                  Call CenterCall Center   \n",
+      "2                              AgentAgent   \n",
+      "3                                  Branch   \n",
+      "4                                   Agent   \n",
+      "...                                   ...   \n",
+      "5161                   WebWebWebWebWebWeb   \n",
+      "5162       AgentAgentAgentAgentAgentAgent   \n",
+      "5163       AgentAgentAgentAgentAgentAgent   \n",
+      "5164  AgentAgentAgentAgentAgentAgentAgent   \n",
+      "5165  AgentAgentAgentAgentAgentAgentAgent   \n",
+      "\n",
+      "                                          Vehicle Class  \\\n",
+      "0                                         Four-Door Car   \n",
+      "1                                  Sports CarSports Car   \n",
+      "2                                         Four-Door Car   \n",
+      "3                                                   SUV   \n",
+      "4                                                   SUV   \n",
+      "...                                                 ...   \n",
+      "5161                                 SUVSUVSUVSUVSUVSUV   \n",
+      "5162  Luxury SUVLuxury SUVLuxury SUVLuxury SUVLuxury...   \n",
+      "5163  Luxury SUVLuxury SUVLuxury SUVLuxury SUVLuxury...   \n",
+      "5164  Luxury SUVLuxury SUVLuxury SUVLuxury SUVLuxury...   \n",
+      "5165  Luxury CarLuxury CarLuxury CarLuxury CarLuxury...   \n",
+      "\n",
+      "                                           Vehicle Size Vehicle Type  \n",
+      "0                                               Medsize            A  \n",
+      "1                                        MedsizeMedsize            0  \n",
+      "2                                               Medsize            A  \n",
+      "3                                               Medsize            0  \n",
+      "4                                               Medsize            A  \n",
+      "...                                                 ...          ...  \n",
+      "5161         MedsizeMedsizeMedsizeMedsizeMedsizeMedsize           AA  \n",
+      "5162         MedsizeMedsizeMedsizeMedsizeMedsizeMedsize          AAA  \n",
+      "5163         MedsizeMedsizeMedsizeMedsizeMedsizeMedsize           AA  \n",
+      "5164  MedsizeMedsizeMedsizeMedsizeMedsizeMedsizeMedsize         AAAA  \n",
+      "5165         MedsizeMedsizeMedsizeMedsizeMedsizeMedsize       AAAAAA  \n",
+      "\n",
+      "[5166 rows x 26 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "aggregated = df_1.groupby(['Response', 'Total Claim Amount']).sum().reset_index()\n",
+    "\n",
+    "print(\"Aggregated Data:\")\n",
+    "print(aggregated)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "c6932a21-89cf-4bcb-b4be-d8ea66722ef6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conditional_1 = df_1[\"Total Claim Amount\"] > 1000\n",
+    "conditional_2 = df_1[\"Response\"] == \"Yes\"\n",
+    "\n",
+    "df_1 = [conditional_1 & conditional_2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "1cf24d63-4110-4449-b69d-866648ea75c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "list indices must be integers or slices, not str",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[45], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m avg_claim \u001b[38;5;241m=\u001b[39m df_1[df_1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mResponse\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYes\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPolicy Type\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Claim Amount\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(avg_claim)\n",
+      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
+     ]
+    }
+   ],
+   "source": [
+    "avg_claim = df_1[df_1['Response'] == 'Yes'].groupby(['Policy Type', 'Gender'])['Total Claim Amount'].mean()\n",
+    "print(avg_claim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "695ae159-95a6-4df1-866c-db3fe243a596",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0        False\n",
+       " 1        False\n",
+       " 2        False\n",
+       " 3        False\n",
+       " 4        False\n",
+       "          ...  \n",
+       " 10905    False\n",
+       " 10906    False\n",
+       " 10907    False\n",
+       " 10908    False\n",
+       " 10909    False\n",
+       " Length: 10910, dtype: bool]"
       ]
-    },
-    {
-      "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."
