diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..d9b14bc 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -36,6 +36,254 @@
         "   - have a response \"Yes\" to the last marketing campaign."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "7766cfde",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+        "df = pd.read_csv(url)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "18ddaef0",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "df.columns = df.columns.str.lower().str.replace(\" \", \"_\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "1155c44c",
+      "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",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>19</td>\n",
+              "      <td>NJ54277</td>\n",
+              "      <td>California</td>\n",
+              "      <td>3746.751625</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Extended</td>\n",
+              "      <td>College</td>\n",
+              "      <td>2/26/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L2</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>19.575683</td>\n",
+              "      <td>Two-Door Car</td>\n",
+              "      <td>Large</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>27</td>\n",
+              "      <td>MQ68407</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>4376.363592</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Premium</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>2/28/11</td>\n",
+              "      <td>Employed</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>Offer2</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>60.036683</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </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",
+              "3            3  XL78013      Oregon             22332.439460      Yes   \n",
+              "8            8  FM55990  California              5989.773931      Yes   \n",
+              "15          15  CW49887  California              4626.801093      Yes   \n",
+              "19          19  NJ54277  California              3746.751625      Yes   \n",
+              "27          27  MQ68407      Oregon              4376.363592      Yes   \n",
+              "\n",
+              "    coverage education effective_to_date employmentstatus gender  ...  \\\n",
+              "3   Extended   College           1/11/11         Employed      M  ...   \n",
+              "8    Premium   College           1/19/11         Employed      M  ...   \n",
+              "15     Basic    Master           1/16/11         Employed      F  ...   \n",
+              "19  Extended   College           2/26/11         Employed      F  ...   \n",
+              "27   Premium  Bachelor           2/28/11         Employed      F  ...   \n",
+              "\n",
+              "    number_of_open_complaints number_of_policies     policy_type  \\\n",
+              "3                         0.0                  2  Corporate Auto   \n",
+              "8                         0.0                  1   Personal Auto   \n",
+              "15                        0.0                  1    Special Auto   \n",
+              "19                        1.0                  1   Personal Auto   \n",
+              "27                        0.0                  1   Personal Auto   \n",
+              "\n",
+              "          policy  renew_offer_type  sales_channel  total_claim_amount  \\\n",
+              "3   Corporate L3            Offer2         Branch          484.013411   \n",
+              "8    Personal L1            Offer2         Branch          739.200000   \n",
+              "15    Special L1            Offer2         Branch          547.200000   \n",
+              "19   Personal L2            Offer2    Call Center           19.575683   \n",
+              "27   Personal L3            Offer2          Agent           60.036683   \n",
+              "\n",
+              "    vehicle_class vehicle_size vehicle_type  \n",
+              "3   Four-Door Car      Medsize            A  \n",
+              "8      Sports Car      Medsize          NaN  \n",
+              "15            SUV      Medsize          NaN  \n",
+              "19   Two-Door Car        Large            A  \n",
+              "27  Four-Door Car      Medsize          NaN  \n",
+              "\n",
+              "[5 rows x 26 columns]"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "low_claim_yes = df[(df['total_claim_amount'] < 1000) & (df['response'] == \"Yes\")]\n",
+        "low_claim_yes.head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
@@ -48,6 +296,112 @@
         "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "bea60123",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "yes_responders = df[df['response'] == \"Yes\"]\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "e9102999",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "grouped = yes_responders.groupby(['policy_type', 'gender'])[['monthly_premium_auto', 'customer_lifetime_value']].mean().reset_index()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "d2b66241",
+      "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>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>433.738499</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>M</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>452.965929</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>M</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>453.280164</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      policy_type gender  total_claim_amount\n",
+              "0  Corporate Auto      F          433.738499\n",
+              "1  Corporate Auto      M          408.582459\n",
+              "2   Personal Auto      F          452.965929\n",
+              "3   Personal Auto      M          457.010178\n",
