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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e",
+   "metadata": {
+    "id": "25d7736c-ba17-4aff-b6bb-66eba20fbf4e"
+   },
+   "source": [
+    "# Lab | Data Structuring and Combining Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986",
+   "metadata": {
+    "id": "a2cdfc70-44c8-478c-81e7-2bc43fdf4986"
+   },
+   "source": [
+    "## Challenge 1: Combining & Cleaning Data\n",
+    "\n",
+    "In this challenge, we will be working with the customer data from an insurance company, as we did in the two previous labs. The data can be found here:\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\n",
+    "\n",
+    "But this time, we got new data, which can be found in the following 2 CSV files located at the links below.\n",
+    "\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\n",
+    "\n",
+    "Note that you'll need to clean and format the new data.\n",
+    "\n",
+    "Observation:\n",
+    "- One option is to first combine the three datasets and then apply the cleaning function to the new combined dataset\n",
+    "- Another option would be to read the clean file you saved in the previous lab, and just clean the two new files and concatenate the three clean datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "ef00922f-b0ec-418c-87c7-3444acae3faa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                      customer\n",
+       "1                            st\n",
+       "2                        gender\n",
+       "3                     education\n",
+       "4       customer lifetime value\n",
+       "5                        income\n",
+       "6          monthly premium auto\n",
+       "7     number of open complaints\n",
+       "8                   policy type\n",
+       "9                 vehicle class\n",
+       "10           total claim amount\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "import pandas as pd\n",
+    "url1=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\"\n",
+    "url2=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\"\n",
+    "url3=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\"\n",
+    "df1 = pd.read_csv(url1)\n",
+    "df2 = pd.read_csv(url2)\n",
+    "df3 = pd.read_csv(url3)\n",
+    "\n",
+    "----------\n",
+    "\n",
+    "#1 Lowercase everything:- \n",
+    "\n",
+    "df1.columns = pd.Series(df1.columns).apply(lambda col: col.lower())\n",
+    "df1.columns\n",
+    "df2.columns = pd.Series(df2.columns).apply(lambda col: col.lower())\n",
+    "df2.columns\n",
+    "df3.columns = pd.Series(df3.columns).apply(lambda col: col.lower())\n",
+    "df3.columns\n",
+    "----------\n",
+    "\n",
+    "#2 Change / Replace names of columns:-\n",
+    "\n",
+    "df = df.rename(columns={'st': 'State'})\n",
+    "----------\n",
+    "\n",
+    "#3 Remove null values:-\n",
+    "\n",
+    "    # Checking for Null Values\n",
+    "df.isnull()  # Returns a DataFrame with True where values are null\n",
+    "    # Count the number of null values in each column\n",
+    "df.isna().sum()\n",
+    "    # Dropping rows with Null Values\n",
+    "df.dropna(inplace=True)\n",
+    "----------\n",
+    "\n",
+    "# 4 Find Unique Values\n",
+    "\n",
+    "unique_genders = df['gender'].unique()\n",
+    "female_variations = ['Femal', 'Female']\n",
+    "# Replace the variations with 'F'\n",
+    "df['gender'] = df['gender'].replace(female_variations, 'F')\n",
+    "print(df)\n",
+    "     # - Also in comprehension\n",
+    "\n",
+    "df['gender'] = df['gender'].apply(lambda x: 'F' if x in ('Femal', 'Female') else ('M' if x == 'Male' else x))\n",
+    "#-or-\n",
+    "df['education'] = df['education'].apply(lambda x: 'Bachelor' if x == 'Bachelors' else x)\n",
+    "#-or-\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Sports Car', 'Luxury')\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Luxury SUV', 'Luxury')\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Luxury Car', 'Luxury')\n",
+    "\n",
+    "vc = df['vehicle_class'].unique()\n",
+    "print(vc)\n",
+    "----------\n",
+    "\n",
+    "#5  Combine Tables\n",
+    "    # Concatenate the sales, revenue, and costs DataFrames vertically (along rows)\n",
+    "pd.concat([df1, df2, df3], axis=0)\n",
+    "    # Concatenate the sales, revenue, and costs DataFrames horizontally (along columns)\n",
+    "pd.concat([df1, df2, df3], axis=1) # by default is outer, takes the union\n",
+    "\n",
+    "#6 Manually Re-order the Columns:-\n",
+    "    # Specify the desired column order -  *** after they have been lower cased and standardized\n",
+    "desired_order = ['customer', 'state','gender', 'education', 'Customer Lifetime Value', 'Income', 'Monthly Premium Auto','Number of Open Complaints', 'Total Claim Amount', 'Policy Type', 'Vehicle Class']  # Replace with your desired column names,\n",
+    "\n",
+    "    # Check if all desired columns are in the DataFrame\n",
+    "if all(column in df1.columns for column in desired_order):\n",
+    "    # Reorder the columns\n",
+    "    df1 = df1[desired_order]\n",
+    "    print(\"\\nDataFrame with columns reordered:\")\n",
+    "    print(df1)\n",
+    "else:\n",
+    "    missing_columns = [col for col in desired_order if col not in df1.columns]\n",
+    "    print(f\"\\nError: The following columns are missing from the DataFrame: {missing_columns}\")\n",
+    "----------\n",
+    "\n",
+    "#7 Convert to Strings\n",
+    "df1 = df1.applymap(lambda x: x.lower() if isinstance(x, str) else str(x) if x is not None else x)\n",
+    "---------\n",
+    "\n",
+    "# Reset dataframe if needed\n",
+    "\n",
+    "df_reset = df.reset_index()\n",
+    "----------\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "492d06e3-92c7-4105-ac72-536db98d3244",
+   "metadata": {
+    "id": "492d06e3-92c7-4105-ac72-536db98d3244"
+   },
+   "outputs": [],
+   "source": [
+    "# Your code goes here\n",
+    "import pandas as pd\n",
+    "url1=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\"\n",
+    "url2=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\"\n",
+    "url3=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\"\n",
+    "df1 = pd.read_csv(url1)\n",
+    "df2 = pd.read_csv(url2)\n",
+    "df3 = pd.read_csv(url3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "2e98bc70-e581-45ea-a066-616959d07e29",
+   "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>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": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "d20839b0-09c5-4cf4-a089-aaadb12fd7d1",
+   "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>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47%</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>633.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>462680.11%</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>547.2</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>899704.02%</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>537.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UH35128</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2580706.30%</td>\n",
