diff --git a/.DS_Store b/.DS_Store
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index 0000000..862f74f
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diff --git a/LabsDataAggregationAndFiltering.ipynb b/LabsDataAggregationAndFiltering.ipynb
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--- /dev/null
+++ b/LabsDataAggregationAndFiltering.ipynb
@@ -0,0 +1,815 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e28ddcd1-14ee-4f34-90a7-0387b77af02b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "41c1fe74-ae67-4c01-b8b5-5ea331225a4f",
+   "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",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 Customer       State  Customer Lifetime Value Response Coverage  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No    Basic   \n",
+       "1           1  KX64629  California              2228.525238       No    Basic   \n",
+       "2           2  LZ68649  Washington             14947.917300       No    Basic   \n",
+       "\n",
+       "  Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+       "0   College           2/18/11         Employed      M  ...   \n",
+       "1   College           1/18/11       Unemployed      F  ...   \n",
+       "2  Bachelor           2/10/11         Employed      M  ...   \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",
+       "\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",
+       "\n",
+       "  Vehicle Size Vehicle Type  \n",
+       "0      Medsize          NaN  \n",
+       "1      Medsize          NaN  \n",
+       "2      Medsize            A  \n",
+       "\n",
+       "[3 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "17c31865-6c31-4b8a-86e5-fc124ad8c5b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10910, 26)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "177f4cdd-9cec-4cab-ad95-04e6b6e9af80",
+   "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>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>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>1/19/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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",
+       "    </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>1/16/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 26 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",
+       "\n",
+       "    Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+       "3   Extended   College           1/11/11         Employed      M  ...   \n",
+       "8    Premium   College           1/19/11         Employed      M  ...   \n",
+       "15     Basic    Master           1/16/11         Employed      F  ...   \n",
+       "\n",
+       "    Number of Open Complaints Number of Policies     Policy Type  \\\n",
+       "3                         0.0                  2  Corporate Auto   \n",
+       "8                         0.0                  1   Personal Auto   \n",
+       "15                        0.0                  1    Special Auto   \n",
+       "\n",
+       "          Policy  Renew Offer Type  Sales Channel  Total Claim Amount  \\\n",
+       "3   Corporate L3            Offer2         Branch          484.013411   \n",
+       "8    Personal L1            Offer2         Branch          739.200000   \n",
+       "15    Special L1            Offer2         Branch          547.200000   \n",
+       "\n",
+       "    Vehicle Class Vehicle Size Vehicle Type  \n",
+       "3   Four-Door Car      Medsize            A  \n",
+       "8      Sports Car      Medsize          NaN  \n",
+       "15            SUV      Medsize          NaN  \n",
+       "\n",
+       "[3 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df = df[(df['Total Claim Amount'] < 1000) & (df['Response'] == 'Yes')]\n",
+    "filtered_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1170df77-a367-495b-b1d3-169655e3d37d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1399, 26)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ebbd3eee-c141-4b7e-8582-7ac0d781c996",
+   "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>Policy Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>433.738499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>408.582459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>452.965929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>457.010178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>453.280164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>86.343750</td>\n",
+       "      <td>8247.088702</td>\n",
+       "      <td>429.527942</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Policy Type Gender  Monthly Premium Auto  Customer Lifetime Value  \\\n",
+       "0  Corporate Auto      F             94.301775              7712.628736   \n",
+       "1  Corporate Auto      M             92.188312              7944.465414   \n",
+       "2   Personal Auto      F             98.998148              8339.791842   \n",
+       "3   Personal Auto      M             91.085821              7448.383281   \n",
+       "4    Special Auto      F             92.314286              7691.584111   \n",
+       "5    Special Auto      M             86.343750              8247.088702   \n",