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "fee80761-57dc-45ba-87a6-54b6756f997c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'groupby'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[65], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_1\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mResponse\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPolicy Type\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m\"\u001b[39m])[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTotal Claim Amount\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'groupby'"
+     ]
+    }
+   ],
+   "source": [
+    "df_1.groupby([\"Response\", \"Policy Type\", \"Gender\"])[\"Total Claim Amount\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "5d194915-3026-49e5-bf7b-9d4506079947",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'groupby'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[41], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m customers_by_state \u001b[38;5;241m=\u001b[39m df_1\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m      2\u001b[0m filtered_states \u001b[38;5;241m=\u001b[39m customers_by_state[customers_by_state \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m500\u001b[39m]\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(filtered_states)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'groupby'"
+     ]
+    }
+   ],
+   "source": [
+    "customers_by_state = df_1.groupby('State').size()\n",
+    "filtered_states = customers_by_state[customers_by_state > 500]\n",
+    "print(filtered_states)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "684d1e53-8a87-4620-bdc6-a2995637387c",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "list indices must be integers or slices, not str",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[75], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_1[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mfilter()\n",
+      "\u001b[1;31mTypeError\u001b[0m: list indices must be integers or slices, not str"
+     ]
+    }
+   ],
+   "source": [
+    "df_1[\"State\"].filter()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2dd5abc4-926a-47bc-84da-b5bb56f7afaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.groupby([\"Pclass\", \"Sex\"]).agg({\"Fare\": [\"min\", \"mean\", \"max\"]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "fee9149d-71cd-4627-b12b-41d4e4e561b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'groupby'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[39], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m clv_stats \u001b[38;5;241m=\u001b[39m df_1\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEducation\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m])[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCustomer Lifetime Value\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39magg([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmedian\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(clv_stats)\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'groupby'"
+     ]
+    }
+   ],
+   "source": [
+    "clv_stats = df_1.groupby(['Education', 'Gender'])['Customer Lifetime Value'].agg(['max', 'min', 'median'])\n",
+    "print(clv_stats)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "f601f7c4-ffd4-4636-ad85-748d2623bdf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'list' object has no attribute 'groupby'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[79], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df_1\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEducation\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39magg({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCustomer Lifetime Value\u001b[39m\u001b[38;5;124m\"\u001b[39m: [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m\"\u001b[39m]})\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'groupby'"
+     ]
+    }
+   ],
+   "source": [
+    "df_1.groupby([\"Gender\", \"Education\"]).agg({\"Customer Lifetime Value\": [\"min\", \"mean\", \"max\"]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7c02315b-3c66-4f41-af19-ae7cc08e9a5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>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]"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "b42999f9-311f-481e-ae63-40a5577072c5",
-      "metadata": {
-        "id": "b42999f9-311f-481e-ae63-40a5577072c5"
-      },
-      "source": [
-        "## Bonus"
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8100f10-a688-4bf7-8089-9935de26ea33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Bonus: Analyze the number of policies sold by state and month and unstack"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "f3c3ffc3-0774-4655-b934-24a3433c6c5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2'"
       ]
-    },
-    {
-      "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."