+       "      <td>71210</td>\n",
+       "      <td>214</td>\n",
+       "      <td>1/1/00</td>\n",
+       "      <td>1027.2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>WH52799</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>380812.21%</td>\n",
+       "      <td>94903</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>451.2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER Education Customer Lifetime Value  Income  \\\n",
+       "0  GS98873     Arizona      F  Bachelor              323912.47%   16061   \n",
+       "1  CW49887  California      F    Master              462680.11%   79487   \n",
+       "2  MY31220  California      F   College              899704.02%   54230   \n",
+       "3  UH35128      Oregon      F   College             2580706.30%   71210   \n",
+       "4  WH52799     Arizona      F   College              380812.21%   94903   \n",
+       "\n",
+       "   Monthly Premium Auto Number of Open Complaints  Total Claim Amount  \\\n",
+       "0                    88                    1/0/00               633.6   \n",
+       "1                   114                    1/0/00               547.2   \n",
+       "2                   112                    1/0/00               537.6   \n",
+       "3                   214                    1/1/00              1027.2   \n",
+       "4                    94                    1/0/00               451.2   \n",
+       "\n",
+       "      Policy Type  Vehicle Class  \n",
+       "0   Personal Auto  Four-Door Car  \n",
+       "1    Special Auto            SUV  \n",
+       "2   Personal Auto   Two-Door Car  \n",
+       "3   Personal Auto     Luxury Car  \n",
+       "4  Corporate Auto   Two-Door Car  "
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "8fa25e52-668f-4dc4-8df9-2e78b2e15313",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'st', 'gender', 'education', 'customer lifetime value',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'policy type', 'vehicle class', 'total claim amount'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "#1 Lowercase everything:- pd.Series(df1.columns).apply(lambda col: col.lower()) - on seaparate cells\n",
+    "#2 Re-order Columns\n",
+    "#  Null Values\n",
+    "\n",
+    "df1.columns = pd.Series(df1.columns).apply(lambda col: col.lower())\n",
+    "df1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "cfe7dd19-629d-415b-ac81-48e62e0cd74f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'st', 'gender', 'education', 'customer lifetime value',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'total claim amount', 'policy type', 'vehicle class'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "#1 Lowercase everything:- df = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)\n",
+    "#2 Re-order Columns\n",
+    "#  Null Values\n",
+    "\n",
+    "df2.columns = pd.Series(df2.columns).apply(lambda col: col.lower())\n",
+    "df2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "6bcd3d11-8290-4490-b1c7-a1dcf855ff74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'state', 'customer lifetime value', 'education', 'gender',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'policy type', 'total claim amount', 'vehicle class'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.columns = pd.Series(df3.columns).apply(lambda col: col.lower())\n",
+    "df3.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "edae10e3-1c37-4c4a-8d45-ef818019e534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = df3.rename(columns={'state': 'st'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "500ffc1f-9752-45c2-8a85-3760d22acb87",
+   "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>customer lifetime value</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</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>total claim amount</th>\n",
+       "      <th>vehicle class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>499.200000</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TB86706</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2502.637401</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>3.468912</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer          st  customer lifetime value             education gender  \\\n",
+       "0  SA25987  Washington              3479.137523  High School or Below      M   \n",
+       "1  TB86706     Arizona              2502.637401                Master      M   \n",
+       "\n",
+       "   income  monthly premium auto  number of open complaints    policy type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "1       0                    66                          0  Personal Auto   \n",
+       "\n",
+       "   total claim amount vehicle class  \n",
+       "0          499.200000  Two-Door Car  \n",
+       "1            3.468912  Two-Door Car  "
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1e26414-f807-4bc2-8b38-7652d1ddd9bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "9e1fca76-fce2-4b86-9387-161b49ae9960",
+   "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>State</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Education</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>Total Claim Amount</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>499.2</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer       State Gender  Customer Lifetime Value             Education  \\\n",
+       "0  SA25987  Washington      M              3479.137523  High School or Below   \n",
+       "\n",
+       "   Income  Monthly Premium Auto  Number of Open Complaints    Policy Type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "\n",
+       "   Total Claim Amount Vehicle Class  \n",
+       "0               499.2  Two-Door Car  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "d0d438fb-4852-4676-b4de-1beabf9b8d31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame with columns reordered:\n",
+      "     Customer          ST GENDER             Education  \\\n",
+      "0     rb50392  washington    nan                master   \n",
+      "1     qz44356     arizona      f              bachelor   \n",
+      "2     ai49188      nevada      f              bachelor   \n",
+      "3     ww63253  california      m              bachelor   \n",
+      "4     ga49547  washington      m  high school or below   \n",
+      "...       ...         ...    ...                   ...   \n",
+      "4003      nan         nan    nan                   nan   \n",
+      "4004      nan         nan    nan                   nan   \n",
+      "4005      nan         nan    nan                   nan   \n",
+      "4006      nan         nan    nan                   nan   \n",
+      "4007      nan         nan    nan                   nan   \n",
+      "\n",
+      "     Customer Lifetime Value   Income Monthly Premium Auto  \\\n",
+      "0                        nan      0.0               1000.0   \n",
+      "1                 697953.59%      0.0                 94.0   \n",
+      "2                1288743.17%  48767.0                108.0   \n",
