diff --git a/lab-dw-pandas.ipynb b/lab-dw-pandas.ipynb
index fbd46831..8b075574 100644
--- a/lab-dw-pandas.ipynb
+++ b/lab-dw-pandas.ipynb
@@ -82,12 +82,507 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "dd4e8cd8-a6f6-486c-a5c4-1745b0c035f4",
    "metadata": {},
-   "outputs": [],
+   "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>Customer</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2.704934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953.59%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743.17%</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566.472247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586.18%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529.881344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307.65%</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17.269323</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER             Education Customer Lifetime Value  \\\n",
+       "0  RB50392  Washington    NaN                Master                     NaN   \n",
+       "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+       "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+       "3  WW63253  California      M              Bachelor              764586.18%   \n",
+       "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+       "\n",
+       "    Income  Monthly Premium Auto Number of Open Complaints     Policy Type  \\\n",
+       "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+       "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+       "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+       "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+       "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+       "\n",
+       "   Vehicle Class  Total Claim Amount  \n",
+       "0  Four-Door Car            2.704934  \n",
+       "1  Four-Door Car         1131.464935  \n",
+       "2   Two-Door Car          566.472247  \n",
+       "3            SUV          529.881344  \n",
+       "4  Four-Door Car           17.269323  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "import pandas as pd\n",
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "3160d342-7f99-4421-ba8b-77253c9442f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4008, 11)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Number of rows and columns\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "b7721069-b889-488c-a6ea-e9b969ae60ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Customer                      object\n",
+       "ST                            object\n",
+       "GENDER                        object\n",
+       "Education                     object\n",
+       "Customer Lifetime Value       object\n",
+       "Income                       float64\n",
+       "Monthly Premium Auto         float64\n",
+       "Number of Open Complaints     object\n",
+       "Policy Type                   object\n",
+       "Vehicle Class                 object\n",
+       "Total Claim Amount           float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes\n",
+    "\n",
+    "# Problems: \n",
+    "# Customer Lifetime Value is an object because of the % symbol\n",
+    "# Number of Open Complaints is an object because of the format used 1/0/00"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "7b75e069-c1bb-4c44-aa5b-1b1be0794c23",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Customer                     1071\n",
+       "ST                              8\n",
+       "GENDER                          5\n",
+       "Education                       6\n",
+       "Customer Lifetime Value      1027\n",
+       "Income                        774\n",
+       "Monthly Premium Auto          132\n",
+       "Number of Open Complaints       6\n",
+       "Policy Type                     3\n",
+       "Vehicle Class                   6\n",
+       "Total Claim Amount            761\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()\n",
+    "# Categorical columns: ST, GENDER, Education, Policy Type, Vehicle Class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "bdd728ff-e780-4b52-84f7-28882dd4f30c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ST:  ['Washington' 'Arizona' 'Nevada' 'California' 'Oregon' 'Cali' 'AZ' 'WA'\n",
+      " nan]\n",
+      "GENDER:  [nan 'F' 'M' 'Femal' 'Male' 'female']\n",
+      "Education:  ['Master' 'Bachelor' 'High School or Below' 'College' 'Bachelors' 'Doctor'\n",
+      " nan]\n",
+      "Policy Type:  ['Personal Auto' 'Corporate Auto' 'Special Auto' nan]\n",
+      "Vehicle Class:  ['Four-Door Car' 'Two-Door Car' 'SUV' 'Luxury SUV' 'Sports Car'\n",
+      " 'Luxury Car' nan]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ST: \", df[\"ST\"].unique())\n",
+    "print(\"GENDER: \", df[\"GENDER\"].unique())\n",
+    "print(\"Education: \",df[\"Education\"].unique())\n",
+    "print(\"Policy Type: \",df[\"Policy Type\"].unique())\n",