+       "\n",
+       "   Total Claim Amount  \n",
+       "0          433.738499  \n",
+       "1          408.582459  \n",
+       "2          452.965929  \n",
+       "3          457.010178  \n",
+       "4          453.280164  \n",
+       "5          429.527942  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "responded_df = df[df['Response'] == 'Yes']\n",
+    "\n",
+    "summary = responded_df.groupby(['Policy Type', 'Gender'])[\n",
+    "    ['Monthly Premium Auto', 'Customer Lifetime Value', 'Total Claim Amount']\n",
+    "].mean().reset_index()\n",
+    "\n",
+    "summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e28118ae-583d-4604-b5d7-d64b062fd3bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 5)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "06854e42-1fa1-47df-bcc1-b73ac1ea89e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Corporate Auto males are more profitable with high value and little claims and \n",
+    "### personal auto males have low value and high claims: this makes them riskier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1df6f892-32ca-4ffd-b3a5-f8d16e88f7de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>State</th>\n",
+       "      <th>Customer Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State  Customer Count\n",
+       "0  California            3552\n",
+       "1      Oregon            2909\n",
+       "2     Arizona            1937\n",
+       "3      Nevada             993\n",
+       "4  Washington             888"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "customers_per_state = df['State'].value_counts()\n",
+    "\n",
+    "states_over_500 = customers_per_state[customers_per_state > 500]\n",
+    "\n",
+    "states_over_500_df = states_over_500.reset_index()\n",
+    "states_over_500_df.columns = ['State', 'Customer Count']\n",
+    "states_over_500_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "add8973a-d24e-44c4-8fc1-240f14e4f175",
+   "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>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>73225.95652</td>\n",
+       "      <td>1904.000852</td>\n",
+       "      <td>5640.505303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>67907.27050</td>\n",
+       "      <td>1898.007675</td>\n",
+       "      <td>5548.031892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>F</td>\n",
+       "      <td>61850.18803</td>\n",
+       "      <td>1898.683686</td>\n",
+       "      <td>5623.611187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>College</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61134.68307</td>\n",
+       "      <td>1918.119700</td>\n",
+       "      <td>6005.847375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44856.11397</td>\n",
+       "      <td>2395.570000</td>\n",
+       "      <td>5332.462694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>32677.34284</td>\n",
+       "      <td>2267.604038</td>\n",
+       "      <td>5577.669457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55277.44589</td>\n",
+       "      <td>2144.921535</td>\n",
+       "      <td>6039.553187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>83325.38119</td>\n",
+       "      <td>1940.981221</td>\n",
+       "      <td>6286.731006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>F</td>\n",
+       "      <td>51016.06704</td>\n",
+       "      <td>2417.777032</td>\n",
+       "      <td>5729.855012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>50568.25912</td>\n",
+       "      <td>2272.307310</td>\n",
+       "      <td>5579.099207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Education Gender          max          min       median\n",
+       "0              Bachelor      F  73225.95652  1904.000852  5640.505303\n",
+       "1              Bachelor      M  67907.27050  1898.007675  5548.031892\n",
+       "2               College      F  61850.18803  1898.683686  5623.611187\n",
+       "3               College      M  61134.68307  1918.119700  6005.847375\n",
+       "4                Doctor      F  44856.11397  2395.570000  5332.462694\n",
+       "5                Doctor      M  32677.34284  2267.604038  5577.669457\n",
+       "6  High School or Below      F  55277.44589  2144.921535  6039.553187\n",
+       "7  High School or Below      M  83325.38119  1940.981221  6286.731006\n",
+       "8                Master      F  51016.06704  2417.777032  5729.855012\n",
+       "9                Master      M  50568.25912  2272.307310  5579.099207"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "stats = df.groupby(['Education', 'Gender'])['Customer Lifetime Value'].agg(['max', 'min', 'median']).reset_index()\n",
+    "\n",
+    "stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ef29969f-c472-49f5-893f-c0b0f5025968",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Bachelor’s degree holders tend to have the highest maximum customer lifetime value, \n",
+    "### while median values are the same across all educaton levels and genders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16a21305-728b-42de-a854-44ce4ba5ab87",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}