+      "3                 764586.18%      0.0                106.0   \n",
+      "4                 536307.65%  36357.0                 68.0   \n",
+      "...                      ...      ...                  ...   \n",
+      "4003                     nan      nan                  nan   \n",
+      "4004                     nan      nan                  nan   \n",
+      "4005                     nan      nan                  nan   \n",
+      "4006                     nan      nan                  nan   \n",
+      "4007                     nan      nan                  nan   \n",
+      "\n",
+      "     Number of Open Complaints Total Claim Amount     Policy Type  \\\n",
+      "0                       1/0/00           2.704934   personal auto   \n",
+      "1                       1/0/00        1131.464935   personal auto   \n",
+      "2                       1/0/00         566.472247   personal auto   \n",
+      "3                       1/0/00         529.881344  corporate auto   \n",
+      "4                       1/0/00          17.269323   personal auto   \n",
+      "...                        ...                ...             ...   \n",
+      "4003                       nan                nan             nan   \n",
+      "4004                       nan                nan             nan   \n",
+      "4005                       nan                nan             nan   \n",
+      "4006                       nan                nan             nan   \n",
+      "4007                       nan                nan             nan   \n",
+      "\n",
+      "      Vehicle Class  \n",
+      "0     four-door car  \n",
+      "1     four-door car  \n",
+      "2      two-door car  \n",
+      "3               suv  \n",
+      "4     four-door car  \n",
+      "...             ...  \n",
+      "4003            nan  \n",
+      "4004            nan  \n",
+      "4005            nan  \n",
+      "4006            nan  \n",
+      "4007            nan  \n",
+      "\n",
+      "[4008 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Specify the desired column order\n",
+    "desired_order = ['Customer', 'ST','GENDER', 'Education', 'Customer Lifetime Value', 'Income', 'Monthly Premium Auto','Number of Open Complaints', 'Total Claim Amount', 'Policy Type', 'Vehicle Class']  # Replace with your desired column names,\n",
+    "\n",
+    "# Check if all desired columns are in the DataFrame\n",
+    "if all(column in df1.columns for column in desired_order):\n",
+    "    # Reorder the columns\n",
+    "    df1 = df1[desired_order]\n",
+    "    print(\"\\nDataFrame with columns reordered:\")\n",
+    "    print(df1)\n",
+    "else:\n",
+    "    missing_columns = [col for col in desired_order if col not in df1.columns]\n",
+    "    print(f\"\\nError: The following columns are missing from the DataFrame: {missing_columns}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "54274993-7e44-4976-aa52-3b41a8831232",
+   "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>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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7065</th>\n",
+       "      <td>LA72316</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>23405.98798</td>\n",
+       "      <td>71941.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>198.234764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7066</th>\n",
+       "      <td>PK87824</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>3096.511217</td>\n",
+       "      <td>21604.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>379.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7067</th>\n",
+       "      <td>TD14365</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>8163.890428</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>790.784983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7068</th>\n",
+       "      <td>UP19263</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>College</td>\n",
+       "      <td>7524.442436</td>\n",
+       "      <td>21941.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>691.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7069</th>\n",
+       "      <td>Y167826</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2611.836866</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>369.600000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12074 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer          st gender             education  \\\n",
+       "0     RB50392  Washington    NaN                Master   \n",
+       "1     QZ44356     Arizona      F              Bachelor   \n",
+       "2     AI49188      Nevada      F              Bachelor   \n",
+       "3     WW63253  California      M              Bachelor   \n",
+       "4     GA49547  Washington      M  High School or Below   \n",
+       "...       ...         ...    ...                   ...   \n",
+       "7065  LA72316  California      M              Bachelor   \n",
+       "7066  PK87824  California      F               College   \n",
+       "7067  TD14365  California      M              Bachelor   \n",
+       "7068  UP19263  California      M               College   \n",
+       "7069  Y167826  California      M               College   \n",
+       "\n",
+       "     customer lifetime value   income  monthly premium auto  \\\n",
+       "0                        NaN      0.0                1000.0   \n",
+       "1                 697953.59%      0.0                  94.0   \n",
+       "2                1288743.17%  48767.0                 108.0   \n",
+       "3                 764586.18%      0.0                 106.0   \n",
+       "4                 536307.65%  36357.0                  68.0   \n",
+       "...                      ...      ...                   ...   \n",
+       "7065             23405.98798  71941.0                  73.0   \n",
+       "7066             3096.511217  21604.0                  79.0   \n",
+       "7067             8163.890428      0.0                  85.0   \n",
+       "7068             7524.442436  21941.0                  96.0   \n",
+       "7069             2611.836866      0.0                  77.0   \n",
+       "\n",
+       "     number of open complaints     policy type  vehicle class  \\\n",
+       "0                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "1                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "2                       1/0/00   Personal Auto   Two-Door Car   \n",
+       "3                       1/0/00  Corporate Auto            SUV   \n",
+       "4                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "...                        ...             ...            ...   \n",
+       "7065                         0   Personal Auto  Four-Door Car   \n",
+       "7066                         0  Corporate Auto  Four-Door Car   \n",
+       "7067                         3  Corporate Auto  Four-Door Car   \n",
+       "7068                         0   Personal Auto  Four-Door Car   \n",
+       "7069                         0  Corporate Auto   Two-Door Car   \n",