+              "4    Special Auto      F          453.280164"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "claim_pattern = yes_responders.groupby(['policy_type', 'gender'])['total_claim_amount'].mean().reset_index()\n",
+        "claim_pattern.head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
@@ -58,6 +412,17 @@
         "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "616bcc9a",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "state_counts = df['state'].value_counts()\n",
+        "states_over_500 = state_counts[state_counts > 500]\n"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
@@ -68,6 +433,113 @@
         "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "4bcf8d06",
+      "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",
+              "  </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"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "clv_stats = df.groupby(['education', 'gender'])['customer_lifetime_value'].agg(['max', 'min', 'median']).reset_index()\n",
+        "clv_stats.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0d6e712c",
+      "metadata": {},
+      "source": [
+        "Doctorate-level customers show a lower maximum CLV\n",
+        "Customers with a Bachelor's degree tend to have the highest maximum CLV"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b42999f9-311f-481e-ae63-40a5577072c5",
@@ -88,6 +560,112 @@
         "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": "code",
+      "execution_count": 13,
+      "id": "2eb4fabe",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\levgi\\AppData\\Local\\Temp\\ipykernel_11876\\3086097951.py:1: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+            "  df['effective_to_date'] = pd.to_datetime(df['effective_to_date'])\n"
+          ]
+        }
+      ],
+      "source": [
+        "df['effective_to_date'] = pd.to_datetime(df['effective_to_date'])\n",
+        "df['month'] = df['effective_to_date'].dt.strftime('%B')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "ab84c1ca",
+      "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>month</th>\n",
+              "      <th>February</th>\n",
+              "      <th>January</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>state</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>929</td>\n",
+              "      <td>1008</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>1634</td>\n",
+              "      <td>1918</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nevada</th>\n",
+              "      <td>442</td>\n",
+              "      <td>551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>1344</td>\n",
+              "      <td>1565</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Washington</th>\n",
+              "      <td>425</td>\n",
+              "      <td>463</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "month       February  January\n",
+              "state                        \n",
+              "Arizona          929     1008\n",
+              "California      1634     1918\n",
+              "Nevada           442      551\n",
+              "Oregon          1344     1565\n",
+              "Washington       425      463"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "policies_by_state_month = df.pivot_table(index='state', columns='month', values='policy', aggfunc='count', fill_value=0)\n",
+        "policies_by_state_month.head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b6aec097-c633-4017-a125-e77a97259cda",
@@ -103,6 +681,93 @@
         "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "e43cdb63",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "top_states = df['state'].value_counts().nlargest(3).index\n",
+        "top_states = top_states.sort_values(ascending=False)\n",
+        "top_states_df = df[df['state'].isin(top_states)]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "9713939d",
+      "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>month</th>\n",
+              "      <th>February</th>\n",
+              "      <th>January</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>state</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>929</td>\n",
+              "      <td>1008</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>1634</td>\n",
+              "      <td>1918</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>1344</td>\n",
+              "      <td>1565</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "month       February  January\n",
+              "state                        \n",
+              "Arizona          929     1008\n",
+              "California      1634     1918\n",
+              "Oregon          1344     1565"
+            ]
+          },
+          "execution_count": 20,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "grouped_top = top_states_df.groupby(['state', 'month'])['policy'].count().reset_index()\n",
+        "top3_pivot = grouped_top.pivot(index='state', columns='month', values='policy').fillna(0)\n",
+        "top3_pivot.head()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
@@ -127,14 +792,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 22,
       "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
       "metadata": {
         "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "sales_channel\n",
+              "Agent          0.190746\n",
+              "Branch         0.113787\n",
+              "Call Center    0.109786\n",
+              "Web            0.117141\n",
+              "Name: Yes, dtype: float64"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "# your code goes here"
+        "response_rate = df.groupby('sales_channel')['response'].value_counts(normalize=True).unstack().fillna(0)['Yes']\n",
+        "response_rate"
       ]
     }
   ],
@@ -143,7 +825,7 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "base",
       "language": "python",
       "name": "python3"
     },
@@ -157,7 +839,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.11.7"
     }
   },
   "nbformat": 4,