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1[\"Effective To Date\"][0][0:1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "7475244d-474a-4ca9-8b50-4140a7f4a919",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        2\n",
+       "1        1\n",
+       "2        2\n",
+       "3        1\n",
+       "4        1\n",
+       "        ..\n",
+       "10905    1\n",
+       "10906    1\n",
+       "10907    2\n",
+       "10908    2\n",
+       "10909    1\n",
+       "Name: Month, Length: 10910, dtype: object"
       ]
-    },
-    {
-      "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.*"
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1['Month'] = df_1['Effective To Date'].str[:1]\n",
+    "df_1[\"Month\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "67a8cff1-e28e-4ffb-b5d1-a895eeefe243",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Customer</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Response</th>\n",
+       "      <th>Coverage</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Effective To Date</th>\n",
+       "      <th>EmploymentStatus</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Policy</th>\n",
+       "      <th>Renew Offer Type</th>\n",
+       "      <th>Sales Channel</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Vehicle Size</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "      <th>Month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/18/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/18/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LZ68649</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>14947.917300</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/10/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>480.000000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/11/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/17/11</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "2           2  LZ68649  Washington             14947.917300       No   \n",
+       "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4           4  QA50777      Oregon              9025.067525       No   \n",
+       "\n",
+       "   Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+       "0     Basic   College           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 Policies     Policy Type        Policy  Renew Offer Type  \\\n",
+       "0                   9  Corporate Auto  Corporate L3            Offer3   \n",
+       "1                   1   Personal Auto   Personal L3            Offer4   \n",
+       "2                   2   Personal Auto   Personal L3            Offer3   \n",
+       "3                   2  Corporate Auto  Corporate L3            Offer2   \n",
+       "4                   7   Personal Auto   Personal L2            Offer1   \n",
+       "\n",
+       "   Sales Channel  Total Claim Amount  Vehicle Class  Vehicle Size  \\\n",
+       "0          Agent          292.800000  Four-Door Car       Medsize   \n",
+       "1    Call Center          744.924331  Four-Door Car       Medsize   \n",
+       "2    Call Center          480.000000            SUV       Medsize   \n",
+       "3         Branch          484.013411  Four-Door Car       Medsize   \n",
+       "4         Branch          707.925645  Four-Door Car       Medsize   \n",
+       "\n",
+       "  Vehicle Type Month  \n",
+       "0          NaN     2  \n",
+       "1          NaN     1  \n",
+       "2            A     2  \n",
+       "3            A     1  \n",
+       "4          NaN     1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
       ]
-    },
-    {
-      "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."
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1[\"Month\"] = df_1[\"Month\"]\n",
+    "df_1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "da704598-bff1-428f-91c3-ae77e3830f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "State       Month\n",
+       "Arizona     1        3052\n",
+       "            2        2864\n",
+       "California  1        5673\n",
+       "            2        4929\n",
+       "Nevada      1        1493\n",
+       "            2        1278\n",
+       "Oregon      1        4697\n",
+       "            2        3969\n",
+       "Washington  1        1358\n",
+       "            2        1225\n",
+       "Name: Number of Policies, dtype: int64"
       ]
-    },
-    {
-      "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"
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "state = df_1.groupby([\"State\", \"Month\"])[\"Number of Policies\"].sum()\n",
+    "state"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b29e9b26-0a45-4c7c-9049-b45713c31743",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Display a new DataFrame for top 3 states with highest number of policies sold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "6db8dc5a-ee50-4a77-a25d-95190ed959f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "State\n",
+       "Arizona        5916\n",
+       "California    10602\n",
+       "Nevada         2771\n",
+       "Oregon         8666\n",
+       "Washington     2583\n",
+       "Name: Number of Policies, dtype: int64"
       ]
-    },
-    {
-      "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"
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_top = df_1.groupby([\"State\"])[\"Number of Policies\"].sum()\n",
+    "df_top"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "76475d06-93f7-4919-8926-4278bf525bc1",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Series.sort_values() got an unexpected keyword argument 'by'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[123], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sorted_df \u001b[38;5;241m=\u001b[39m df_top\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Policies\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: Series.sort_values() got an unexpected keyword argument 'by'"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df = df_top.sort_values(by='Number of Policies')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "1d00633d-23a7-4998-85d9-175fcfcda590",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Number of Policies</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>State</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>5916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>10602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>2771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>8666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>2583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Number of Policies\n",
+       "State                         \n",
+       "Arizona                   5916\n",
+       "California               10602\n",
+       "Nevada                    2771\n",
+       "Oregon                    8666\n",
+       "Washington                2583"
       ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "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"
-    },
-    "colab": {
-      "provenance": []
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
-}
\ No newline at end of file
+   ],
+   "source": [
+    "pd.DataFrame(df_top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "b634d2aa-8356-4999-918e-701ad9126d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Series.sort_values() got an unexpected keyword argument 'by'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[135], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m sorted_df \u001b[38;5;241m=\u001b[39m df_top\u001b[38;5;241m.\u001b[39msort_values(by \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Policies\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: Series.sort_values() got an unexpected keyword argument 'by'"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df = df_top.sort_values(by = 'Number of Policies')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81caf42a-f133-475d-82d5-13272ca08c17",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}