+       "\n",
+       "      total claim amount  \n",
+       "0               2.704934  \n",
+       "1            1131.464935  \n",
+       "2             566.472247  \n",
+       "3             529.881344  \n",
+       "4              17.269323  \n",
+       "...                  ...  \n",
+       "7065          198.234764  \n",
+       "7066          379.200000  \n",
+       "7067          790.784983  \n",
+       "7068          691.200000  \n",
+       "7069          369.600000  \n",
+       "\n",
+       "[12074 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df2, df3], axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31b8a9e7-7db9-4604-991b-ef6771603e57",
+   "metadata": {
+    "id": "31b8a9e7-7db9-4604-991b-ef6771603e57"
+   },
+   "source": [
+    "# Challenge 2: Structuring Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b",
+   "metadata": {
+    "id": "a877fd6d-7a0c-46d2-9657-f25036e4ca4b"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company, but we will use a dataset with more columns, 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_clean.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 performing data cleaning, formatting, and structuring."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
+   "metadata": {
+    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
+   },
+   "outputs": [],
+   "source": [
+    "# Your code goes here\n",
+    "url4=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    "df4 = pd.read_csv(url4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "c1d5fe1e-5478-4da4-906a-87976faf4674",
+   "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>2011-02-18</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>A</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>2011-01-18</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>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 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",
+       "\n",
+       "  education effective_to_date employmentstatus gender  ...  \\\n",
+       "0   College        2011-02-18         Employed      M  ...   \n",
+       "1   College        2011-01-18       Unemployed      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",
+       "\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",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "0dac4374-5c3f-41c5-bcd0-da29057d314c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['unnamed:_0', 'customer', 'state', 'customer_lifetime_value',\n",
+       "       'response', 'coverage', 'education', 'effective_to_date',\n",
+       "       'employmentstatus', 'gender', 'income', 'location_code',\n",
+       "       'marital_status', 'monthly_premium_auto', 'months_since_last_claim',\n",
+       "       'months_since_policy_inception', 'number_of_open_complaints',\n",
+       "       'number_of_policies', 'policy_type', 'policy', 'renew_offer_type',\n",
+       "       'sales_channel', 'total_claim_amount', 'vehicle_class', 'vehicle_size',\n",
+       "       'vehicle_type', 'month'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.columns = pd.Series(df4.columns).apply(lambda col: col.lower())\n",
+    "df4.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "0978f14b-1ad3-406b-b1a5-a793b900fa90",
+   "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>2011-02-18</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>A</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>2011-01-18</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>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 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",
+       "\n",
+       "  education effective_to_date employmentstatus gender  ...  \\\n",
+       "0   College        2011-02-18         Employed      M  ...   \n",
+       "1   College        2011-01-18       Unemployed      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",
+       "\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",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "039fdbf3-a81f-44e4-b06c-5013ac4c4abe",
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>152490152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>113775608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>81055004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>62200103</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  income\n",
+       "sales_channel           \n",
+       "Agent          152490152\n",
+       "Branch         113775608\n",
+       "Call Center     81055004\n",
+       "Web             62200103"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df4 = df4.pivot_table(index='sales_channel',values=['income'], aggfunc='sum')\n",
+    "pivot_df4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad12823-4f6d-4d42-a40c-97b72d3fd5d3",
+   "metadata": {},
+   "source": [
+    "The in-person sales method is the most lucrative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "6c8abea1-107e-4e66-9331-f0ed90e2c5f3",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">customer_lifetime_value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bachelor</th>\n",
+       "      <td>13386258.11</td>\n",
+       "      <td>12110061.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>College</th>\n",
+       "      <td>12460107.91</td>\n",
+       "      <td>12851725.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Doctor</th>\n",
+       "      <td>1465701.78</td>\n",
+       "      <td>1490482.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High School or Below</th>\n",
+       "      <td>13793600.12</td>\n",
+       "      <td>12680914.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>3874600.25</td>\n",
+       "      <td>3365559.06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     customer_lifetime_value             \n",
+       "gender                                     F            M\n",
+       "education                                                \n",
+       "Bachelor                         13386258.11  12110061.83\n",
+       "College                          12460107.91  12851725.02\n",
+       "Doctor                            1465701.78   1490482.06\n",
+       "High School or Below             13793600.12  12680914.19\n",
+       "Master                            3874600.25   3365559.06"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df4a = df4.pivot_table(index='education',columns='gender', values=['customer_lifetime_value'], aggfunc='sum')\n",
+    "pivot_df4a = pivot_df4a.round(2)\n",
+    "pivot_df4a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df35fd0d-513e-4e77-867e-429da10a9cc7",
+   "metadata": {
+    "id": "df35fd0d-513e-4e77-867e-429da10a9cc7"
+   },
+   "source": [
+    "1. You work at the marketing department and you want to know which sales channel brought the most sales in terms of total revenue. Using pivot, create a summary table showing the total revenue for each sales channel (branch, call center, web, and mail).\n",