+    "print(\"Vehicle Class: \",df[\"Vehicle Class\"].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "fc50b0aa-23a5-49c8-a9ac-6fb7b0c3297c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The maximum value of Income is  99960.0 and the minimum value is  0.0\n",
+      "The maximum value of Monthly Premium Auto is  35354.0 and the minimum value is  61.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "print(\"The maximum value of Income is \", df[\"Income\"].max(),\"and the minimum value is \" , df[\"Income\"].min())\n",
+    "print(\"The maximum value of Monthly Premium Auto is \", df[\"Monthly Premium Auto\"].max(),\"and the minimum value is \", df[\"Monthly Premium Auto\"].min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "c16899ad-11c9-4791-bd40-d2ee2460916b",
+   "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>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1071.000000</td>\n",
+       "      <td>1071.000000</td>\n",
+       "      <td>1071.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>39295.701214</td>\n",
+       "      <td>193.234360</td>\n",
+       "      <td>404.986909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>30469.427060</td>\n",
+       "      <td>1601.190369</td>\n",
+       "      <td>293.027260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>0.382107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>14072.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>202.157702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>36234.000000</td>\n",
+       "      <td>83.000000</td>\n",
+       "      <td>354.729129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>64631.000000</td>\n",
+       "      <td>109.500000</td>\n",
+       "      <td>532.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>99960.000000</td>\n",
+       "      <td>35354.000000</td>\n",
+       "      <td>2893.239678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Income  Monthly Premium Auto  Total Claim Amount\n",
+       "count   1071.000000           1071.000000         1071.000000\n",
+       "mean   39295.701214            193.234360          404.986909\n",
+       "std    30469.427060           1601.190369          293.027260\n",
+       "min        0.000000             61.000000            0.382107\n",
+       "25%    14072.000000             68.000000          202.157702\n",
+       "50%    36234.000000             83.000000          354.729129\n",
+       "75%    64631.000000            109.500000          532.800000\n",
+       "max    99960.000000          35354.000000         2893.239678"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "6def634e-3aca-4082-856f-1b37e9c94cb3",
+   "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>Customer</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1071</td>\n",
+       "      <td>1071</td>\n",
+       "      <td>954</td>\n",
+       "      <td>1071</td>\n",
+       "      <td>1068</td>\n",
+       "      <td>1071</td>\n",
+       "      <td>1071</td>\n",
+       "      <td>1071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>1071</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1027</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>445811.34%</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>1</td>\n",
+       "      <td>320</td>\n",
+       "      <td>457</td>\n",
+       "      <td>324</td>\n",
+       "      <td>4</td>\n",
+       "      <td>830</td>\n",
+       "      <td>780</td>\n",
+       "      <td>576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Customer      ST GENDER Education Customer Lifetime Value  \\\n",
+       "count      1071    1071    954      1071                    1068   \n",
+       "unique     1071       8      5         6                    1027   \n",
+       "top     RB50392  Oregon      F  Bachelor              445811.34%   \n",
+       "freq          1     320    457       324                       4   \n",
+       "\n",
+       "       Number of Open Complaints    Policy Type  Vehicle Class  \n",
+       "count                       1071           1071           1071  \n",
+       "unique                         6              3              6  \n",
+       "top                       1/0/00  Personal Auto  Four-Door Car  \n",
+       "freq                         830            780            576  "
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include=object)"
    ]
   },
   {
@@ -116,12 +611,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "id": "2dca5073-4520-4f42-9390-4b92733284ed",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ST\n",
+       "AZ             25\n",
+       "WA             30\n",
+       "Washington     81\n",
+       "Nevada         98\n",
+       "Cali          120\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "location_series = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\", usecols=[\"ST\"]).squeeze(\"columns\")\n",
+    "location_series_count = location_series.value_counts()\n",