+    "Round the total revenue to 2 decimal points.  Analyze the resulting table to draw insights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "640993b2-a291-436c-a34d-a551144f8196",
+   "metadata": {
+    "id": "640993b2-a291-436c-a34d-a551144f8196"
+   },
+   "source": [
+    "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
+   "metadata": {
+    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198"
+   },
+   "source": [
+    "## Bonus\n",
+    "\n",
+    "You work at the customer service department and you want to know which months had the highest number of complaints by policy type category. Create a summary table showing the number of complaints by policy type and month.\n",
+    "Show it in a long format table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291",
+   "metadata": {
+    "id": "e3d09a8f-953c-448a-a5f8-2e5a8cca7291"
+   },
+   "source": [
+    "*In data analysis, a long format table is a way of structuring data in which each observation or measurement is stored in a separate row of the table. The key characteristic of a long format table is that each column represents a single variable, and each row represents a single observation of that variable.*\n",
+    "\n",
+    "*More information about long and wide format tables here: https://www.statology.org/long-vs-wide-data/*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a069e0b-b400-470e-904d-d17582191be4",
+   "metadata": {
+    "id": "3a069e0b-b400-470e-904d-d17582191be4"
+   },
+   "outputs": [],
+   "source": [
+    "# Your code goes here"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-dw-data-structuring-and-combining.ipynb b/lab-dw-data-structuring-and-combining.ipynb
index ec4e3f9..02133a7 100644
--- a/lab-dw-data-structuring-and-combining.ipynb
+++ b/lab-dw-data-structuring-and-combining.ipynb
@@ -36,14 +36,1060 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
+   "id": "ef00922f-b0ec-418c-87c7-3444acae3faa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                      customer\n",
+       "1                            st\n",
+       "2                        gender\n",
+       "3                     education\n",
+       "4       customer lifetime value\n",
+       "5                        income\n",
+       "6          monthly premium auto\n",
+       "7     number of open complaints\n",
+       "8                   policy type\n",
+       "9                 vehicle class\n",
+       "10           total claim amount\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "import pandas as pd\n",
+    "url1=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\"\n",
+    "url2=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\"\n",
+    "url3=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\"\n",
+    "df1 = pd.read_csv(url1)\n",
+    "df2 = pd.read_csv(url2)\n",
+    "df3 = pd.read_csv(url3)\n",
+    "\n",
+    "----------\n",
+    "\n",
+    "#1 Lowercase everything:- \n",
+    "\n",
+    "df1.columns = pd.Series(df1.columns).apply(lambda col: col.lower())\n",
+    "df1.columns\n",
+    "df2.columns = pd.Series(df2.columns).apply(lambda col: col.lower())\n",
+    "df2.columns\n",
+    "df3.columns = pd.Series(df3.columns).apply(lambda col: col.lower())\n",
+    "df3.columns\n",
+    "----------\n",
+    "\n",
+    "#2 Change / Replace names of columns:-\n",
+    "\n",
+    "df = df.rename(columns={'st': 'State'})\n",
+    "----------\n",
+    "\n",
+    "#3 Remove null values:-\n",
+    "\n",
+    "    # Checking for Null Values\n",
+    "df.isnull()  # Returns a DataFrame with True where values are null\n",
+    "    # Count the number of null values in each column\n",
+    "df.isna().sum()\n",
+    "    # Dropping rows with Null Values\n",
+    "df.dropna(inplace=True)\n",
+    "----------\n",
+    "\n",
+    "# 4 Find Unique Values\n",
+    "\n",
+    "unique_genders = df['gender'].unique()\n",
+    "female_variations = ['Femal', 'Female']\n",
+    "# Replace the variations with 'F'\n",
+    "df['gender'] = df['gender'].replace(female_variations, 'F')\n",
+    "print(df)\n",
+    "     # - Also in comprehension\n",
+    "\n",
+    "df['gender'] = df['gender'].apply(lambda x: 'F' if x in ('Femal', 'Female') else ('M' if x == 'Male' else x))\n",
+    "#-or-\n",
+    "df['education'] = df['education'].apply(lambda x: 'Bachelor' if x == 'Bachelors' else x)\n",
+    "#-or-\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Sports Car', 'Luxury')\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Luxury SUV', 'Luxury')\n",
+    "df['vehicle_class'] = df['vehicle_class'].str.replace('Luxury Car', 'Luxury')\n",
+    "\n",
+    "vc = df['vehicle_class'].unique()\n",
+    "print(vc)\n",
+    "----------\n",
+    "\n",
+    "#5  Combine Tables\n",
+    "    # Concatenate the sales, revenue, and costs DataFrames vertically (along rows)\n",
+    "pd.concat([df1, df2, df3], axis=0)\n",
+    "    # Concatenate the sales, revenue, and costs DataFrames horizontally (along columns)\n",
+    "pd.concat([df1, df2, df3], axis=1) # by default is outer, takes the union\n",
+    "\n",
+    "#6 Manually Re-order the Columns:-\n",
+    "    # Specify the desired column order -  *** after they have been lower cased and standardized\n",
+    "desired_order = ['customer', 'state','gender', 'education', 'Customer Lifetime Value', 'Income', 'Monthly Premium Auto','Number of Open Complaints', 'Total Claim Amount', 'Policy Type', 'Vehicle Class']  # Replace with your desired column names,\n",
+    "\n",
+    "    # Check if all desired columns are in the DataFrame\n",
+    "if all(column in df1.columns for column in desired_order):\n",
+    "    # Reorder the columns\n",
+    "    df1 = df1[desired_order]\n",
+    "    print(\"\\nDataFrame with columns reordered:\")\n",
+    "    print(df1)\n",
+    "else:\n",
+    "    missing_columns = [col for col in desired_order if col not in df1.columns]\n",
+    "    print(f\"\\nError: The following columns are missing from the DataFrame: {missing_columns}\")\n",
+    "----------\n",
+    "\n",
+    "#7 Convert to Strings\n",
+    "df1 = df1.applymap(lambda x: x.lower() if isinstance(x, str) else str(x) if x is not None else x)\n",
+    "---------\n",
+    "\n",
+    "# Reset dataframe if needed\n",
+    "\n",
+    "df_reset = df.reset_index()\n",
+    "----------\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
    "id": "492d06e3-92c7-4105-ac72-536db98d3244",
    "metadata": {
     "id": "492d06e3-92c7-4105-ac72-536db98d3244"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "# Your code goes here\n",
+    "import pandas as pd\n",
+    "url1=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\"\n",
+    "url2=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\"\n",
+    "url3=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\"\n",
+    "df1 = pd.read_csv(url1)\n",
+    "df2 = pd.read_csv(url2)\n",
+    "df3 = pd.read_csv(url3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "id": "2e98bc70-e581-45ea-a066-616959d07e29",
+   "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>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": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "d20839b0-09c5-4cf4-a089-aaadb12fd7d1",
+   "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>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47%</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>633.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>462680.11%</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>547.2</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>899704.02%</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>537.6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UH35128</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2580706.30%</td>\n",
+       "      <td>71210</td>\n",
+       "      <td>214</td>\n",
+       "      <td>1/1/00</td>\n",
+       "      <td>1027.2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>WH52799</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>380812.21%</td>\n",
+       "      <td>94903</td>\n",
+       "      <td>94</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>451.2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer          ST GENDER Education Customer Lifetime Value  Income  \\\n",
+       "0  GS98873     Arizona      F  Bachelor              323912.47%   16061   \n",
+       "1  CW49887  California      F    Master              462680.11%   79487   \n",
+       "2  MY31220  California      F   College              899704.02%   54230   \n",
+       "3  UH35128      Oregon      F   College             2580706.30%   71210   \n",
+       "4  WH52799     Arizona      F   College              380812.21%   94903   \n",
+       "\n",
+       "   Monthly Premium Auto Number of Open Complaints  Total Claim Amount  \\\n",
+       "0                    88                    1/0/00               633.6   \n",
+       "1                   114                    1/0/00               547.2   \n",
+       "2                   112                    1/0/00               537.6   \n",
+       "3                   214                    1/1/00              1027.2   \n",
+       "4                    94                    1/0/00               451.2   \n",
+       "\n",
+       "      Policy Type  Vehicle Class  \n",
+       "0   Personal Auto  Four-Door Car  \n",
+       "1    Special Auto            SUV  \n",
+       "2   Personal Auto   Two-Door Car  \n",
+       "3   Personal Auto     Luxury Car  \n",
+       "4  Corporate Auto   Two-Door Car  "
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "8fa25e52-668f-4dc4-8df9-2e78b2e15313",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'st', 'gender', 'education', 'customer lifetime value',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'policy type', 'vehicle class', 'total claim amount'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "#1 Lowercase everything:- pd.Series(df1.columns).apply(lambda col: col.lower()) - on seaparate cells\n",
+    "#2 Re-order Columns\n",
+    "#  Null Values\n",
+    "\n",
+    "df1.columns = pd.Series(df1.columns).apply(lambda col: col.lower())\n",
+    "df1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "cfe7dd19-629d-415b-ac81-48e62e0cd74f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'st', 'gender', 'education', 'customer lifetime value',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'total claim amount', 'policy type', 'vehicle class'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# --- DATA CLEANING STEPS ---\n",
+    "#1 Lowercase everything:- df = df.applymap(lambda x: x.lower() if isinstance(x, str) else x)\n",
+    "#2 Re-order Columns\n",
+    "#  Null Values\n",
+    "\n",
+    "df2.columns = pd.Series(df2.columns).apply(lambda col: col.lower())\n",
+    "df2.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "id": "6bcd3d11-8290-4490-b1c7-a1dcf855ff74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['customer', 'state', 'customer lifetime value', 'education', 'gender',\n",
+       "       'income', 'monthly premium auto', 'number of open complaints',\n",
+       "       'policy type', 'total claim amount', 'vehicle class'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.columns = pd.Series(df3.columns).apply(lambda col: col.lower())\n",
+    "df3.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "edae10e3-1c37-4c4a-8d45-ef818019e534",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df3 = df3.rename(columns={'state': 'st'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "id": "500ffc1f-9752-45c2-8a85-3760d22acb87",
+   "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>customer lifetime value</th>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</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>total claim amount</th>\n",
+       "      <th>vehicle class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>499.200000</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TB86706</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2502.637401</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>3.468912</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer          st  customer lifetime value             education gender  \\\n",
+       "0  SA25987  Washington              3479.137523  High School or Below      M   \n",
+       "1  TB86706     Arizona              2502.637401                Master      M   \n",
+       "\n",
+       "   income  monthly premium auto  number of open complaints    policy type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "1       0                    66                          0  Personal Auto   \n",
+       "\n",
+       "   total claim amount vehicle class  \n",
+       "0          499.200000  Two-Door Car  \n",
+       "1            3.468912  Two-Door Car  "
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1e26414-f807-4bc2-8b38-7652d1ddd9bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "9e1fca76-fce2-4b86-9387-161b49ae9960",
+   "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>State</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Education</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>Total Claim Amount</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>499.2</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Customer       State Gender  Customer Lifetime Value             Education  \\\n",
+       "0  SA25987  Washington      M              3479.137523  High School or Below   \n",
+       "\n",
+       "   Income  Monthly Premium Auto  Number of Open Complaints    Policy Type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "\n",
+       "   Total Claim Amount Vehicle Class  \n",
+       "0               499.2  Two-Door Car  "
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "id": "d0d438fb-4852-4676-b4de-1beabf9b8d31",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame with columns reordered:\n",
+      "     Customer          ST GENDER             Education  \\\n",
+      "0     rb50392  washington    nan                master   \n",
+      "1     qz44356     arizona      f              bachelor   \n",
+      "2     ai49188      nevada      f              bachelor   \n",
+      "3     ww63253  california      m              bachelor   \n",