+    "sorted_locations = location_series_count.sort_values()\n",
+    "less_common = sorted_locations.head(5)\n",
+    "less_common"
    ]
   },
   {
@@ -146,12 +662,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "bcfad6c1-9af2-4b0b-9aa9-0dc5c17473c0",
+   "execution_count": 145,
+   "id": "e0fc76bd-dedf-495b-a25b-c1e2f784510b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Policy Type\n",
+      "Personal Auto     780\n",
+      "Corporate Auto    234\n",
+      "Special Auto       57\n",
+      "Name: count, dtype: int64\n",
+      "----\n",
+      "780\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "policy_series = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\", usecols=[\"Policy Type\"]).squeeze(\"columns\")\n",
+    "policy_series_count = policy_series.value_counts()\n",
+    "print(policy_series_count)\n",
+    "print(\"----\")\n",
+    "print(policy_series_count.max())"
    ]
   },
   {
@@ -164,6 +698,17 @@
     "The sales team wants to know if customers with Personal Auto have a lower income than those with Corporate Auto. How does the average income compare between the two policy types?"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "bcfad6c1-9af2-4b0b-9aa9-0dc5c17473c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "personal_auto = df.loc[df[\"Policy Type\"] == \"Personal Auto\"]\n",
+    "corporate_auto = df.loc[df[\"Policy Type\"] == \"Corporate Auto\"]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b1386d75-2810-4aa1-93e0-9485aa12d552",
@@ -176,12 +721,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 165,
    "id": "0c0563cf-6f8b-463d-a321-651a972f82e5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The average income of the customer with a personal policy is  38180.69871794872\n",
+      "The average income of the customer with a corporate policy is  41390.31196581197\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code here"
+    "avg_personal = sum(personal_auto[\"Income\"]) / len(personal_auto[\"Income\"])\n",
+    "avg_corporate = sum(corporate_auto[\"Income\"]) / len(corporate_auto[\"Income\"])\n",
+    "print(\"The average income of the customer with a personal policy is \", avg_personal)\n",
+    "print(\"The average income of the customer with a corporate policy is \", avg_corporate)"
    ]
   },
   {
@@ -226,12 +783,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 169,
    "id": "b731bca6-a760-4860-a27b-a33efa712ce0",
    "metadata": {},
-   "outputs": [],
+   "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>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1071.000000</td>\n",
+       "      <td>1071.000000</td>\n",
+       "      <td>1071.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>39295.701214</td>\n",
+       "      <td>193.234360</td>\n",
+       "      <td>404.986909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>30469.427060</td>\n",
+       "      <td>1601.190369</td>\n",
+       "      <td>293.027260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>0.382107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>14072.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>202.157702</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>36234.000000</td>\n",
+       "      <td>83.000000</td>\n",
+       "      <td>354.729129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>64631.000000</td>\n",
+       "      <td>109.500000</td>\n",
+       "      <td>532.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>99960.000000</td>\n",
+       "      <td>35354.000000</td>\n",
+       "      <td>2893.239678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Income  Monthly Premium Auto  Total Claim Amount\n",
+       "count   1071.000000           1071.000000         1071.000000\n",
+       "mean   39295.701214            193.234360          404.986909\n",
+       "std    30469.427060           1601.190369          293.027260\n",
+       "min        0.000000             61.000000            0.382107\n",
+       "25%    14072.000000             68.000000          202.157702\n",
+       "50%    36234.000000             83.000000          354.729129\n",
+       "75%    64631.000000            109.500000          532.800000\n",
+       "max    99960.000000          35354.000000         2893.239678"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "20fd3459-ed16-4492-b376-47cd2ab4a804",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count     264.000000\n",
+       "mean      782.228263\n",
+       "std       292.751640\n",
+       "min       537.600000\n",
+       "25%       606.521741\n",
+       "50%       679.597985\n",
+       "75%       851.400000\n",
+       "max      2893.239678\n",
+       "Name: Total Claim Amount, dtype: float64"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here"
+    "customers_high_claim = df.loc[df[\"Total Claim Amount\"] > 532.80]\n",
+    "customers_high_claim[\"Total Claim Amount\"].describe()"
    ]
   }
  ],
@@ -251,7 +934,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,