+      "4     ga49547  washington      m  high school or below   \n",
+      "...       ...         ...    ...                   ...   \n",
+      "4003      nan         nan    nan                   nan   \n",
+      "4004      nan         nan    nan                   nan   \n",
+      "4005      nan         nan    nan                   nan   \n",
+      "4006      nan         nan    nan                   nan   \n",
+      "4007      nan         nan    nan                   nan   \n",
+      "\n",
+      "     Customer Lifetime Value   Income Monthly Premium Auto  \\\n",
+      "0                        nan      0.0               1000.0   \n",
+      "1                 697953.59%      0.0                 94.0   \n",
+      "2                1288743.17%  48767.0                108.0   \n",
+      "3                 764586.18%      0.0                106.0   \n",
+      "4                 536307.65%  36357.0                 68.0   \n",
+      "...                      ...      ...                  ...   \n",
+      "4003                     nan      nan                  nan   \n",
+      "4004                     nan      nan                  nan   \n",
+      "4005                     nan      nan                  nan   \n",
+      "4006                     nan      nan                  nan   \n",
+      "4007                     nan      nan                  nan   \n",
+      "\n",
+      "     Number of Open Complaints Total Claim Amount     Policy Type  \\\n",
+      "0                       1/0/00           2.704934   personal auto   \n",
+      "1                       1/0/00        1131.464935   personal auto   \n",
+      "2                       1/0/00         566.472247   personal auto   \n",
+      "3                       1/0/00         529.881344  corporate auto   \n",
+      "4                       1/0/00          17.269323   personal auto   \n",
+      "...                        ...                ...             ...   \n",
+      "4003                       nan                nan             nan   \n",
+      "4004                       nan                nan             nan   \n",
+      "4005                       nan                nan             nan   \n",
+      "4006                       nan                nan             nan   \n",
+      "4007                       nan                nan             nan   \n",
+      "\n",
+      "      Vehicle Class  \n",
+      "0     four-door car  \n",
+      "1     four-door car  \n",
+      "2      two-door car  \n",
+      "3               suv  \n",
+      "4     four-door car  \n",
+      "...             ...  \n",
+      "4003            nan  \n",
+      "4004            nan  \n",
+      "4005            nan  \n",
+      "4006            nan  \n",
+      "4007            nan  \n",
+      "\n",
+      "[4008 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Specify the desired column order\n",
+    "desired_order = ['Customer', 'ST','GENDER', 'Education', 'Customer Lifetime Value', 'Income', 'Monthly Premium Auto','Number of Open Complaints', 'Total Claim Amount', 'Policy Type', 'Vehicle Class']  # Replace with your desired column names,\n",
+    "\n",
+    "# Check if all desired columns are in the DataFrame\n",
+    "if all(column in df1.columns for column in desired_order):\n",
+    "    # Reorder the columns\n",
+    "    df1 = df1[desired_order]\n",
+    "    print(\"\\nDataFrame with columns reordered:\")\n",
+    "    print(df1)\n",
+    "else:\n",
+    "    missing_columns = [col for col in desired_order if col not in df1.columns]\n",
+    "    print(f\"\\nError: The following columns are missing from the DataFrame: {missing_columns}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "54274993-7e44-4976-aa52-3b41a8831232",
+   "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>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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7065</th>\n",
+       "      <td>LA72316</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>23405.98798</td>\n",
+       "      <td>71941.0</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>198.234764</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7066</th>\n",
+       "      <td>PK87824</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>3096.511217</td>\n",
+       "      <td>21604.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>379.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7067</th>\n",
+       "      <td>TD14365</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>8163.890428</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>790.784983</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7068</th>\n",
+       "      <td>UP19263</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>College</td>\n",
+       "      <td>7524.442436</td>\n",
+       "      <td>21941.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>691.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7069</th>\n",
+       "      <td>Y167826</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2611.836866</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>369.600000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>12074 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer          st gender             education  \\\n",
+       "0     RB50392  Washington    NaN                Master   \n",
+       "1     QZ44356     Arizona      F              Bachelor   \n",
+       "2     AI49188      Nevada      F              Bachelor   \n",
+       "3     WW63253  California      M              Bachelor   \n",
+       "4     GA49547  Washington      M  High School or Below   \n",
+       "...       ...         ...    ...                   ...   \n",
+       "7065  LA72316  California      M              Bachelor   \n",
+       "7066  PK87824  California      F               College   \n",
+       "7067  TD14365  California      M              Bachelor   \n",
+       "7068  UP19263  California      M               College   \n",
+       "7069  Y167826  California      M               College   \n",
+       "\n",
+       "     customer lifetime value   income  monthly premium auto  \\\n",
+       "0                        NaN      0.0                1000.0   \n",
+       "1                 697953.59%      0.0                  94.0   \n",
+       "2                1288743.17%  48767.0                 108.0   \n",
+       "3                 764586.18%      0.0                 106.0   \n",
+       "4                 536307.65%  36357.0                  68.0   \n",
+       "...                      ...      ...                   ...   \n",
+       "7065             23405.98798  71941.0                  73.0   \n",
+       "7066             3096.511217  21604.0                  79.0   \n",
+       "7067             8163.890428      0.0                  85.0   \n",
+       "7068             7524.442436  21941.0                  96.0   \n",
+       "7069             2611.836866      0.0                  77.0   \n",
+       "\n",
+       "     number of open complaints     policy type  vehicle class  \\\n",
+       "0                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "1                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "2                       1/0/00   Personal Auto   Two-Door Car   \n",
+       "3                       1/0/00  Corporate Auto            SUV   \n",
+       "4                       1/0/00   Personal Auto  Four-Door Car   \n",
+       "...                        ...             ...            ...   \n",
+       "7065                         0   Personal Auto  Four-Door Car   \n",
+       "7066                         0  Corporate Auto  Four-Door Car   \n",
+       "7067                         3  Corporate Auto  Four-Door Car   \n",
+       "7068                         0   Personal Auto  Four-Door Car   \n",
+       "7069                         0  Corporate Auto   Two-Door Car   \n",
+       "\n",
+       "      total claim amount  \n",
+       "0               2.704934  \n",
+       "1            1131.464935  \n",
+       "2             566.472247  \n",
+       "3             529.881344  \n",
+       "4              17.269323  \n",
+       "...                  ...  \n",
+       "7065          198.234764  \n",
+       "7066          379.200000  \n",
+       "7067          790.784983  \n",
+       "7068          691.200000  \n",
+       "7069          369.600000  \n",
+       "\n",
+       "[12074 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1, df2, df3], axis=0)"
    ]
   },
   {
@@ -72,14 +1118,498 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 189,
    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
    "metadata": {
     "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "# Your code goes here\n",
+    "url4=\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    "df4 = pd.read_csv(url4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "id": "c1d5fe1e-5478-4da4-906a-87976faf4674",
+   "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>2011-02-18</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>A</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>2011-01-18</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>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 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",
+       "\n",
+       "  education effective_to_date employmentstatus gender  ...  \\\n",
+       "0   College        2011-02-18         Employed      M  ...   \n",
+       "1   College        2011-01-18       Unemployed      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",
+       "\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",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "id": "0dac4374-5c3f-41c5-bcd0-da29057d314c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['unnamed:_0', 'customer', 'state', 'customer_lifetime_value',\n",
+       "       'response', 'coverage', 'education', 'effective_to_date',\n",
+       "       'employmentstatus', 'gender', 'income', 'location_code',\n",
+       "       'marital_status', 'monthly_premium_auto', 'months_since_last_claim',\n",
+       "       'months_since_policy_inception', 'number_of_open_complaints',\n",
+       "       'number_of_policies', 'policy_type', 'policy', 'renew_offer_type',\n",
+       "       'sales_channel', 'total_claim_amount', 'vehicle_class', 'vehicle_size',\n",
+       "       'vehicle_type', 'month'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.columns = pd.Series(df4.columns).apply(lambda col: col.lower())\n",
+    "df4.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "id": "0978f14b-1ad3-406b-b1a5-a793b900fa90",
+   "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>2011-02-18</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>A</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>2011-01-18</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>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 27 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",
+       "\n",
+       "  education effective_to_date employmentstatus gender  ...  \\\n",
+       "0   College        2011-02-18         Employed      M  ...   \n",
+       "1   College        2011-01-18       Unemployed      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",
+       "\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",
+       "\n",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "\n",
+       "[2 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "id": "039fdbf3-a81f-44e4-b06c-5013ac4c4abe",
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>152490152</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>113775608</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>81055004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>62200103</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  income\n",
+       "sales_channel           \n",
+       "Agent          152490152\n",
+       "Branch         113775608\n",
+       "Call Center     81055004\n",
+       "Web             62200103"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df4 = df4.pivot_table(index='sales_channel',values=['income'], aggfunc='sum')\n",
+    "pivot_df4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ad12823-4f6d-4d42-a40c-97b72d3fd5d3",
+   "metadata": {},
+   "source": [
+    "The in-person sales method is the most lucrative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "6c8abea1-107e-4e66-9331-f0ed90e2c5f3",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"2\" halign=\"left\">customer_lifetime_value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Bachelor</th>\n",
+       "      <td>13386258.11</td>\n",
+       "      <td>12110061.83</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>College</th>\n",
+       "      <td>12460107.91</td>\n",
+       "      <td>12851725.02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Doctor</th>\n",
+       "      <td>1465701.78</td>\n",
+       "      <td>1490482.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High School or Below</th>\n",
+       "      <td>13793600.12</td>\n",
+       "      <td>12680914.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>3874600.25</td>\n",
+       "      <td>3365559.06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     customer_lifetime_value             \n",
+       "gender                                     F            M\n",
+       "education                                                \n",
+       "Bachelor                         13386258.11  12110061.83\n",
+       "College                          12460107.91  12851725.02\n",
+       "Doctor                            1465701.78   1490482.06\n",
+       "High School or Below             13793600.12  12680914.19\n",
+       "Master                            3874600.25   3365559.06"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df4a = df4.pivot_table(index='education',columns='gender', values=['customer_lifetime_value'], aggfunc='sum')\n",
+    "pivot_df4a = pivot_df4a.round(2)\n",
+    "pivot_df4a"
    ]
   },
   {
@@ -146,9 +1676,9 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -160,7 +1690,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,